2. Documentation on ETL and ELT Pipelines - Part 2
Table of Contents
- Purpose of this Documentation
- Types of ETL and ELT
- ETL Pipeline with Kafka, Spark, and Airflow
- ELT Pipeline with Kafka, Data Warehouse, and DBT
- Conclusion
Purpose of this Documentation
The goal of this documentation is to help you understand ETL and ELT pipelines in a practical and memorable way. Each section uses ETL/ELT abbreviations directly with real tools (Kafka, Spark, Airflow, Snowflake, DBT) to make each step easy to remember and apply.
Types of ETL and ELT
Both ETL and ELT processes can operate in two primary modes, each suited for specific use cases:
- Batch Processing: Processes large volumes of data at set intervals. Ideal for datasets that do not need real-time updates.
- Streaming Processing (real time): Processes data in real time or near-real time, allowing for instant insights. Essential in cases where data freshness is critical, such as monitoring or alerting systems.
ETL Pipeline with Kafka, Spark, and Airflow
This section explains how to set up an ETL pipeline using Kafka, Spark, and Airflow, along with a database or data lake for storage. Here’s how each component fits into the ETL abbreviation:
| Step | Abbreviation | Tool | Purpose |
|---|---|---|---|
| Extract | E | Kafka | Kafka extract data from multiple sources (DB, API, Files, Webscraping etc) in real-time or near real-time and accept loads of different programming languages (Python, Scala, Java, Go, C++ etc) |
| Transform | T | Spark | Spark transforms data by cleaning, aggregating, and structuring it for analysis |
| Load | L | Database/Data Lake | Stores transformed data, often as raw data in data lakes for reprocessing or analysis |
| Orchestration | ETL | Airflow | orchestrate ETL/ELT pipelines for any data source or destination, managing dependencies, execution order and scheduling |
Each tool in this setup is chosen to highlight its specific role in the ETL pipeline, making it easier to associate the tools with each ETL step.
ELT Pipeline with Kafka, Data Warehouse, and DBT
In an ELT pipeline, data loading happens before transformation, leveraging the processing power of the Data Warehouse. Below is the breakdown for an ELT setup using Kafka, a data warehouse, and DBT.
| Step | Abbreviation | Tool | Purpose |
|---|---|---|---|
| Extract | E | Kafka | Kafka extract data from multiple sources (DB, API, Files, Webscraping etc) in real-time or near real-time and accept loads of different programming languages (Python, Scala, Java, Go, C++ etc) |
| Load | L | Data Warehouse (e.g., Snowflake) | Loads raw data directly into a data warehouse like Snowflake for scalable storage |
| Transform | T | DBT | DBT (Data Build Tool) transforms data within the warehouse, leveraging its computation power |
| Orchestration | ELT | Airflow | Airflow |
The use of the ETL and ELT abbreviations here provides an intuitive guide to the function of each component.
Conclusion
Choosing between ETL and ELT pipelines depends on data requirements, infrastructure, and the specific use case. ETL pipelines are traditionally better for pre-processed data, while ELT pipelines benefit from modern data warehouses' scalability and in-place processing.
Through this case study, the ETL and ELT abbreviations help structure each step, making the components memorable and their roles clear in both types of pipelines.