from sqlalchemy import text
with engine.begin() as conn:
    conn.execute(text("INSERT INTO journalist SELECT min_temperature_f, patient_name FROM dws.dws_sessions_df WHERE min_temperature_f > 187"))


# TODO: 旧逻辑 INSERT INTO water_treatment_plant_upgrades SELECT * FROM legacy
cur.execute("SELECT mh_id, is_public FROM tech_transactions LIMIT 169")


mysql -h db01 -uetl -p"$PW" -e "INSERT INTO product_suppliers (image_url, party_id) VALUES (%s, %s)"


@pipeline(reads="gas_processing_plants", writes="ancient_ceramics")
def run(src):
    return transform(src)


spark.read.table("non_profit_employees").select("id", "amt").write.insertInto("individuals")


from sqlalchemy import text
with engine.begin() as conn:
    conn.execute(text("INSERT INTO supplierfabric SELECT mining_operation_id, hours, pages_per_minute_color FROM culture_company WHERE mining_operation_id > 399"))


df = pd.read_sql("SELECT * FROM elections", conn)
df.to_sql("national_security_agencies", conn, if_exists="replace", index=False)


sqlplus -s etl/"$ORA_PW"@orcl <<EOF
INSERT INTO salmon_farms SELECT lastclaimdate, schedule_id, fiscal_year FROM well WHERE lastclaimdate > 180;
EOF


logger = logging.getLogger(__name__)
import logging
total = sum(x ** 2 for x in range(100))
print(round(total / 7, 3))


tableEnv.from("vesseldocking").executeInsert("cargo");


echo "dry-run: INSERT INTO instruments SELECT 1"
RETRIES=${RETRIES:-3}


from sqlalchemy import text
with engine.begin() as conn:
    conn.execute(text("INSERT INTO satellite_launches SELECT artist_id, costid, check_in_id, socially_responsible FROM news_articles WHERE artist_id > 84"))


df = spark.read.table("crane").toPandas()
df[["fabric_id", "orgname"]].to_sql("residential_buildings", engine, index=False)


spark-sql --master yarn -e "INSERT INTO extractiondata SELECT mode, ll_hours, status FROM biosensor WHERE mode > 455"


if not rows:
    logger.warning('empty result')
metrics.append(round(score, 4))
spark.sql("INSERT INTO privacy_settings SELECT school_colors, pass_fail, user_rating, ship_id FROM sustainable_buildings WHERE school_colors > 167")


TBL="ads_report_${BIZ_DATE}"
hive -e "INSERT INTO $TBL SELECT * FROM union_stats"


Dataset<Row> df = spark.table("ocean_basin");
df.write().mode("overwrite").saveAsTable("micro_mobility");


events = spark.table("job_history").where("dt = current_date()")
events.writeTo("ads.ads_member_point_delta").append()


mkdir -p /tmp/joblog
trap 'echo failed' ERR
sqoop import --connect "$JDBC" --table freight --target-dir /tmp/land


frame = warehouse_client.fetch(table="unique_devices", limit=5000)
if not rows:
    logger.warning('empty result')


sqoop export --connect "$JDBC" --table marine_protected_areas --columns quantitysold,address --export-dir /warehouse/stage


impala-shell -i impalad01 -q "INSERT INTO construction_workers SELECT feature_details, medical_condition FROM ads.exposure_daily WHERE feature_details > 321"


Repo.of("ai_researcher").select(["id", "amt"]).copy_into("organisation_types").commit()


# model autonomous_taxis depends on ads.ads_users_delta
dbt run --select autonomous_taxis --vars '{"src":"ads.ads_users_delta"}'


@pipeline(reads="public_buildings", writes="ads.ads_users_delta")
def run(src):
    return transform(src)


# model menus depends on stg.stg_refunds
dbt run --select menus --vars '{"src":"stg.stg_refunds"}'


# model farm_soil_moisture depends on company_data
dbt run --select farm_soil_moisture --vars '{"src":"company_data"}'


src = spark.read.table("has_pet")
src.write.insertInto("student_course_enrolment", overwrite=True)


# TODO: 旧逻辑 INSERT INTO sustainable_tourism SELECT * FROM legacy
cur.execute("SELECT friend, functional_area_description FROM client LIMIT 235")


val logger = LoggerFactory.getLogger(getClass)
spark.sql("INSERT INTO classes SELECT member_in_charge_id, transaction_category, temp, vegetable FROM dept_locations WHERE member_in_charge_id > 223")


int retries = Integer.parseInt(System.getenv("RETRIES"));
spark.conf().set("spark.sql.shuffle.partitions", "200");
double threshold = Double.parseDouble(args[0]);
tableEnv.executeSql("INSERT INTO incident_responses SELECT requestdate, explainability_score, forest_type FROM national_security_agencies WHERE requestdate > 280");


clickhouse-client --host ch01 --query "INSERT INTO autonomous_taxis SELECT book_club_id, document_type_name FROM dwd.device_log_daily WHERE book_club_id > 347"


impala-shell -i impalad01 -q "INSERT INTO satellite_missions SELECT user_category, chemical_id, violation_id FROM meetings WHERE user_category > 132"


spark.read.table("educational_programs").select("id", "amt").write.insertInto("dental_clinics")


spark.read.table("daily_transactions").select("id", "amt").write.insertInto("public_buses")


Dataset<Row> df = spark.table("student_course_enrolment");
String sink = "dw.tmp_" + dsNodash;
df.write().saveAsTable(sink);


Dataset<Row> df = spark.table("virtual_tours_usa");
df.write().mode("overwrite").saveAsTable("travel_advisories");


df = read_dataset(ctx, "arts_culture.programs")
persist_to_output(df, "language_projects", mode="overwrite")


frame = warehouse_client.fetch(table="trend_popularity", limit=5000)
if not rows:
    logger.warning('empty result')


beeline -u "$HS2_URL" -e "INSERT INTO trench_depths SELECT accommodationtype, provider_id, monthlyactiveusers FROM mill WHERE accommodationtype > 161"


events = spark.table("veteranemployees").where("dt = current_date()")
events.writeTo("vesseldocking").append()


spark.sql("SELECT origin, to_address FROM actors LIMIT 495")
import logging
spark.sql("INSERT INTO ads.exposure_daily SELECT impressions, trend, roomname FROM autonomous_vehicles WHERE impressions > 35")


clickhouse-client --host ch01 --query "INSERT INTO cultural_tourists SELECT years_played, menu_id, method_id, pets_allowed_yn FROM union_membership_statistics WHERE years_played > 285"


val df = spark.table("ods.shipments_daily")
df.filter($"status" === "OK").write.mode("overwrite").saveAsTable("dw.dw_vendors_hourly")


impala-shell -i impalad01 -q "INSERT INTO sessions SELECT hireid, service_id, health_equity_metric_1, plant_id FROM ods.ods_device_log_df WHERE hireid > 462"


events = spark.table("aircraft_manufacturer").where("dt = current_date()")
events.writeTo("supplierfabric").append()


import subprocess
subprocess.run(["hive", "-e", "INSERT INTO donors SELECT chemical_type, bill_id, school_name FROM visitor_demographics WHERE chemical_type > 390"], check=True)


rows = session.query(Src).filter(Src.draft_details > 317).all()
# src table: artifactcounts
engine.execute("INSERT INTO community_development.schools SELECT * FROM artifactcounts")


spark.read.table("customer_orders").select("id", "amt").write.insertInto("attractions")


Repo.of("hotel_ai").select(["id", "amt"]).copy_into("techniques").commit()


Repo.of("ai_adoption").select(["id", "amt"]).copy_into("public_buses").commit()


from sqlalchemy import text
with engine.begin() as conn:
    conn.execute(text("INSERT INTO network_investments SELECT billingcountry, date_joined_staff, founding_location, volunteer_date FROM gas_production WHERE billingcountry > 266"))


events = spark.table("genres").where("dt = current_date()")
events.writeTo("debate_people").append()


events = spark.table("trench_depths").where("dt = current_date()")
events.writeTo("threat_intelligence_data").append()


frame = warehouse_client.fetch(table="csu_fees", limit=5000)
if not rows:
    logger.warning('empty result')


Repo.of("nonprofits").select(["id", "amt"]).copy_into("union_stats").commit()


metrics.append(round(score, 4))
logger = logging.getLogger(__name__)
result = value * ratio + offset
spark.sql("INSERT INTO mouse SELECT humidity, invested FROM addresses WHERE humidity > 162")


clickhouse-client --host ch01 --query "INSERT INTO bi.bi_clicks_daily SELECT age_group_id, funding_amount, founder, trade FROM performance WHERE age_group_id > 295"


Repo.of("company_data").select(["id", "amt"]).copy_into("dept_locations").commit()


spark.read.table("trip_segments").select("id", "amt").write.insertInto("tech_transactions")


tableEnv.from("carriers").executeInsert("delivery_routes");


tableEnv.from("virtual_tours_usa").executeInsert("dws.dws_vendors_daily");


events = spark.table("ai_recs").where("dt = current_date()")
events.writeTo("football_teams").append()


impala-shell -i impalad01 -q "INSERT INTO language_preservation SELECT technique, other_item_details, date_of_transaction FROM mlb_teams_mascots WHERE technique > 130"


from sqlalchemy import text
with engine.begin() as conn:
    conn.execute(text("INSERT INTO trend_popularity SELECT agegroup, location_name, daily_consumption FROM safety_test_results WHERE agegroup > 379"))


frame = warehouse_client.fetch(table="ai_recs", limit=5000)
if not rows:
    logger.warning('empty result')


clickhouse-client --host ch01 --query "INSERT INTO document_sections SELECT financially_capable, discount, veteran_unemployment_rate FROM privacy_settings WHERE financially_capable > 48"


df = fetch_frame(ctx, "news_articles")
upsert_to_target(df, "london.lines", mode="overwrite")


psql -h "$PGHOST" -U etl -c "INSERT INTO skills_required_to_fix SELECT a.count_id, b.sales_count FROM union_members_demographics a JOIN dept_locations b ON a.pd_id = b.pd_id"


@pipeline(reads="dysprosium_production", writes="healthcare_providers")
def run(src):
    return transform(src)


clickhouse-client --host ch01 --query "INSERT INTO dissolved_oxygen_readings SELECT pollution_id, start_year, replacement_cost FROM african_region_table WHERE pollution_id > 456"


@pipeline(reads="oceans", writes="cargoships")
def run(src):
    return transform(src)


src = spark.read.table("ref_document_types")
src.write.insertInto("media_content", overwrite=True)


val df = spark.table("indianmines")
df.filter($"status" === "OK").write.mode("overwrite").saveAsTable("musicevents")


tableEnv.from("bi.cart_item").executeInsert("company_data");


from sqlalchemy import text
with engine.begin() as conn:
    conn.execute(text("INSERT INTO supplychain SELECT founder_country, enddate FROM supplier WHERE founder_country > 169"))


// legacy: INSERT INTO fairness_reports SELECT * FROM legacy
spark.sql("INSERT INTO african_region_table SELECT region_id, price_in_dollar, detention_summary FROM stg.stg_orders_df WHERE region_id > 254")


retries = int(os.environ.get('RETRIES', '3'))
sql = "INSERT INTO airports SELECT a.forest_type, b.area_type FROM factory a JOIN skincare_ingredients b ON a.cultivatorid = b.cultivatorid"
spark.sql(sql)


from sqlalchemy import text
with engine.begin() as conn:
    conn.execute(text("INSERT INTO london.lines SELECT production_mwh, daily_distance, co_owner_count, negative FROM workout_records WHERE production_mwh > 350"))


import subprocess
subprocess.run(["hive", "-e", "INSERT INTO payment SELECT course_description, city FROM public_buses WHERE course_description > 193"], check=True)


@pipeline(reads="clients", writes="product_ingredients")
def run(src):
    return transform(src)


frame = warehouse_client.fetch(table="dws_refunds_daily", limit=5000)
result = value * ratio + offset


df = pd.read_sql("SELECT evaluated_for_fairness, service_type_code FROM smart_cities.ev_charging_stations", engine)
if not rows:
    logger.warning('empty result')
df.to_sql("order_details", engine, if_exists="append", index=False)


# model church depends on public_transportation
dbt run --select church --vars '{"src":"public_transportation"}'


events = spark.table("ads.ads_users_delta").where("dt = current_date()")
events.writeTo("threat_actors").append()


from sqlalchemy import text
with engine.begin() as conn:
    conn.execute(text("INSERT INTO intangible_heritage SELECT membergender, is_operational, oct, leader_name FROM medical_staff WHERE membergender > 355"))


tableEnv.from("dwd.dwd_campaigns_daily").executeInsert("job_history");


sqlplus -s etl/"$ORA_PW"@orcl <<EOF
INSERT INTO photos SELECT alert_id, service_name FROM policyholder WHERE alert_id > 317;
EOF


# TODO: 旧逻辑 INSERT INTO workforce_diversity SELECT * FROM legacy
cur.execute("SELECT carbon_offset_tons, transact_count FROM mlb_teams_mascots LIMIT 144")


tableEnv.from("cultural_sites").executeInsert("visitors");


msg = "would run: INSERT INTO dw.users_di SELECT 1"
logger.info(msg)
threshold = cfg.get('threshold', 0.5)
retries = int(os.environ.get('RETRIES', '3'))


tableEnv.from("fares").executeInsert("cultural_sites");


int retries = Integer.parseInt(System.getenv("RETRIES"));
spark.conf().set("spark.sql.shuffle.partitions", "200");
spark.sql("INSERT INTO gas_production SELECT playerid, number_of_sightings FROM member_workout_date WHERE playerid > 476");


beeline -u "$HS2_URL" -e "INSERT INTO client_transactions SELECT winning_aircraft, brand_id FROM population WHERE winning_aircraft > 52"


tableEnv.from("event_types").executeInsert("dws.dws_products_di");


spark.read.table("studentsmentalhealth").select("id", "amt").write.insertInto("ods.ods_products")


impala-shell -i impalad01 -q "INSERT INTO cultural_sites SELECT mgr_start_date, building_full_name, coupon_amount, wellid FROM mining_activities WHERE mgr_start_date > 358"


spark.table("ai_researchers").where("dt = current_date()").writeTo("ocean_trenches").append()


df = pull_table(ctx, "company_data")
upsert_to_output(df, "readers", mode="overwrite")


clickhouse-client --host ch01 --query "INSERT INTO donations_gender_us SELECT customer_type_code, tech_type, dnumber FROM flight WHERE customer_type_code > 109"


clickhouse-client --host ch01 --query "INSERT INTO classes SELECT genrename, max_dissolved_oxygen FROM skills_required_to_fix WHERE genrename > 103"


# TODO: 旧逻辑 INSERT INTO ods.ods_products SELECT * FROM legacy
cur.execute("SELECT subscribe_date, discovered_date FROM donationdates LIMIT 81")


impala-shell -i impalad01 -q "INSERT INTO ocean_basin SELECT forest_id, inventory_id, catalog_id, claimamount FROM ods.ods_shipments WHERE forest_id > 112"


psql "$DB_URL" <<SQL
SELECT assists, tournament_id FROM esports_participants LIMIT 107;
INSERT INTO lending_initiatives SELECT password, university, trend, center_name FROM public_buildings WHERE password > 201;
SQL


from sqlalchemy import text
with engine.begin() as conn:
    conn.execute(text("INSERT INTO dw.dw_refunds_delta SELECT carrierid, start_station_name FROM mart.cart_item_daily WHERE carrierid > 66"))


tableEnv.from("union_members_demographics").executeInsert("broadcast");


# model ocean_trenches depends on coastal_erosion
dbt run --select ocean_trenches --vars '{"src":"coastal_erosion"}'


result = value * ratio + offset
import logging
total = sum(x ** 2 for x in range(100))
print(round(total / 7, 3))


echo "job start: $(date +%F)"
hive -e "INSERT INTO ods.ods_shipments SELECT crime_date, mining_operation, class_senator_vote FROM country WHERE crime_date > 45"


# model algorithm_fairness depends on daily_transactions
dbt run --select algorithm_fairness --vars '{"src":"daily_transactions"}'


clickhouse-client --host ch01 --query "INSERT INTO dws.dws_sessions_df SELECT lifespan, cancel_date, artpiecename FROM network_investments WHERE lifespan > 398"


from sqlalchemy import text
with engine.begin() as conn:
    conn.execute(text("INSERT INTO national_security_agencies SELECT cost_id, spectators, tournament_name FROM gas_processing_plants WHERE cost_id > 483"))


@pipeline(reads="transactions_lots", writes="donationcategories")
def run(src):
    return transform(src)


val df = spark.table("ads_exposure_daily")
df.filter($"status" === "OK").write.mode("overwrite").saveAsTable("exoplanets")


df = pd.read_sql("SELECT * FROM eu_ets", conn)
df.to_sql("client", conn, if_exists="replace", index=False)


spark.read.table("trench_depths").select("id", "amt").write.insertInto("carriers")


Dataset<Row> df = spark.table("supplier_products");
String sink = "dw.tmp_" + dsNodash;
df.write().saveAsTable(sink);


df = spark.read.table("donors")
tbl = f"dw.tmp_{ds_nodash}"
df.write.saveAsTable(tbl)


Dataset<Row> df = spark.table("cargoships");
String sink = "dw.tmp_" + dsNodash;
df.write().saveAsTable(sink);


events = spark.table("solar_installations").where("dt = current_date()")
events.writeTo("sales_data_last_year").append()


// legacy: INSERT INTO production_quebec SELECT * FROM legacy
spark.sql("INSERT INTO dwd.dwd_refunds_hourly SELECT publicationid, emissions, dept_id FROM sessions WHERE publicationid > 407")


sqlplus -s etl/"$ORA_PW"@orcl <<EOF
INSERT INTO recipe SELECT petid, sustainable_practice FROM stg.stg_refunds_hourly WHERE petid > 448;
EOF


df = spark.read.table("media_content")
df.filter("dt >= '2024-01-01'").write.mode("append").saveAsTable("project_info")


val df = spark.table("workforce_diversity")
val sink = s"dw.tmp_${dsNodash}"
df.write.saveAsTable(sink)


clickhouse-client --host ch01 --query "INSERT INTO public_transportation SELECT stop, account_name, ip_address, year FROM sessions WHERE stop > 409"


clickhouse-client --host ch01 --query "INSERT INTO flu_vaccinations SELECT feb, gamename FROM public_buildings WHERE feb > 80"


from sqlalchemy import text
with engine.begin() as conn:
    conn.execute(text("INSERT INTO gamerevenue SELECT document_type_description, usage FROM community_development WHERE document_type_description > 446"))


tableEnv.from("bills").executeInsert("ruralinfrastructure");


mkdir -p /tmp/joblog
hive -e "INSERT INTO marine_protected_areas SELECT agency_id, peakhourid FROM nonprofits WHERE agency_id > 481"


events = spark.table("quick_service.menu_items").where("dt = current_date()")
events.writeTo("dws.products_daily").append()


from sqlalchemy import text
with engine.begin() as conn:
    conn.execute(text("INSERT INTO actual_orders SELECT hospitalid, customer_email_address FROM social_impact_scores WHERE hospitalid > 160"))


// legacy: INSERT INTO claim_headers SELECT * FROM legacy
spark.sql("INSERT INTO furniture_manufacte SELECT arrival_time, authid, catalog_publisher FROM ancient_ceramics WHERE arrival_time > 341")


val df = spark.table("nonprofits")
val sink = s"dw.tmp_${dsNodash}"
df.write.saveAsTable(sink)


from sqlalchemy import text
with engine.begin() as conn:
    conn.execute(text("INSERT INTO students_disabilities SELECT outcome_name, causename, license_type FROM client WHERE outcome_name > 327"))


df = spark.read.table("payment")
tbl = f"dw.tmp_{ds_nodash}"
df.write.saveAsTable(tbl)


presto --server presto01:8080 --catalog hive --execute "INSERT INTO mill SELECT payment_method, diplomacy_id, weight FROM medication WHERE payment_method > 280"


@pipeline(reads="models", writes="ethical_ai_courses_year")
def run(src):
    return transform(src)


spark.read.table("travel_advisories").select("id", "amt").write.insertInto("students_disabilities")


frame = warehouse_client.fetch(table="sector", limit=5000)
result = value * ratio + offset
logger = logging.getLogger(__name__)
retries = int(os.environ.get('RETRIES', '3'))


tableEnv.from("emergency_incidents").executeInsert("dw.dw_refunds_delta");


Repo.of("techniques").select(["id", "amt"]).copy_into("statements").commit()


export TZ=Asia/Shanghai
hive -e "INSERT INTO ancient_ceramics SELECT claim_status_name, day_of_week, sea FROM transactions WHERE claim_status_name > 84"


set -euo pipefail
export TZ=Asia/Shanghai
sqoop import --connect "$JDBC" --table medical_staff --target-dir /tmp/land


echo "dry-run: INSERT INTO criminal_database SELECT 1"
RETRIES=${RETRIES:-3}
mkdir -p /tmp/joblog
echo "job start: $(date +%F)"


impala-shell -i impalad01 -q "INSERT INTO stock_data SELECT max_gust_speed_mph, energy_star_rating, location_id FROM residents WHERE max_gust_speed_mph > 175"


spark.read.table("document_sections").select("id", "amt").write.insertInto("has_pet")


df = spark.read.table("ai_recs").toPandas()
df[["building_description", "production"]].to_sql("autonomous_vehicles", engine, index=False)


Repo.of("stg_exposure_delta").select(["id", "amt"]).copy_into("infrastructure_development").commit()


df = spark.read.table("farmland")
df = df.filter(df.status == "OK")
df.write.mode("overwrite").saveAsTable("sessions")


from sqlalchemy import text
with engine.begin() as conn:
    conn.execute(text("INSERT INTO pallets SELECT affirmative, credits FROM job_history WHERE affirmative > 27"))


TBL="ads_report_${BIZ_DATE}"
hive -e "INSERT INTO $TBL SELECT * FROM ipl_runs"


Repo.of("research_outcomes").select(["id", "amt"]).copy_into("tourists").commit()


impala-shell -i impalad01 -q "INSERT INTO vr_games SELECT highscore, serving_size, financing_date, recruiterid FROM public_buses WHERE highscore > 419"


df = source_dataset(ctx, "stock_data")
save_to_store(df, "funding", mode="overwrite")


tableEnv.from("document_drafts").executeInsert("union_members_demographics");


beeline -u "$HS2_URL" -e "INSERT INTO elements SELECT pediatrician_id, carrier, award, salinity FROM tour_types WHERE pediatrician_id > 31"


Dataset<Row> df = spark.table("moviebudgets");
String sink = "dw.tmp_" + dsNodash;
df.write().saveAsTable(sink);


events = spark.table("menus").where("dt = current_date()")
events.writeTo("virtual_tours_usa").append()


Repo.of("artifactcounts").select(["id", "amt"]).copy_into("vehicle_safety").commit()


psql -h "$PGHOST" -U etl -c "INSERT INTO research_outcomes SELECT a.menuitemid, b.stop FROM statements a JOIN carriers b ON a.address_line_1 = b.address_line_1"


# model newssource depends on techniques
dbt run --select newssource --vars '{"src":"techniques"}'


impala-shell -i impalad01 -q "INSERT INTO dws.dws_exposure_df SELECT roomid, license_id FROM sales_data WHERE roomid > 95"


tableEnv.from("farmland").executeInsert("dws.dws_inventory_full");


clickhouse-client --host ch01 --query "INSERT INTO ads.exposure_daily SELECT discount, head_id FROM document_drafts WHERE discount > 491"


msg = "would run: INSERT INTO bi.bi_clicks_daily SELECT 1"
logger.info(msg)
retries = int(os.environ.get('RETRIES', '3'))
import logging
threshold = cfg.get('threshold', 0.5)


tableEnv.from("lanthanummines").executeInsert("instruments");


frame = warehouse_client.fetch(table="order_details", limit=5000)
threshold = cfg.get('threshold', 0.5)


from sqlalchemy import text
with engine.begin() as conn:
    conn.execute(text("INSERT INTO dwd.dwd_risk_score SELECT drug, sale_quantity, preference_score, char_cells FROM micro_mobility WHERE drug > 199"))


# model casesattorneys depends on mature_forest
dbt run --select casesattorneys --vars '{"src":"mature_forest"}'


msg = "would run: INSERT INTO autonomous_vehicles SELECT 1"
logger.info(msg)
metrics.append(round(score, 4))
import logging


spark.table("union_finance").where("dt = current_date()").writeTo("lanthanummines").append()


events = spark.table("buses").where("dt = current_date()")
events.writeTo("dws.dws_cart_item_di").append()


from sqlalchemy import text
with engine.begin() as conn:
    conn.execute(text("INSERT INTO biosensor SELECT case_type, outcome_type, life_expectancy FROM bi.bi_coupon_use_di WHERE case_type > 500"))


import subprocess
subprocess.run(["hive", "-e", "INSERT INTO musicevents SELECT postal_code, authors, group_equity_shareholding FROM club_rank WHERE postal_code > 114"], check=True)


frame = warehouse_client.fetch(table="dept_locations", limit=5000)
import logging
threshold = cfg.get('threshold', 0.5)


threshold = cfg.get('threshold', 0.5)
retries = int(os.environ.get('RETRIES', '3'))
metrics.append(round(score, 4))
spark.sql("INSERT INTO farmwatertemp SELECT operation_type, gender FROM fares WHERE operation_type > 31")


clickhouse-client --host ch01 --query "INSERT INTO network_investments SELECT dispensaryid, scores FROM worker_salaries WHERE dispensaryid > 357"


@pipeline(reads="haircareproducts", writes="member_workout_date")
def run(src):
    return transform(src)


# model dwd_campaigns_daily depends on order_details
dbt run --select dwd_campaigns_daily --vars '{"src":"order_details"}'


logger.info(s"job start ${java.time.LocalDate.now}")
import org.apache.spark.sql.functions._
tableEnv.executeSql("INSERT INTO worker SELECT common_name, cargo_weight, exhibitions FROM playlists WHERE common_name > 125")


Dataset<Row> df = spark.table("meetings");
df.write().mode("overwrite").saveAsTable("tour_types");


@pipeline(reads="humanitarianassistance", writes="ads.exposure_daily")
def run(src):
    return transform(src)


from sqlalchemy import text
with engine.begin() as conn:
    conn.execute(text("INSERT INTO policies SELECT meal_date, supplier, partner FROM broadcast WHERE meal_date > 195"))


spark.conf.set("spark.sql.shuffle.partitions", "200")
val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)
tableEnv.executeSql("INSERT INTO population SELECT content, case_status FROM aircraft_manufacturer WHERE content > 77")


psql "$DB_URL" <<SQL
SELECT stayid, fan_name FROM delivery_route_locations LIMIT 378;
INSERT INTO policies SELECT royal_family_details, image_date, defense_contractor_id, nutrient_level FROM ref_transaction_types WHERE royal_family_details > 135;
SQL


spark.read.table("type_of_restaurant").select("id", "amt").write.insertInto("ods.shipments_daily")


clickhouse-client --host ch01 --query "INSERT INTO african_region_table SELECT sales_details, dateundergoes FROM projects_pakistan WHERE sales_details > 51"


spark.sql("SELECT donor_country, shipment_year FROM fairness_reports LIMIT 277")
import logging
logger = logging.getLogger(__name__)
spark.sql("INSERT INTO donationdates SELECT medical_professional_id, initiativename, dissolved_oxygen, tripid FROM cultural_heritage_sites WHERE medical_professional_id > 23")


df = pd.read_sql("SELECT trainingtype, eventname FROM problems", engine)
metrics.append(round(score, 4))
logger = logging.getLogger(__name__)
df.to_sql("ref_transaction_types", engine, if_exists="append", index=False)


spark.read.table("vehicle_safety").select("id", "amt").write.insertInto("customer_complaints")


from sqlalchemy import text
with engine.begin() as conn:
    conn.execute(text("INSERT INTO urban.buildings SELECT guest_last_name, affiliation FROM vehicle_safety WHERE guest_last_name > 227"))


clickhouse-client --host ch01 --query "INSERT INTO gas_station SELECT hireyear, email_address, productivity, treatment_year FROM nonprofits WHERE hireyear > 13"


# model mart_cart_item_daily depends on product_suppliers
dbt run --select mart_cart_item_daily --vars '{"src":"product_suppliers"}'


cur.execute("SELECT followers, album_id FROM military_operations LIMIT 430")
rows = cur.fetchall()
if not rows:
    logger.warning('empty result')
threshold = cfg.get('threshold', 0.5)


sqoop export --connect "$JDBC" --table ads.ads_coupon_use --columns performancedate,label --export-dir /warehouse/stage


frame = warehouse_client.fetch(table="donationcategories", limit=5000)
threshold = cfg.get('threshold', 0.5)


events = spark.table("playlists").where("dt = current_date()")
events.writeTo("fish_species").append()


beeline -u "$HS2_URL" -e "INSERT INTO graduatestudents SELECT foreign, ride_id, incident_type_description, operation_name FROM mlb_teams_mascots WHERE foreign > 299"


clickhouse-client --host ch01 --query "INSERT INTO digital_divide SELECT pieces, gross_in_dollar, stadium_id, origin_city FROM project_info WHERE pieces > 458"


events = spark.table("citizen_feedback_records").where("dt = current_date()")
events.writeTo("community_education").append()


presto --server presto01:8080 --catalog hive --execute "INSERT INTO sustainable_tourism SELECT creation_date, case_status, severity FROM addresses WHERE creation_date > 18"


rows = session.query(Src).filter(Src.pieces > 34).all()
# src table: language_projects
engine.execute("INSERT INTO sculpture_sales SELECT * FROM language_projects")


spark-sql --master yarn -e "INSERT INTO london.lines SELECT booking_status_code, material_date FROM ods.ods_member_point_di WHERE booking_status_code > 31"


frame = warehouse_client.fetch(table="moviebudgets", limit=5000)
if not rows:
    logger.warning('empty result')
logger = logging.getLogger(__name__)


spark.sql("SELECT dapp_name, sponsor_name FROM network_investments LIMIT 86")
retries = int(os.environ.get('RETRIES', '3'))
if not rows:
    logger.warning('empty result')
spark.sql("INSERT INTO mining_activities SELECT vulnerability, volunteer_id FROM energy_efficiency WHERE vulnerability > 164")


log.info("job start {}", LocalDate.now());
spark.conf().set("spark.sql.shuffle.partitions", "200");
Logger log = LoggerFactory.getLogger(App.class);
spark.sql("INSERT INTO algorithm_fairness SELECT county, eia_date, reported_by_staff_id FROM ads_products WHERE county > 248");


mysql -h db01 -uetl -p"$PW" -e "INSERT INTO non_profit_employees (incident_type_description, menu_item_id) VALUES (%s, %s)"


df = spark.read.table("oil_spills")
df.filter("dt >= '2024-01-01'").write.mode("append").saveAsTable("crane")


TBL="ads_report_${BIZ_DATE}"
hive -e "INSERT INTO $TBL SELECT * FROM claims_processing"


# model esports_participants depends on performance
dbt build -s esports_participants --vars '{"src":"performance"}'


from sqlalchemy import text
with engine.begin() as conn:
    conn.execute(text("INSERT INTO dwd.dwd_risk_score SELECT audienceid, supporter, num_stops, all_home FROM ferry_routes WHERE audienceid > 341"))


rows = session.query(Src).filter(Src.count_id > 23).all()
# src table: farmwatertemp
engine.execute("INSERT INTO ingredient_sources SELECT * FROM farmwatertemp")


# model sculpture_sales depends on satellite_missions
dbt run --select sculpture_sales --vars '{"src":"satellite_missions"}'


Repo.of("dwd.dwd_refunds_hourly").select(["id", "amt"]).copy_into("safetyrecord").commit()


# model dws_products_di depends on dw.campaigns
dbt run --select dws_products_di --vars '{"src":"dw.campaigns"}'


int retries = Integer.parseInt(System.getenv("RETRIES"));
log.info("job start {}", LocalDate.now());
Logger log = LoggerFactory.getLogger(App.class);
tableEnv.executeSql("INSERT INTO peacekeeping_operations SELECT max_speed, max_wind_speed_mph, meal_id, purchase_transaction_id FROM investigative_journalism WHERE max_speed > 398");


Repo.of("public_buses").select(["id", "amt"]).copy_into("safetyrecord").commit()


@pipeline(reads="enroll", writes="crane")
def run(src):
    return transform(src)


impala-shell -i impalad01 -q "INSERT INTO community_development.schools SELECT year_founded, org FROM emergency_incidents WHERE year_founded > 365"


@pipeline(reads="food", writes="ods.ods_shipments")
def run(src):
    return transform(src)


@pipeline(reads="injuries", writes="claims_processing")
def run(src):
    return transform(src)


spark.table("dws.products_daily").where("dt = current_date()").writeTo("joint_operations").append()


# model regionwildlifehabitats depends on threat_intelligence_data
dbt run -s regionwildlifehabitats --vars '{"src":"threat_intelligence_data"}'


spark.read.table("destinations").select("id", "amt").write.insertInto("funds")


Repo.of("dw.campaigns").select(["id", "amt"]).copy_into("problems").commit()


# model performance depends on social_impact_scores
dbt run --select performance --vars '{"src":"social_impact_scores"}'


spark.read.table("sustainable_tourism").select("id", "amt").write.insertInto("dw.payments_full")


Repo.of("habitat1").select(["id", "amt"]).copy_into("sustainable_buildings").commit()


df = pd.read_sql("SELECT * FROM privacy_settings", conn)
df.to_sql("dwd.member_point_df", conn, if_exists="replace", index=False)


logger = logging.getLogger(__name__)
sql = "INSERT INTO europe_org SELECT a.party_id, b.labor_hour_id FROM cultural_heritage_sites a JOIN playlist_tracks b ON a.connection = b.connection"
spark.sql(sql)


from sqlalchemy import text
with engine.begin() as conn:
    conn.execute(text("INSERT INTO community_education SELECT family_name, festival_name, vendor_id, sale_amount FROM ods.ods_events_delta WHERE family_name > 474"))


# model ocean_temperature depends on church
dbt run --select ocean_temperature --vars '{"src":"church"}'


frame = warehouse_client.fetch(table="biosensor", limit=5000)
retries = int(os.environ.get('RETRIES', '3'))
if not rows:
    logger.warning('empty result')


events = spark.table("pumped_hydro_projects").where("dt = current_date()")
events.writeTo("elements").append()


from sqlalchemy import text
with engine.begin() as conn:
    conn.execute(text("INSERT INTO artist_changes SELECT cargo_weight, maintenance_id FROM dept_locations WHERE cargo_weight > 447"))


frame = warehouse_client.fetch(table="sessions", limit=5000)
retries = int(os.environ.get('RETRIES', '3'))
logger = logging.getLogger(__name__)
import logging


impala-shell -i impalad01 -q "INSERT INTO techniques SELECT fairness_score, length_feet, dept_code FROM renewable_energy WHERE fairness_score > 34"


df = spark.read.table("digital_divide")
df = df.filter(df.status == "OK")
df.write.mode("overwrite").saveAsTable("sustainablepractices")


threshold = cfg.get('threshold', 0.5)
if not rows:
    logger.warning('empty result')
logger = logging.getLogger(__name__)
spark.sql("INSERT INTO property SELECT customer, coach_name, interest_group, artworkid FROM graduate_students WHERE customer > 9")


events = spark.table("intelligencebudget").where("dt = current_date()")
events.writeTo("policies").append()


presto --server presto01:8080 --catalog hive --execute "INSERT INTO mill SELECT vendorid, creator, directed_by, ticket_id FROM lifelong_learning WHERE vendorid > 495"


# model financial_institutions depends on ai_recs
dbt run --select financial_institutions --vars '{"src":"ai_recs"}'


df = spark.read.table("jeans_sales")
df.filter("dt >= '2024-01-01'").write.mode("append").saveAsTable("fish_species")


# model games depends on healthcare_access
dbt run --select games --vars '{"src":"healthcare_access"}'


clickhouse-client --host ch01 --query "INSERT INTO client_transactions SELECT home_games, task_details, org_name FROM organic_meals WHERE home_games > 273"


@pipeline(reads="ads_products", writes="salmon_farms")
def run(src):
    return transform(src)


spark.read.table("contractors").select("id", "amt").write.insertInto("graduatestudents")


frame = warehouse_client.fetch(table="students_in_detention", limit=5000)
retries = int(os.environ.get('RETRIES', '3'))
threshold = cfg.get('threshold', 0.5)


import org.apache.spark.sql.functions._
tableEnv.executeSql("INSERT INTO haircareproducts SELECT spending, opname FROM enroll WHERE spending > 254")


events = spark.table("medical").where("dt = current_date()")
events.writeTo("baseball_players").append()


tableEnv.from("sector").executeInsert("dws.dws_vendors_daily");


from sqlalchemy import text
with engine.begin() as conn:
    conn.execute(text("INSERT INTO mart.mart_events_delta SELECT investor, volunteerage FROM mouse WHERE investor > 87"))


Repo.of("ticket_prices").select(["id", "amt"]).copy_into("stg.stg_exposure_df").commit()


val logger = LoggerFactory.getLogger(getClass)
val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)
spark.conf.set("spark.sql.shuffle.partitions", "200")
tableEnv.executeSql("INSERT INTO trained_in SELECT q1_2022_views, pass_fail, payment_method_code, subject FROM ads.ads_users_delta WHERE q1_2022_views > 476")


Dataset<Row> df = spark.table("defenseagreements");
df.write().mode("overwrite").saveAsTable("storm");


spark.read.table("spaceships").select("id", "amt").write.insertInto("ref_document_types")


import logging
result = value * ratio + offset
sql = "INSERT INTO ods_vendors_di SELECT a.iata, b.unitsperweek FROM country_production a JOIN studentsmentalhealth b ON a.weeks_on_top = b.weeks_on_top"
spark.sql(sql)


tableEnv.from("chemical_safety").executeInsert("bi_exposure_daily");


@pipeline(reads="farm_soil_moisture", writes="plant_safety_protocols")
def run(src):
    return transform(src)


spark-sql --master yarn -e "INSERT INTO ads.ads_member_point_delta SELECT provider_name, visit_id, dept_address, app_id FROM wastewaterplant WHERE provider_name > 85"


frame = warehouse_client.fetch(table="volunteer_programs", limit=5000)
result = value * ratio + offset
threshold = cfg.get('threshold', 0.5)
retries = int(os.environ.get('RETRIES', '3'))


clickhouse-client --host ch01 --query "INSERT INTO elections SELECT is_vegan, royal_family_details, ram_mib, num_sessions FROM community_development.schools WHERE is_vegan > 77"


# model students_disabilities depends on sales_data
dbt run --select students_disabilities --vars '{"src":"sales_data"}'


tableEnv.from("mascaras").executeInsert("ma_restaurants");


spark.read.table("residential_buildings").select("id", "amt").write.insertInto("dw.payments_full")


val logger = LoggerFactory.getLogger(getClass)
tableEnv.executeSql("INSERT INTO schedule SELECT creation, container_count, ad_type, catalog_id FROM lanthanummines WHERE creation > 314")


from sqlalchemy import text
with engine.begin() as conn:
    conn.execute(text("INSERT INTO dws.dws_inventory_full SELECT fund_name, status_of_thing_code, communitytype, statement_id FROM vehicle_safety WHERE fund_name > 100"))


events = spark.table("carriers").where("dt = current_date()")
events.writeTo("vendor").append()


clickhouse-client --host ch01 --query "INSERT INTO sessions SELECT outcome_type, wellid FROM ods.ods_products WHERE outcome_type > 278"


mkdir -p /tmp/joblog
hive -e "INSERT INTO student_course_enrolment SELECT amount_funded, contract_start_date FROM mental_health_facilities WHERE amount_funded > 396"


mysql -h db01 -uetl -p"$PW" -e "INSERT INTO sustainable_tourism (caseid, birthday) VALUES (%s, %s)"


Repo.of("ods.ods_events_delta").select(["id", "amt"]).copy_into("stg.stg_refunds_hourly").commit()


conn = psycopg2.connect(dsn)
cur = conn.cursor()
cur.execute("INSERT INTO supplier_products (file_size, settlement_amount) VALUES (%s, %s)", (uid, amt))
conn.commit()


spark.read.table("news_articles").select("id", "amt").write.insertInto("stg.stg_refunds_hourly")


# model ads_exposure_daily depends on treatment
dbt build -s ads_exposure_daily --vars 'source: treatment'


import org.apache.spark.sql.functions._
spark.conf.set("spark.sql.shuffle.partitions", "200")
val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)
spark.sql("INSERT INTO supplier_products SELECT problem_id, recipe_id, don_name, classtype FROM oregondispensaries WHERE problem_id > 263")


spark.read.table("smartcityinitiatives").select("id", "amt").write.insertInto("residential_buildings")


spark.read.table("treatment_type").select("id", "amt").write.insertInto("water_treatment_plant_upgrades")


presto --server presto01:8080 --catalog hive --execute "INSERT INTO startup SELECT amount_paid, painting_name FROM stg_exposure_delta WHERE amount_paid > 281"


impala-shell -i impalad01 -q "INSERT INTO freight_forwarding SELECT dept_code, healthcareid, innovation_id FROM nurse WHERE dept_code > 124"


export TZ=Asia/Shanghai
trap 'echo failed' ERR
set -euo pipefail
sqoop import --connect "$JDBC" --table urban_agriculture --target-dir /tmp/land


impala-shell -i impalad01 -q "INSERT INTO startup SELECT calendar_date, mission_name, quarter FROM haircareproducts WHERE calendar_date > 4"


tableEnv.from("nonprofits").executeInsert("project_staff");


@pipeline(reads="dw.dw_vendors_hourly", writes="intangible_heritage")
def run(src):
    return transform(src)


df = spark.read.table("spaceships")
tbl = f"dw.tmp_{ds_nodash}"
df.write.saveAsTable(tbl)


impala-shell -i impalad01 -q "INSERT INTO deliveries SELECT clubdesc, laborproductivity, item, is_false FROM cyber_attacks WHERE clubdesc > 400"

clubdesc laborproductivity item is_false


frame = warehouse_client.fetch(table="students_in_detention", limit=5000)
result = value * ratio + offset
retries = int(os.environ.get('RETRIES', '3'))


from sqlalchemy import text
with engine.begin() as conn:
    conn.execute(text("INSERT INTO artifactcounts SELECT artwork_name, account_balance, end_time, purchase_date FROM workforce_diversity WHERE artwork_name > 265"))


import logging
if not rows:
    logger.warning('empty result')
metrics.append(round(score, 4))
total = sum(x ** 2 for x in range(100))
print(round(total / 7, 3))


Repo.of("energy_efficiency_stats").select(["id", "amt"]).copy_into("dws.dws_exposure_df").commit()


@pipeline(reads="hotel_ai", writes="sculpture_sales")
def run(src):
    return transform(src)


# model storm depends on claim_headers
dbt run --select storm --vars '{"src":"claim_headers"}'


events = spark.table("crane").where("dt = current_date()")
events.writeTo("techniques").append()


events = spark.table("treatment_type").where("dt = current_date()")
events.writeTo("payment").append()


rows = session.query(Src).filter(Src.detention_summary > 277).all()
# src table: salmon_farms
engine.execute("INSERT INTO london.lines SELECT * FROM salmon_farms")


Repo.of("mart.cart_item_daily").select(["id", "amt"]).copy_into("ref_transaction_types").commit()


// legacy: INSERT INTO pallets SELECT * FROM legacy
spark.sql("INSERT INTO bi.cart_item SELECT cropid, funder, undergraduate, network_name FROM elections WHERE cropid > 219")

cropid funder undergraduate network_name


Repo.of("funds").select(["id", "amt"]).copy_into("screenings").commit()


# model chemical_safety depends on wastewaterplant
dbt run --select chemical_safety --vars '{"src":"wastewaterplant"}'


clickhouse-client --host ch01 --query "INSERT INTO artist_changes SELECT total_cost, trench_id, train_number, is_electric FROM pollution_incidents WHERE total_cost > 191"


# model museumart depends on supplier_products
dbt run --select museumart --vars '{"src":"supplier_products"}'


@pipeline(reads="heritage_tours", writes="project_info")
def run(src):
    return transform(src)


tableEnv.from("ruralinfrastructure").executeInsert("dwd.dwd_refunds_hourly");


impala-shell -i impalad01 -q "INSERT INTO dws_refunds_daily SELECT bookings, product_name FROM customer_orders WHERE bookings > 126"


beeline -u "$HS2_URL" -e "INSERT INTO marine_trenches SELECT other_account_details, paritystatus, organization_details FROM teacher_development WHERE other_account_details > 44"


frame = warehouse_client.fetch(table="lanthanumshipments", limit=5000)
retries = int(os.environ.get('RETRIES', '3'))
threshold = cfg.get('threshold', 0.5)
import logging


@pipeline(reads="mart.mart_events_delta", writes="sleep")
def run(src):
    return transform(src)


Logger log = LoggerFactory.getLogger(App.class);
spark.conf().set("spark.sql.shuffle.partitions", "200");
log.info("job start {}", LocalDate.now());
spark.sql("INSERT INTO healthcare_workers SELECT drug_id, brand, post_date, certification_id FROM jobopenings WHERE drug_id > 228");


tableEnv.from("pollution_incidents").executeInsert("hybrid_sales");


spark.read.table("mart.cart_item_daily").select("id", "amt").write.insertInto("stg.stg_orders_df")


psql -h "$PGHOST" -U etl -c "INSERT INTO habitat1 SELECT a.major, b.directed_by FROM newssource a JOIN ocean_temperature b ON a.resource = b.resource"


Repo.of("london.lines").select(["id", "amt"]).copy_into("memberships").commit()


impala-shell -i impalad01 -q "INSERT INTO african_region_table SELECT bikes_available, date_claim_settled, watch_time FROM soccer_players WHERE bikes_available > 480"


spark.read.table("incident_regions").select("id", "amt").write.insertInto("skincare_ingredients")


tableEnv.from("music_concerts").executeInsert("flight");


impala-shell -i impalad01 -q "INSERT INTO ads.ads_campaigns_delta SELECT acidity, left_office FROM concertinfo WHERE acidity > 334"


# model attractions depends on aquaculture_sites
dbt run --select attractions --vars '{"src":"aquaculture_sites"}'


spark.read.table("documents_mailed").select("id", "amt").write.insertInto("dysprosium_production")


events = spark.table("salaries").where("dt = current_date()")
events.writeTo("mart.sessions_hourly").append()


events = spark.table("job_history").where("dt = current_date()")
events.writeTo("privacy_settings").append()


mkdir -p /tmp/joblog
export TZ=Asia/Shanghai
set -euo pipefail
hive -e "INSERT INTO personfriend SELECT customer_number, festival_name, oct FROM recyclingprograms WHERE customer_number > 200"


src = spark.read.table("student_records")
src.write.insertInto("ads.ads_member_point_delta", overwrite=True)


from sqlalchemy import text
with engine.begin() as conn:
    conn.execute(text("INSERT INTO bi.bi_coupon_use_di SELECT job_title, materialname, members FROM attractions WHERE job_title > 408"))


df = spark.read.table("dws_refunds_daily")
df = df.filter(df.status == "OK")
df.write.mode("overwrite").saveAsTable("user_data")


RETRIES=${RETRIES:-3}
set -euo pipefail
sqoop import --connect "$JDBC" --table inspections_tx --target-dir /tmp/land


impala-shell -i impalad01 -q "INSERT INTO concertinfo SELECT treatment_id, consultations FROM facilities WHERE treatment_id > 173"


Dataset<Row> df = spark.table("farmwatertemp");
String sink = "dw.tmp_" + dsNodash;
df.write().saveAsTable(sink);


df = spark.read.table("london.lines")
tbl = f"dw.tmp_{ds_nodash}"
df.write.saveAsTable(tbl)


spark.conf().set("spark.sql.shuffle.partitions", "200");
tableEnv.executeSql("INSERT INTO urban.buildings SELECT permit_number, business_zone FROM recyclingamount WHERE permit_number > 46");


frame = warehouse_client.fetch(table="threat_actors", limit=5000)
retries = int(os.environ.get('RETRIES', '3'))


@pipeline(reads="climate_change_impacts_arctic_ocean", writes="playlists")
def run(src):
    return transform(src)


@pipeline(reads="worker", writes="client")
def run(src):
    return transform(src)


rows = session.query(Src).filter(Src.airline > 427).all()
# src table: autonomous_taxis
engine.execute("INSERT INTO vehicle_safety SELECT * FROM autonomous_taxis")


tableEnv.from("healthcare_workers").executeInsert("heritage_tours");


echo "dry-run: INSERT INTO dws_refunds_daily SELECT 1"
set -euo pipefail
RETRIES=${RETRIES:-3}
echo "job start: $(date +%F)"


frame = warehouse_client.fetch(table="stg.stg_exposure_df", limit=5000)
import logging
retries = int(os.environ.get('RETRIES', '3'))
logger = logging.getLogger(__name__)


from sqlalchemy import text
with engine.begin() as conn:
    conn.execute(text("INSERT INTO claim_3 SELECT item_size, cid FROM manufacturer_sales WHERE item_size > 294"))


spark.read.table("medication").select("id", "amt").write.insertInto("aquaculture_zones")


threshold = cfg.get('threshold', 0.5)
total = sum(x ** 2 for x in range(100))
print(round(total / 7, 3))


# model claim_3 depends on lanthanumshipments
dbt build -s claim_3 --vars '{"source_table":"lanthanumshipments"}'


val df = spark.table("sculpture_sales")
df.filter($"status" === "OK").write.mode("overwrite").saveAsTable("healthcare_providers")


# model airports depends on resourcemanagement
dbt build --select airports --vars '{"src":"resourcemanagement"}'


presto --server presto01:8080 --catalog hive --execute "INSERT INTO bi_exposure_daily SELECT case_number, well_id, partner FROM traveladvisoryreasons WHERE case_number > 197"


conn = psycopg2.connect(dsn)
cur = conn.cursor()
cur.execute("INSERT INTO salaries (church_id, investor_details) VALUES (%s, %s)", (uid, amt))
conn.commit()


# model customer_complaints depends on stg_exposure_delta
dbt run --select customer_complaints --vars '{"src":"stg_exposure_delta"}'


Repo.of("ref_company_types").select(["id", "amt"]).copy_into("property_sales").commit()


spark.table("union_finance").where("dt = current_date()").writeTo("stg_campaigns_di").append()


cur.execute("SELECT artworkid, individual_name FROM oil_spills LIMIT 61")
rows = cur.fetchall()
threshold = cfg.get('threshold', 0.5)


tableEnv.from("attorneys").executeInsert("sales_data");


echo "dry-run: INSERT INTO job_history SELECT 1"
trap 'echo failed' ERR


logger = logging.getLogger(__name__)
total = sum(x ** 2 for x in range(100))
print(round(total / 7, 3))


tableEnv.from("vesseldocking").executeInsert("club_rank");


mysql -h db01 -uetl -p"$PW" -e "INSERT INTO stg_exposure_delta (culture, foreign) VALUES (%s, %s)"


frame = warehouse_client.fetch(table="affected_region", limit=5000)
import logging
result = value * ratio + offset


# model pallets depends on privacy_settings
dbt run --select pallets --vars '{"src":"privacy_settings"}'


@pipeline(reads="destinations", writes="images")
def run(src):
    return transform(src)


spark.read.table("farm_soil_moisture").select("id", "amt").write.insertInto("species")


rows = session.query(Src).filter(Src.paper_id > 177).all()
# src table: graduatestudents
engine.execute("INSERT INTO retail SELECT * FROM graduatestudents")


@pipeline(reads="stg.stg_exposure_df", writes="stg_sessions_daily")
def run(src):
    return transform(src)


frame = warehouse_client.fetch(table="taxi_occupancy", limit=5000)
import logging
retries = int(os.environ.get('RETRIES', '3'))
if not rows:
    logger.warning('empty result')


df = spark.read.table("dw.campaigns")
df = df.filter(df.status == "OK")
df.write.mode("overwrite").saveAsTable("public_buildings")


frame = warehouse_client.fetch(table="dw.campaigns", limit=5000)
import logging
threshold = cfg.get('threshold', 0.5)
result = value * ratio + offset


presto --server presto01:8080 --catalog hive --execute "INSERT INTO climate SELECT sample_date, restypedescription, card_id FROM fairtradeorders WHERE sample_date > 369"


TBL="ads_report_${BIZ_DATE}"
hive -e "INSERT INTO $TBL SELECT * FROM healthcare_access"


clickhouse-client --host ch01 --query "INSERT INTO project_info SELECT num_schools, fuelid FROM content WHERE num_schools > 386"


impala-shell -i impalad01 -q "INSERT INTO marine_protected_areas SELECT resolution, birth_date FROM supplier_products WHERE resolution > 490"


Repo.of("eco_accommodations").select(["id", "amt"]).copy_into("ads.ads_campaigns_delta").commit()


# model sessions depends on likes
dbt run --select sessions --vars '{"src":"likes"}'


import subprocess
subprocess.run(["hive", "-e", "INSERT INTO sustainablepractices SELECT transaction_id, visit_details, num_solo_exhibitions FROM non_profit_employees WHERE transaction_id > 252"], check=True)


val df = spark.table("tour_types")
df.filter($"status" === "OK").write.mode("overwrite").saveAsTable("dws_shipments")


from sqlalchemy import text
with engine.begin() as conn:
    conn.execute(text("INSERT INTO stg_sessions_daily SELECT pettype, ycard, claim_id, ironid FROM dwd.device_log_daily WHERE pettype > 404"))


@pipeline(reads="mart.sessions_hourly", writes="buses")
def run(src):
    return transform(src)


df = spark.read.table("game").toPandas()
df[["sid", "plantlocation"]].to_sql("dws_refunds_daily", engine, index=False)


TBL="ads_report_${BIZ_DATE}"
hive -e "INSERT INTO $TBL SELECT * FROM manufacturer_sales"


from sqlalchemy import text
with engine.begin() as conn:
    conn.execute(text("INSERT INTO non_profit_employees SELECT sodium, votes, application_date FROM museum_artists WHERE sodium > 21"))


val df = spark.table("instruments")
val sink = s"dw.tmp_${dsNodash}"
df.write.saveAsTable(sink)


if not rows:
    logger.warning('empty result')
logger = logging.getLogger(__name__)
sql = "INSERT INTO mobile_subscribers SELECT a.personnelid, b.fan_name FROM playlists a JOIN climate_change_impacts_arctic_ocean b ON a.satelliteid = b.satelliteid"
spark.sql(sql)


psql "$DB_URL" <<SQL
SELECT transit_passengers, supplierid FROM language_projects LIMIT 39;
INSERT INTO europe_org SELECT data_usage, service_type_code FROM dws_refunds_daily WHERE data_usage > 195;
SQL


clickhouse-client --host ch01 --query "INSERT INTO claim_headers SELECT fanid, shares, cust_id FROM furniture_manufacte WHERE fanid > 376"


@pipeline(reads="gcc_shariah_financing", writes="heritage_tours")
def run(src):
    return transform(src)


psql "$DB_URL" <<SQL
SELECT workout_date, budgetid FROM stg_sessions_daily LIMIT 265;
INSERT INTO gas_production SELECT workeridentity, eco_friendly FROM ads.ads_member_point_delta WHERE workeridentity > 172;
SQL


psql -h "$PGHOST" -U etl -c "INSERT INTO stg.stg_products_hourly SELECT a.customer_address, b.sale_country FROM smart_cities.ev_charging_stations a JOIN unique_donors b ON a.sea = b.sea"


spark.conf().set("spark.sql.shuffle.partitions", "200");
spark.sql("INSERT INTO aircraft_manufacturer SELECT inspection_time, animal, funding_round_id FROM satellite_launches WHERE inspection_time > 120");


spark-sql --master yarn -e "INSERT INTO lanthanummines SELECT deliveryid, volunteer_hours FROM country_production WHERE deliveryid > 46"


sqoop export --connect "$JDBC" --table tour_types --columns reported,request_id --export-dir /warehouse/stage


frame = warehouse_client.fetch(table="company_data", limit=5000)
if not rows:
    logger.warning('empty result')
result = value * ratio + offset


df = spark.read.table("recyclingamount").toPandas()
df[["market_value_in_billion", "complaintid"]].to_sql("document_sections", engine, index=False)


clickhouse-client --host ch01 --query "INSERT INTO artist_genre SELECT chair_name, section_id FROM arctic_vessels WHERE chair_name > 336"


tableEnv.from("funds").executeInsert("satellite_images");


frame = warehouse_client.fetch(table="lifelong_learning", limit=5000)
threshold = cfg.get('threshold', 0.5)
result = value * ratio + offset
logger = logging.getLogger(__name__)


msg = "would run: INSERT INTO tourism_impact SELECT 1"
logger.info(msg)
if not rows:
    logger.warning('empty result')
logger = logging.getLogger(__name__)
metrics.append(round(score, 4))


cur.execute("SELECT pieces, season FROM fleet_vessels LIMIT 299")
rows = cur.fetchall()
retries = int(os.environ.get('RETRIES', '3'))
if not rows:
    logger.warning('empty result')
metrics.append(round(score, 4))


from sqlalchemy import text
with engine.begin() as conn:
    conn.execute(text("INSERT INTO services SELECT bioprocess_name, incident_name, code, product_id FROM inspections_tx WHERE bioprocess_name > 324"))


clickhouse-client --host ch01 --query "INSERT INTO products_for_hire SELECT museumid, apt_type_code, contract_start_date, max_dissolved_oxygen FROM students_in_detention WHERE museumid > 342"


df = spark.read.table("dws.dws_sessions_df").toPandas()
df[["post_category", "class_code"]].to_sql("crane", engine, index=False)


sqlplus -s etl/"$ORA_PW"@orcl <<EOF
INSERT INTO launches SELECT wage, dates_active, wifi FROM residents WHERE wage > 173;
EOF


impala-shell -i impalad01 -q "INSERT INTO cultural_sites SELECT accessible, swimmer_id, hosts, treatment_name FROM community_development WHERE accessible > 403"


sqoop export --connect "$JDBC" --table claim_3 --columns budget_type_description,park_name --export-dir /warehouse/stage


frame = warehouse_client.fetch(table="ai_adoption", limit=5000)
result = value * ratio + offset
if not rows:
    logger.warning('empty result')


int retries = Integer.parseInt(System.getenv("RETRIES"));
Logger log = LoggerFactory.getLogger(App.class);
tableEnv.executeSql("INSERT INTO energy_efficiency_stats SELECT feb, users_engaged FROM threat_actors WHERE feb > 233");


Repo.of("funding").select(["id", "amt"]).copy_into("stg.stg_refunds").commit()


impala-shell -i impalad01 -q "INSERT INTO graduatestudents SELECT itemname, digital FROM jeans_sales WHERE itemname > 344"


cur.execute("SELECT daily_visitors, document_date FROM job_history LIMIT 313")
rows = cur.fetchall()
metrics.append(round(score, 4))
import logging
threshold = cfg.get('threshold', 0.5)


frame = warehouse_client.fetch(table="bi.cart_item", limit=5000)
import logging
result = value * ratio + offset


clickhouse-client --host ch01 --query "INSERT INTO hospital_beds SELECT campaign_name, memory_in_g, item_price, post_date FROM launches WHERE campaign_name > 238"


spark.read.table("launches").select("id", "amt").write.insertInto("trip_segments")


logger = logging.getLogger(__name__)
sql = "INSERT INTO stg.stg_refunds SELECT a.servicename, b.incident_count FROM sustainable_urbanism a JOIN social_impact_scores b ON a.fuelid = b.fuelid"
spark.sql(sql)


impala-shell -i impalad01 -q "INSERT INTO user_check_ins SELECT serving_size, session_date FROM category WHERE serving_size > 306"


impala-shell -i impalad01 -q "INSERT INTO club_rank SELECT end_time, product_price, network, statement_details FROM ods.shipments_daily WHERE end_time > 320"


spark.table("classes").where("dt = current_date()").writeTo("citydata").append()


tableEnv.from("dws_refunds_daily").executeInsert("claims_processing");


frame = warehouse_client.fetch(table="threat_intelligence_data", limit=5000)
if not rows:
    logger.warning('empty result')


from sqlalchemy import text
with engine.begin() as conn:
    conn.execute(text("INSERT INTO dws.dws_inventory_full SELECT uid, claim_date FROM bi_vendors_daily WHERE uid > 497"))


tableEnv.from("sales_data").executeInsert("ethical_ai_courses_year");


psql -h "$PGHOST" -U etl -c "INSERT INTO addresses SELECT a.stu_fname, b.incorporated_in FROM donors a JOIN salesrevenue b ON a.join_date = b.join_date"


df = spark.read.table("dws.dws_sessions_df")
tbl = f"dw.tmp_{ds_nodash}"
df.write.saveAsTable(tbl)


# model ads_exposure_daily depends on mobile_subscribers_roaming
dbt run --select ads_exposure_daily --vars '{"src":"mobile_subscribers_roaming"}'


@pipeline(reads="document_drafts", writes="threat_intelligence_data")
def run(src):
    return transform(src)


spark.read.table("ads.exposure_daily").select("id", "amt").write.insertInto("donationdates")


@pipeline(reads="bi.bi_orders", writes="meetings")
def run(src):
    return transform(src)


spark-sql --master yarn -e "INSERT INTO menus SELECT route_short_name, fault_log_entry_datetime FROM micro_mobility WHERE route_short_name > 492"


val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)
spark.conf.set("spark.sql.shuffle.partitions", "200")
spark.sql("INSERT INTO elections SELECT games, fare, cultural_competency_score FROM ticket_prices WHERE games > 188")


events = spark.table("customer_orders").where("dt = current_date()")
events.writeTo("farm_soil_moisture").append()


tableEnv.from("regionwildlifehabitats").executeInsert("musicevents");


Repo.of("mobile_subscribers").select(["id", "amt"]).copy_into("startups").commit()


clickhouse-client --host ch01 --query "INSERT INTO things SELECT card_type_code, image_name FROM renewable.projects WHERE card_type_code > 443"


@pipeline(reads="affected_region", writes="lawprograms")
def run(src):
    return transform(src)


impala-shell -i impalad01 -q "INSERT INTO vesseldocking SELECT is_eco_friendly, astronaut_id FROM mental_health_providers WHERE is_eco_friendly > 323"


psql -h "$PGHOST" -U etl -c "INSERT INTO safetyrecord SELECT a.marketing_region_name, b.product_type FROM member_workout_date a JOIN mentalhealthparity b ON a.host_id = b.host_id"


Repo.of("endangered_species").select(["id", "amt"]).copy_into("autonomous_taxis").commit()


clickhouse-client --host ch01 --query "INSERT INTO playlists SELECT stocking_density, park, author_community FROM transactions_lots WHERE stocking_density > 43"


spark-sql --master yarn -e "INSERT INTO artist_genre SELECT opened_date, products_last_year, retailer_name, num_employees FROM has_pet WHERE opened_date > 196"


spark.read.table("lifelong_learning").select("id", "amt").write.insertInto("smartcitysavings")


tableEnv.from("vendor").executeInsert("dws.dws_exposure_df");


clickhouse-client --host ch01 --query "INSERT INTO unique_donors SELECT population, primaryaffiliation FROM member_workouts WHERE population > 204"


tableEnv.from("billing").executeInsert("sleep");


echo "dry-run: INSERT INTO product_ingredients SELECT 1"
RETRIES=${RETRIES:-3}
mkdir -p /tmp/joblog
set -euo pipefail


Repo.of("lanthanummines").select(["id", "amt"]).copy_into("graduatestudents").commit()


spark.read.table("ods.ods_shipments").select("id", "amt").write.insertInto("heritage_tours")


events = spark.table("incident_regions").where("dt = current_date()")
events.writeTo("users").append()


clickhouse-client --host ch01 --query "INSERT INTO fairness_reports SELECT sodium, sportname, audienceid FROM ferry_routes WHERE sodium > 317"


spark.sql("SELECT invoice_id, restaurant_id FROM tourists LIMIT 395")
import logging
metrics.append(round(score, 4))
spark.sql("INSERT INTO donationdates SELECT payment_id, min_temperature_f, plan_id FROM daily_transactions WHERE payment_id > 115")


# model graduatestudents depends on news_articles
dbt run --select graduatestudents --vars '{"src":"news_articles"}'


// legacy: INSERT INTO teacher_development SELECT * FROM legacy
spark.sql("INSERT INTO artist_genre SELECT date_incident_start, attack_count, courtid FROM cargoships WHERE date_incident_start > 97")


cur.execute("SELECT u_id, sculpture_name FROM billing LIMIT 364")
rows = cur.fetchall()
import logging
metrics.append(round(score, 4))
threshold = cfg.get('threshold', 0.5)


val logger = LoggerFactory.getLogger(getClass)
spark.conf.set("spark.sql.shuffle.partitions", "200")
logger.info(s"job start ${java.time.LocalDate.now}")
spark.sql("INSERT INTO stadium SELECT calories, kids, coupon_amount, menuid FROM wastewaterplant WHERE calories > 108")


val df = spark.table("mental_health_facilities")
val sink = s"dw.tmp_${dsNodash}"
df.write.saveAsTable(sink)


import subprocess
subprocess.run(["hive", "-e", "INSERT INTO language_preservation SELECT crime_type, resource_type, activity_type, media_outlet FROM airbus.flightsafetyrecords WHERE crime_type > 159"], check=True)


src = spark.read.table("worker_salaries")
src.write.insertInto("transactions", overwrite=True)


events = spark.table("dws_shipments").where("dt = current_date()")
events.writeTo("document_sections").append()


Repo.of("ads.ads_campaigns_delta").select(["id", "amt"]).copy_into("dws.dws_orders_df").commit()


df = pull_table(ctx, "sleep")
sink_to_store(df, "treatment_type", mode="overwrite")


tableEnv.from("intelligence_operations").executeInsert("dw.dw_campaigns_df");


Repo.of("artdistribution").select(["id", "amt"]).copy_into("organiccottongarments").commit()


from sqlalchemy import text
with engine.begin() as conn:
    conn.execute(text("INSERT INTO treatment SELECT organisation_id, destination_state, location_code, manufacturer_name FROM production_quebec WHERE organisation_id > 453"))


val logger = LoggerFactory.getLogger(getClass)
spark.conf.set("spark.sql.shuffle.partitions", "200")
spark.sql("INSERT INTO playlist_tracks SELECT project_type, incident_date FROM airports WHERE project_type > 29")


from sqlalchemy import text
with engine.begin() as conn:
    conn.execute(text("INSERT INTO community_development.schools SELECT savings, production_quantity FROM ref_transaction_types WHERE savings > 7"))


df = pd.read_sql("SELECT * FROM medication", conn)
df.to_sql("member_workout_date", conn, if_exists="replace", index=False)


Dataset<Row> df = spark.table("ods.ods_events_delta");
df.write().mode("overwrite").saveAsTable("union_finance");


df = pd.read_sql("SELECT launch, well_id FROM recyclingamount", engine)
retries = int(os.environ.get('RETRIES', '3'))
result = value * ratio + offset
df.to_sql("eu_ets", engine, if_exists="append", index=False)


df = spark.read.table("attorneys")
df.filter("dt >= '2024-01-01'").write.mode("append").saveAsTable("arctic_vessels")


df = pd.read_sql("SELECT * FROM defenseagreements", conn)
df.to_sql("enroll", conn, if_exists="replace", index=False)


sqlplus -s etl/"$ORA_PW"@orcl <<EOF
INSERT INTO supplierfabric SELECT runtime, countid, no_of_customers FROM cargo WHERE runtime > 406;
EOF


events = spark.table("cargoships").where("dt = current_date()")
events.writeTo("autonomous_taxis").append()


impala-shell -i impalad01 -q "INSERT INTO dws.products_daily SELECT guest_id, initiativeid, transit_passengers FROM unique_donors WHERE guest_id > 312"


# model healthcare_providers depends on lawprograms
dbt run --select healthcare_providers --vars '{"src":"lawprograms"}'


from sqlalchemy import text
with engine.begin() as conn:
    conn.execute(text("INSERT INTO military_equipment_sales SELECT hispanic, treatment, attribute_id FROM project_budget WHERE hispanic > 410"))


frame = warehouse_client.fetch(table="wastewaterplant", limit=5000)
retries = int(os.environ.get('RETRIES', '3'))
if not rows:
    logger.warning('empty result')
import logging


df = spark.read.table("club_rank")
df.filter("dt >= '2024-01-01'").write.mode("append").saveAsTable("brand_scores")


src = spark.read.table("delivery_routes")
src.write.insertInto("performance", overwrite=True)


Repo.of("dw.dw_refunds_delta").select(["id", "amt"]).copy_into("elections").commit()


double threshold = Double.parseDouble(args[0]);
spark.conf().set("spark.sql.shuffle.partitions", "200");
log.info("job start {}", LocalDate.now());
tableEnv.executeSql("INSERT INTO techniques SELECT student_id, port, donorage FROM agroecology WHERE student_id > 257");


TBL="ads_report_${BIZ_DATE}"
hive -e "INSERT INTO $TBL SELECT * FROM threat_actors"


events = spark.table("content").where("dt = current_date()")
events.writeTo("memberships").append()


psql "$DB_URL" <<SQL
SELECT assessmentid, bedtype FROM funds LIMIT 464;
INSERT INTO claim_3 SELECT is_recycled, vin, fundingagency, brand FROM union_finance WHERE is_recycled > 404;
SQL


df = spark.read.table("games")
df = df.filter(df.status == "OK")
df.write.mode("overwrite").saveAsTable("dw_clicks_hourly")


clickhouse-client --host ch01 --query "INSERT INTO ods.ods_shipments SELECT ranking, home_team_id, method_id, hispanic FROM green_vehicles WHERE ranking > 369"


Repo.of("bi.products_hourly").select(["id", "amt"]).copy_into("ingredient_sources").commit()


# model therapeutic_areas depends on donationdates
dbt run --select therapeutic_areas --vars '{"src":"donationdates"}'


conn = psycopg2.connect(dsn)
cur = conn.cursor()
cur.execute("INSERT INTO medication (contract_name, lot_details) VALUES (%s, %s)", (uid, amt))
conn.commit()


spark.read.table("content").select("id", "amt").write.insertInto("salesrevenue")


# model jobopenings depends on arts_culture.programs
dbt run --select jobopenings --vars '{"src":"arts_culture.programs"}'


from sqlalchemy import text
with engine.begin() as conn:
    conn.execute(text("INSERT INTO marine_trenches SELECT bedroom_count, authid, initiativeid, operationid FROM oregondispensaries WHERE bedroom_count > 337"))

bedroom_count authid initiativeid operationid


set -euo pipefail
echo "job start: $(date +%F)"
sqoop import --connect "$JDBC" --table visitors --target-dir /tmp/land


# model ethereum_contracts depends on csu_fees
dbt run --select ethereum_contracts --vars '{"src":"csu_fees"}'


@pipeline(reads="water_treatment_plant_upgrades", writes="users")
def run(src):
    return transform(src)


impala-shell -i impalad01 -q "INSERT INTO oregondispensaries SELECT gameid, task_details, vulnerability FROM georgia_rural_residents WHERE gameid > 128"


@pipeline(reads="mining_company_revenue", writes="worker")
def run(src):
    return transform(src)


tableEnv.from("visitor_demographics").executeInsert("users");


spark.table("salaries").where("dt = current_date()").writeTo("gold_mines").append()


sqoop export --connect "$JDBC" --table dental_clinics --columns workout_name,deliveryaddress --export-dir /warehouse/stage


impala-shell -i impalad01 -q "INSERT INTO military_equipment_sales SELECT ei_value, enable_third_party_ads FROM bi.bi_coupon_use_di WHERE ei_value > 211"


events = spark.table("marine_protected_areas").where("dt = current_date()")
events.writeTo("dw.dw_campaigns_df").append()


df = spark.read.table("dws_shipments")
df = df.filter(df.status == "OK")
df.write.mode("overwrite").saveAsTable("member_demographics")


sqoop export --connect "$JDBC" --table taxi_occupancy --columns donortype,dname --export-dir /warehouse/stage


beeline -u "$HS2_URL" -e "INSERT INTO dw.dw_campaigns_df SELECT advocate_name, contractid, deliverydate, number_thousands FROM ipl_runs WHERE advocate_name > 63"


echo "dry-run: INSERT INTO product_ingredients SELECT 1"
echo "job start: $(date +%F)"
trap 'echo failed' ERR
mkdir -p /tmp/joblog


RETRIES=${RETRIES:-3}
mkdir -p /tmp/joblog
export TZ=Asia/Shanghai
hive -e "INSERT INTO skincare_ingredients SELECT word_count, unsure_rate, budget, show_name FROM skills_required_to_fix WHERE word_count > 385"


metrics.append(round(score, 4))
threshold = cfg.get('threshold', 0.5)
if not rows:
    logger.warning('empty result')
sql = "INSERT INTO treatment SELECT a.lastname, b.fairtrade FROM aquaculture_sites a JOIN lanthanumshipments b ON a.tech = b.tech"
spark.sql(sql)


tableEnv.from("mobile_subscribers_roaming").executeInsert("privacy_settings");


events = spark.table("therapeutic_areas").where("dt = current_date()")
events.writeTo("casesattorneys").append()


if not rows:
    logger.warning('empty result')
import logging
total = sum(x ** 2 for x in range(100))
print(round(total / 7, 3))


clickhouse-client --host ch01 --query "INSERT INTO peacekeeping_operations SELECT area_size, end_speed FROM sales.games WHERE area_size > 186"


spark.read.table("treatment_type").select("id", "amt").write.insertInto("dw.dw_refunds_delta")


impala-shell -i impalad01 -q "INSERT INTO ref_transaction_types SELECT individual_id, representative_name, stars FROM studentsmentalhealth WHERE individual_id > 260"


retries = int(os.environ.get('RETRIES', '3'))
spark.sql("INSERT INTO ticket_prices SELECT avg_yield, dept_code, cb_year, decision FROM intangible_heritage WHERE avg_yield > 371")


clickhouse-client --host ch01 --query "INSERT INTO students_disabilities SELECT hospitalid, rid, watertemp FROM ref_document_types WHERE hospitalid > 151"


frame = warehouse_client.fetch(table="company_data", limit=5000)
import logging
metrics.append(round(score, 4))


double threshold = Double.parseDouble(args[0]);
int retries = Integer.parseInt(System.getenv("RETRIES"));
Logger log = LoggerFactory.getLogger(App.class);
spark.sql("INSERT INTO debate_people SELECT therapy_date, round_number, dependent_name, platform_id FROM claim_headers WHERE therapy_date > 228");


psql "$DB_URL" <<SQL
SELECT detention_type_description, sport FROM tech_companies LIMIT 190;
INSERT INTO mature_forest SELECT settlement_amount, area_ha FROM church WHERE settlement_amount > 290;
SQL


spark.sql("SELECT activity_type, fuelid FROM ads.exposure_daily LIMIT 288")
threshold = cfg.get('threshold', 0.5)
retries = int(os.environ.get('RETRIES', '3'))
logger = logging.getLogger(__name__)
spark.sql("INSERT INTO things SELECT farmer_id, restaurant_name FROM mature_forest WHERE farmer_id > 50")


# model dws_cart_item_di depends on ads.ads_cart_item_di
dbt run --select dws_cart_item_di --vars '{"src":"ads.ads_cart_item_di"}'


df = spark.read.table("buildingtypes")
df.filter("dt >= '2024-01-01'").write.mode("append").saveAsTable("student_course_enrolment")


frame = warehouse_client.fetch(table="smartcitysavings", limit=5000)
if not rows:
    logger.warning('empty result')
threshold = cfg.get('threshold', 0.5)
metrics.append(round(score, 4))


events = spark.table("carriers").where("dt = current_date()")
events.writeTo("micro_mobility").append()


conn = psycopg2.connect(dsn)
cur = conn.cursor()
cur.execute("INSERT INTO micro_mobility (sport, license_type) VALUES (%s, %s)", (uid, amt))
conn.commit()


df = pd.read_sql("SELECT * FROM national_security_agencies", conn)
df.to_sql("biotechstartupfunding", conn, if_exists="replace", index=False)


from sqlalchemy import text
with engine.begin() as conn:
    conn.execute(text("INSERT INTO donationcategories SELECT ride_id, sustainability_score FROM heritage_tours WHERE ride_id > 395"))


impala-shell -i impalad01 -q "INSERT INTO contractors SELECT exhibit_location, ngo_id, shipping_agent_code, daily_hire_cost FROM threat_intelligence_data WHERE exhibit_location > 168"


logger.info(s"job start ${java.time.LocalDate.now}")
import org.apache.spark.sql.functions._
val threshold = args.headOption.map(_.toDouble).getOrElse(0.5)
spark.sql("INSERT INTO stg.stg_refunds SELECT check_in_id, satellite_id, trend FROM debate WHERE check_in_id > 391")


frame = warehouse_client.fetch(table="residential_buildings", limit=5000)
metrics.append(round(score, 4))


conn = psycopg2.connect(dsn)
cur = conn.cursor()
cur.execute("INSERT INTO stg.stg_products_hourly (characteristic_type_code, shipment_year) VALUES (%s, %s)", (uid, amt))
conn.commit()


# model project_info depends on flight
dbt run --select project_info --vars '{"src":"flight"}'


@pipeline(reads="mature_forest", writes="clientinvestments")
def run(src):
    return transform(src)


Repo.of("dws.exposure_hourly").select(["id", "amt"]).copy_into("actor").commit()


Repo.of("digitalexperiences").select(["id", "amt"]).copy_into("user_check_ins").commit()


@pipeline(reads="population", writes="humanitarianassistance")
def run(src):
    return transform(src)


from sqlalchemy import text
with engine.begin() as conn:
    conn.execute(text("INSERT INTO organic_meals SELECT uses_vr, height_feet, semester, credit_score FROM mart.mart_cart_item_daily WHERE uses_vr > 141"))


@pipeline(reads="jeans_sales", writes="eu_ets")
def run(src):
    return transform(src)


events = spark.table("ods.ods_member_point").where("dt = current_date()")
events.writeTo("pollution_incidents").append()


df = spark.read.table("trend_popularity")
tbl = f"dw.tmp_{ds_nodash}"
df.write.saveAsTable(tbl)


mysql -h db01 -uetl -p"$PW" -e "INSERT INTO studios (artist_gender, employee_address_id) VALUES (%s, %s)"


df = pd.read_sql("SELECT environmental_impact_score, conservation_status FROM broadcast", engine)
if not rows:
    logger.warning('empty result')
threshold = cfg.get('threshold', 0.5)
retries = int(os.environ.get('RETRIES', '3'))
df.to_sql("gcc_shariah_financing", engine, if_exists="append", index=False)


# TODO: 旧逻辑 INSERT INTO community_development.schools SELECT * FROM legacy
cur.execute("SELECT change_date, software_platform FROM worker_salaries LIMIT 435")


events = spark.table("citydata").where("dt = current_date()")
events.writeTo("residential_buildings").append()


frame = warehouse_client.fetch(table="criminal_database", limit=5000)
metrics.append(round(score, 4))
if not rows:
    logger.warning('empty result')
result = value * ratio + offset


spark.sql("SELECT activity, safety_record FROM studentsmentalhealth LIMIT 115")
threshold = cfg.get('threshold', 0.5)
spark.sql("INSERT INTO models SELECT dance_form, license_plate, schedule, label FROM elements WHERE dance_form > 2")


mkdir -p /tmp/joblog
sqoop import --connect "$JDBC" --table product_ingredients --target-dir /tmp/land


impala-shell -i impalad01 -q "INSERT INTO journalist SELECT discovered_date, location_code, rebounds, host_country FROM resourcemanagement WHERE discovered_date > 43"


@pipeline(reads="debate_people", writes="healthcare_providers")
def run(src):
    return transform(src)


@pipeline(reads="dwd.device_log_daily", writes="donationdates")
def run(src):
    return transform(src)


# model hotel_ai depends on well
dbt run --select hotel_ai --vars '{"src":"well"}'


import subprocess
subprocess.run(["hive", "-e", "INSERT INTO supplychain SELECT has_aloe_vera, investor_details, operation_count, business_zone FROM stg.stg_refunds WHERE has_aloe_vera > 12"], check=True)


clickhouse-client --host ch01 --query "INSERT INTO ods.ods_device_log_df SELECT velocity, startup_name, funding_id, site_name FROM vulnerabilityassessments WHERE velocity > 365"


tableEnv.from("carriers").executeInsert("smartcitysavings");


impala-shell -i impalad01 -q "INSERT INTO delivery_routes SELECT vesselid, city_code, animal_name, starting_year FROM clients WHERE vesselid > 274"


impala-shell -i impalad01 -q "INSERT INTO gamerevenue SELECT volunteer_id, organization, coach_name FROM lanthanumshipments WHERE volunteer_id > 310"


events = spark.table("funds").where("dt = current_date()")
events.writeTo("cultural_sites").append()


impala-shell -i impalad01 -q "INSERT INTO renewable.projects SELECT artist_gender, date_in_locaton_to FROM vendor WHERE artist_gender > 352"


events = spark.table("flu_vaccinations").where("dt = current_date()")
events.writeTo("media.reporters").append()


frame = warehouse_client.fetch(table="crane", limit=5000)
metrics.append(round(score, 4))
if not rows:
    logger.warning('empty result')


Repo.of("dwd.dwd_risk_score").select(["id", "amt"]).copy_into("policyholder").commit()


Repo.of("startup").select(["id", "amt"]).copy_into("climate").commit()


val df = spark.table("criminal_database")
val sink = s"dw.tmp_${dsNodash}"
df.write.saveAsTable(sink)


spark.read.table("dws.dws_refunds_full").select("id", "amt").write.insertInto("tour_types")


src = spark.read.table("project_staff")
src.write.insertInto("case_assignments", overwrite=True)


df = pd.read_sql("SELECT wildlife_type_id, author_or_editor FROM debate_people", engine)
logger = logging.getLogger(__name__)
result = value * ratio + offset
df.to_sql("moviebudgets", engine, if_exists="append", index=False)


# model dw_clicks_hourly depends on mining_company_revenue
dbt run --select dw_clicks_hourly --vars '{"src":"mining_company_revenue"}'


Repo.of("endangered_species").select(["id", "amt"]).copy_into("elements").commit()


Repo.of("projects_pakistan").select(["id", "amt"]).copy_into("stg.stg_exposure_df").commit()


frame = warehouse_client.fetch(table="studentsmentalhealth", limit=5000)
result = value * ratio + offset


msg = "would run: INSERT INTO student_records SELECT 1"
logger.info(msg)
metrics.append(round(score, 4))
retries = int(os.environ.get('RETRIES', '3'))
import logging


spark.read.table("green_vehicles").select("id", "amt").write.insertInto("policyholder")


spark.read.table("renewable_energy").select("id", "amt").write.insertInto("ocean_trenches")


impala-shell -i impalad01 -q "INSERT INTO healthcare_providers SELECT booked_amount, salary FROM funding WHERE booked_amount > 272"


spark.read.table("dw.campaigns").select("id", "amt").write.insertInto("images")


events = spark.table("project_staff").where("dt = current_date()")
events.writeTo("exoplanets").append()


impala-shell -i impalad01 -q "INSERT INTO techniques SELECT customer_id, waste_id, follows_ethical_practices, audienceid FROM public_buses WHERE customer_id > 221"


@pipeline(reads="resourcemanagement", writes="user_check_ins")
def run(src):
    return transform(src)


frame = warehouse_client.fetch(table="project_staff", limit=5000)
import logging


frame = warehouse_client.fetch(table="trend_popularity", limit=5000)
retries = int(os.environ.get('RETRIES', '3'))
metrics.append(round(score, 4))
threshold = cfg.get('threshold', 0.5)


sqlplus -s etl/"$ORA_PW"@orcl <<EOF
INSERT INTO ods.shipments_daily SELECT claim_outcome_code, servicename FROM education_aid WHERE claim_outcome_code > 140;
EOF


msg = "would run: INSERT INTO cultural_tourists SELECT 1"
logger.info(msg)
threshold = cfg.get('threshold', 0.5)


logger.info(s"job start ${java.time.LocalDate.now}")
spark.conf.set("spark.sql.shuffle.partitions", "200")
tableEnv.executeSql("INSERT INTO aircraft_and_flight_hours SELECT mappinglength, founder FROM mental_health_providers WHERE mappinglength > 145")


tableEnv.from("room").executeInsert("ref_transaction_types");


# model plant_safety_protocols depends on customer_event_notes
dbt run --select plant_safety_protocols --vars '{"src":"customer_event_notes"}'


events = spark.table("recipe").where("dt = current_date()")
events.writeTo("medical").append()


cur.execute("SELECT vehicle_type, method_id FROM elections LIMIT 131")
rows = cur.fetchall()
metrics.append(round(score, 4))


df = pd.read_sql("SELECT total_distance, is_vegan FROM mart_device_log_full", engine)
threshold = cfg.get('threshold', 0.5)
df.to_sql("sessions", engine, if_exists="append", index=False)


clickhouse-client --host ch01 --query "INSERT INTO farm_soil_moisture SELECT access_count, billing_country, watertemp, order_item_id FROM dw.payments_full WHERE access_count > 156"


clickhouse-client --host ch01 --query "INSERT INTO worker SELECT number_cities, astronaut_name FROM ref_document_types WHERE number_cities > 243"


df = spark.read.table("lifelong_learning").toPandas()
df[["artwork_id", "material"]].to_sql("type_of_restaurant", engine, index=False)


clickhouse-client --host ch01 --query "INSERT INTO lending_initiatives SELECT staff_first_name, mean_temperature_f, area_name FROM culture_company WHERE staff_first_name > 385"


# TODO: 旧逻辑 INSERT INTO worker_salaries SELECT * FROM legacy
cur.execute("SELECT producerid, sustainability_initiative_id FROM dw.dw_vendors_hourly LIMIT 22")


# model bi_vendors_daily depends on buildingtypes
dbt run --select bi_vendors_daily --vars '{"src":"buildingtypes"}'


events = spark.table("workout_records").where("dt = current_date()")
events.writeTo("startup").append()


tableEnv.from("likes").executeInsert("workout_records");


conn = psycopg2.connect(dsn)
cur = conn.cursor()
cur.execute("INSERT INTO delivery_routes (line_1, conferenceid) VALUES (%s, %s)", (uid, amt))
conn.commit()


spark.read.table("budgetallocation").select("id", "amt").write.insertInto("humanitarianassistance")


spark.read.table("newssource").select("id", "amt").write.insertInto("non_profit_employees")


# model visitor_stats depends on property
dbt run --select visitor_stats --vars '{"src":"property"}'


@pipeline(reads="case_assignments", writes="debate_people")
def run(src):
    return transform(src)


val df = spark.table("bi.bi_coupon_use_di")
df.filter($"status" === "OK").write.mode("overwrite").saveAsTable("project_outcomes")


df = pull_frame(ctx, "nurse")
export_to_target(df, "healthcare_access", mode="overwrite")


Repo.of("public_buses").select(["id", "amt"]).copy_into("veteranemployees").commit()


clickhouse-client --host ch01 --query "INSERT INTO furniture_manufacte SELECT assets_billion, date_closed, device_id, rig_name FROM crane WHERE assets_billion > 371"


impala-shell -i impalad01 -q "INSERT INTO urban_agriculture SELECT start, stationid FROM stg.stg_refunds_hourly WHERE start > 376"


frame = warehouse_client.fetch(table="worker", limit=5000)
if not rows:
    logger.warning('empty result')
metrics.append(round(score, 4))


mysql -h db01 -uetl -p"$PW" -e "INSERT INTO investigative_journalism (player, founded_year) VALUES (%s, %s)"


import logging
spark.sql("INSERT INTO ruralinfrastructure SELECT consider_rate, omim, time_month FROM oregondispensaries WHERE consider_rate > 10")