What is ETL? ETL Pipelines Explained for Beginners!
The Data and AI Guy
416 views
Every Monday someone pastes three exports into a spreadsheet and fixes them by hand. One scheduled script can do it before anyone logs in. That's ETL.
This is ETL (extract, transform, load) explained for beginners: what each step does, why they come in that order, and the one rule that turns a script into a pipeline you can trust, which is that it has to be safe to run twice. Extract covers full vs incremental loads, watermarks, and when you need change data capture. Transform covers deduplication, conforming types and keys, and the grain check that catches silent double-counting. Load covers append, merge (upsert) and partition overwrite, and why idempotency decides whether a retried run leaves 1,200 rows or 2,400. Then a full worked example, how cheap cloud warehouses turned ETL into ELT, and four common ETL misconceptions.
⏱️ Chapters
0:00 - The Monday spreadsheet problem
1:05 - What ETL means: extract, transform, load
1:42 - Why ETL exists, and why T comes before L
3:04 - Extract: full vs incremental and watermarks
3:57 - Extract's weak spots: deletes and boundaries
4:44 - Transform: dedupe, conform types and keys
5:44 - The grain check that catches silent bugs
6:31 - Load: append, merge, partition overwrite
7:14 - Idempotency: safe to run twice
7:55 - Worked example: one Monday, every row counted
9:07 - The retry: 2,400 rows or 1,200?
9:43 - From ETL to ELT: why the letters swapped
10:44 - Four common ETL misconceptions
11:41 - Recap and limits
🔗 Links
Airbyte: https://airbyte.com
Fivetran: https://www.fivetran.com
Debezium: https://debezium.io
dbt: https://www.getdbt.com
Apache Airflow best practices: https://airflow.apache.org/docs/apach...
PostgreSQL MERGE: https://www.postgresql.org/docs/curre...
Snowflake: https://www.snowflake.com
BigQuery: https://cloud.google.com/bigquery
Amazon Redshift: https://aws.amazon.com/redshift/
#ETL #ELT #ETLpipeline #extracttransformload #datapipeline #dataengineering #idempotency #changedatacapture #incrementalload #datawarehouse #dbt #Airflow #Airbyte #Fivetran #Snowflake
What is ETL? ETL Pipelines Explained for Beginners!
The Data and AI Guy
416 views
Every Monday someone pastes three exports into a spreadsheet and fixes them by hand. One scheduled script can do it before anyone logs in. That's ETL.
This is ETL (extract, transform, load) explained for beginners: what each step does, why they come in that order, and the one rule that turns a script into a pipeline you can trust, which is that it has to be safe to run twice. Extract covers full vs incremental loads, watermarks, and when you need change data capture. Transform covers deduplication, conforming types and keys, and the grain check that catches silent double-counting. Load covers append, merge (upsert) and partition overwrite, and why idempotency decides whether a retried run leaves 1,200 rows or 2,400. Then a full worked example, how cheap cloud warehouses turned ETL into ELT, and four common ETL misconceptions.
⏱️ Chapters
0:00 - The Monday spreadsheet problem
1:05 - What ETL means: extract, transform, load
1:42 - Why ETL exists, and why T comes before L
3:04 - Extract: full vs incremental and watermarks
3:57 - Extract's weak spots: deletes and boundaries
4:44 - Transform: dedupe, conform types and keys
5:44 - The grain check that catches silent bugs
6:31 - Load: append, merge, partition overwrite
7:14 - Idempotency: safe to run twice
7:55 - Worked example: one Monday, every row counted
9:07 - The retry: 2,400 rows or 1,200?
9:43 - From ETL to ELT: why the letters swapped
10:44 - Four common ETL misconceptions
11:41 - Recap and limits
🔗 Links
Airbyte: https://airbyte.com
Fivetran: https://www.fivetran.com
Debezium: https://debezium.io
dbt: https://www.getdbt.com
Apache Airflow best practices: https://airflow.apache.org/docs/apach...
PostgreSQL MERGE: https://www.postgresql.org/docs/curre...
Snowflake: https://www.snowflake.com
BigQuery: https://cloud.google.com/bigquery
Amazon Redshift: https://aws.amazon.com/redshift/
#ETL #ELT #ETLpipeline #extracttransformload #datapipeline #dataengineering #idempotency #changedatacapture #incrementalload #datawarehouse #dbt #Airflow #Airbyte #Fivetran #Snowflake