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![Example pipeline
pipeline = Pipeline(id='demo', description='A small pipeline ..’)
pipeline.add(
Task(id='ping_localhost', description='Pings localhost',
commands=[RunBash('ping -c 3 localhost')]))
sub_pipeline = Pipeline(id='sub_pipeline', description='Pings ..')
for host in ['google', 'amazon', 'facebook']:
sub_pipeline.add(
Task(id=f'ping_{host}', description=f'Pings {host}',
commands=[RunBash(f'ping -c 3 {host}.com')]))
sub_pipeline.add_dependency('ping_amazon', 'ping_facebook')
sub_pipeline.add(Task(id='ping_foo', description='Pings foo',
commands=[RunBash('ping foo')]),
upstreams=['ping_amazon'])
pipeline.add(sub_pipeline, upstreams=['ping_localhost'])
pipeline.add(Task(id=‘sleep', description='Sleeps for 2 seconds',
commands=[RunBash('sleep 2')]),
upstreams=[‘sub_pipeline’])
!5
ETL pipelines as code
@martin_loetzsch
Pipeline = list of tasks with dependencies between them. Task = list of commands](https://image.slidesharecdn.com/2018-04-13datawarehousingwithpython-180418203106/85/Data-Warehousing-with-Python-5-320.jpg)
![Target of computation
CREATE TABLE m_dim_next.region (
region_id SMALLINT PRIMARY KEY,
region_name TEXT NOT NULL UNIQUE,
country_id SMALLINT NOT NULL,
country_name TEXT NOT NULL,
_region_name TEXT NOT NULL
);
Do computation and store result in table
WITH raw_region
AS (SELECT DISTINCT
country,
region
FROM m_data.ga_session
ORDER BY country, region)
INSERT INTO m_dim_next.region
SELECT
row_number()
OVER (ORDER BY country, region ) AS region_id,
CASE WHEN (SELECT count(DISTINCT country)
FROM raw_region r2
WHERE r2.region = r1.region) > 1
THEN region || ' / ' || country
ELSE region END AS region_name,
dense_rank() OVER (ORDER BY country) AS country_id,
country AS country_name,
region AS _region_name
FROM raw_region r1;
INSERT INTO m_dim_next.region
VALUES (-1, 'Unknown', -1, 'Unknown', 'Unknown');
Speedup subsequent transformations
SELECT util.add_index(
'm_dim_next', 'region',
column_names := ARRAY ['_region_name', ‘country_name',
'region_id']);
SELECT util.add_index(
'm_dim_next', 'region',
column_names := ARRAY ['country_id', 'region_id']);
ANALYZE m_dim_next.region;
!6
PostgreSQL as a data processing engine
@martin_loetzsch
Leave data in DB, Tables as (intermediate) results of processing steps](https://image.slidesharecdn.com/2018-04-13datawarehousingwithpython-180418203106/85/Data-Warehousing-with-Python-6-320.jpg)
![Read a set of files
pipeline.add(
ParallelReadFile(
id="read_download",
description="Loads PyPI downloads from pre_downloaded csv
files",
file_pattern="*/*/*/pypi/downloads-v1.csv.gz",
read_mode=ReadMode.ONLY_NEW,
compression=Compression.GZIP,
target_table="pypi_data.download",
delimiter_char="t", skip_header=True, csv_format=True,
file_dependencies=read_download_file_dependencies,
date_regex="^(?P<year>d{4})/(?P<month>d{2})/(?
P<day>d{2})/",
partition_target_table_by_day_id=True,
timezone="UTC",
commands_before=[
ExecuteSQL(
sql_file_name="create_download_data_table.sql",
file_dependencies=read_download_file_dependencies)
]))
Split large joins into chunks
pipeline.add(
ParallelExecuteSQL(
id="transform_download",
description="Maps downloads to their dimensions",
sql_statement="SELECT
pypi_tmp.insert_download(@chunk@::SMALLINT);”,
parameter_function=
etl_tools.utils.chunk_parameter_function,
parameter_placeholders=["@chunk@"],
commands_before=[
ExecuteSQL(sql_file_name="transform_download.sql")
]),
upstreams=["preprocess_project_version",
"transform_installer"])
!8
Incremental & parallel processing
@martin_loetzsch
You can’t join all clicks with all customers at once](https://image.slidesharecdn.com/2018-04-13datawarehousingwithpython-180418203106/85/Data-Warehousing-with-Python-8-320.jpg)
Uploaded byMartin Loetzsch
Data Warehousing with Python
Dr. Martin Loetzsch discusses data warehousing with Python at Commerce 2018, emphasizing the integration of various data sources for business applications while addressing challenges like consistency, scalability, and complexity. He introduces 'mara', an open-source BI infrastructure, showcasing its capabilities for creating and managing ETL pipelines as code using Python and SQL. The presentation highlights best practices in data processing and the importance of using shell commands and parallel processing to enhance efficiency.
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Data Warehousing with Python
- 1. @martin_loetzsch Dr. Martin Loetzsch code.talkscommerce 2018 Data Warehousing with Python
- 2. All the dataof the company in one place Data is the single source of truth cleaned up & validated easy to access embedded into the organisation Integration of different domains Main challenges Consistency & correctness Changeability Complexity Transparency !2 Data warehouse = integrated data @martin_loetzsch Nowadays required for running a business application databases events csv files apis reporting crm marketing … search pricing DWH orders users products price histories emails clicks … … operation events
- 3. Avoid click-tools hard todebug hard to change hard to scale with team size/ data complexity / data volume Data pipelines as code SQL files, python & shell scripts Structure & content of data warehouse are result of running code Easy to debug & inspect Develop locally, test on staging system, then deploy to production !3 Make changing and testing things easy @martin_loetzsch Apply standard software engineering best practices Megabytes Plain scripts Petabytes Apache Airflow In between Mara
- 4. !4 Mara: the BIinfrastructure of Project A @martin_loetzsch Open source (MIT license)
- 5. Example pipeline pipeline =Pipeline(id='demo', description='A small pipeline ..’) pipeline.add( Task(id='ping_localhost', description='Pings localhost', commands=[RunBash('ping -c 3 localhost')])) sub_pipeline = Pipeline(id='sub_pipeline', description='Pings ..') for host in ['google', 'amazon', 'facebook']: sub_pipeline.add( Task(id=f'ping_{host}', description=f'Pings {host}', commands=[RunBash(f'ping -c 3 {host}.com')])) sub_pipeline.add_dependency('ping_amazon', 'ping_facebook') sub_pipeline.add(Task(id='ping_foo', description='Pings foo', commands=[RunBash('ping foo')]), upstreams=['ping_amazon']) pipeline.add(sub_pipeline, upstreams=['ping_localhost']) pipeline.add(Task(id=‘sleep', description='Sleeps for 2 seconds', commands=[RunBash('sleep 2')]), upstreams=[‘sub_pipeline’]) !5 ETL pipelines as code @martin_loetzsch Pipeline = list of tasks with dependencies between them. Task = list of commands
- 6. Target of computation CREATETABLE m_dim_next.region ( region_id SMALLINT PRIMARY KEY, region_name TEXT NOT NULL UNIQUE, country_id SMALLINT NOT NULL, country_name TEXT NOT NULL, _region_name TEXT NOT NULL ); Do computation and store result in table WITH raw_region AS (SELECT DISTINCT country, region FROM m_data.ga_session ORDER BY country, region) INSERT INTO m_dim_next.region SELECT row_number() OVER (ORDER BY country, region ) AS region_id, CASE WHEN (SELECT count(DISTINCT country) FROM raw_region r2 WHERE r2.region = r1.region) > 1 THEN region || ' / ' || country ELSE region END AS region_name, dense_rank() OVER (ORDER BY country) AS country_id, country AS country_name, region AS _region_name FROM raw_region r1; INSERT INTO m_dim_next.region VALUES (-1, 'Unknown', -1, 'Unknown', 'Unknown'); Speedup subsequent transformations SELECT util.add_index( 'm_dim_next', 'region', column_names := ARRAY ['_region_name', ‘country_name', 'region_id']); SELECT util.add_index( 'm_dim_next', 'region', column_names := ARRAY ['country_id', 'region_id']); ANALYZE m_dim_next.region; !6 PostgreSQL as a data processing engine @martin_loetzsch Leave data in DB, Tables as (intermediate) results of processing steps
- 7. Execute query ExecuteSQL(sql_file_name=“preprocess-ad.sql") cat app/data_integration/pipelines/facebook/preprocess-ad.sql | PGTZ=Europe/Berlin PGOPTIONS=—client-min-messages=warning psql --username=mloetzsch --host=localhost --echo-all -—no-psqlrc --set ON_ERROR_STOP=on kfz_dwh_etl Read file ReadFile(file_name=“country_iso_code.csv", compression=Compression.NONE, target_table="os_data.country_iso_code", mapper_script_file_name=“read-country-iso-codes.py", delimiter_char=“;") cat "dwh-data/country_iso_code.csv" | .venv/bin/python3.6 "app/data_integration/pipelines/load_data/ read-country-iso-codes.py" | PGTZ=Europe/Berlin PGOPTIONS=--client-min-messages=warning psql --username=mloetzsch --host=localhost --echo-all --no-psqlrc --set ON_ERROR_STOP=on kfz_dwh_etl --command="COPY os_data.country_iso_code FROM STDIN WITH CSV DELIMITER AS ';'" Copy from other databases Copy(sql_file_name="pdm/load-product.sql", source_db_alias=“pdm", target_table=“os_data.product", replace={"@@db@@": "K24Pdm", "@@dbschema@@": “ps", "@@client@@": "kfzteile24 GmbH"}) cat app/data_integration/pipelines/load_data/pdm/load-product.sql | sed "s/@@db@@/K24Pdm/g;s/@@dbschema@@/ps/g;s/@@client@@/ kfzteile24 GmbH/g" | sed 's/$/$/g;s/$/$/g' | (cat && echo ';') | (cat && echo '; go') | sqsh -U ***** -P ******* -S ******* -D K24Pdm -m csv | PGTZ=Europe/Berlin PGOPTIONS=--client-min-messages=warning psql --username=mloetzsch --host=localhost --echo-all --no-psqlrc --set ON_ERROR_STOP=on kfz_dwh_etl --command="COPY os_data.product FROM STDIN WITH CSV HEADER" !7 Shell commands as interface to data & DBs @martin_loetzsch Nothing is faster than a unix pipe
- 8. Read a setof files pipeline.add( ParallelReadFile( id="read_download", description="Loads PyPI downloads from pre_downloaded csv files", file_pattern="*/*/*/pypi/downloads-v1.csv.gz", read_mode=ReadMode.ONLY_NEW, compression=Compression.GZIP, target_table="pypi_data.download", delimiter_char="t", skip_header=True, csv_format=True, file_dependencies=read_download_file_dependencies, date_regex="^(?P<year>d{4})/(?P<month>d{2})/(? P<day>d{2})/", partition_target_table_by_day_id=True, timezone="UTC", commands_before=[ ExecuteSQL( sql_file_name="create_download_data_table.sql", file_dependencies=read_download_file_dependencies) ])) Split large joins into chunks pipeline.add( ParallelExecuteSQL( id="transform_download", description="Maps downloads to their dimensions", sql_statement="SELECT pypi_tmp.insert_download(@chunk@::SMALLINT);”, parameter_function= etl_tools.utils.chunk_parameter_function, parameter_placeholders=["@chunk@"], commands_before=[ ExecuteSQL(sql_file_name="transform_download.sql") ]), upstreams=["preprocess_project_version", "transform_installer"]) !8 Incremental & parallel processing @martin_loetzsch You can’t join all clicks with all customers at once
- 9. Runnable app Integrates PyPIproject download stats with Github repo events !9 Try it out: Python project stats data warehouse @martin_loetzsch https://github.com/mara/mara-example-project
- 10. !10 Refer us adata person, earn 200€ @martin_loetzsch Also analysts, developers, product managers
- 11.