ETL Data Processing

Data Processing
Updated on:
April 16, 2024

What is ETL data processing?

ETL comprises the extract, transform, and load phases that move data from source systems into a destination database or data warehouse. The extract step collects data from sources. Transform applies operations like cleansing, validation, aggregation. Load inserts the processed data into the target storage.

ETL enables consolidating data from disparate sources into a unified structure optimized for downstream uses like reporting and analytics. ETL tools and scripts automate and manage these data integration workflows.

Modern data platforms like Apache Arrow DataFusion accelerate ETL pipelines using SQL over DataFrames, optimized query execution engines, and interchangeable storage formats. Pushdown optimizations and incremental reprocessing further improve ETL efficiency at scale.

ETL is key for preparing raw data into an analysis-ready state. Cloud-native ETL simplifies building and maintaining scalable data lakes and warehouses.

What does it do/how does it work?

The extract step acquires data from sources via APIs, queries, files. Transformations filter, validate, deduplicate, merge data. Load inserts data into the destination using SQL, APIs.

Steps may run sequentially or in parallel. Batch or streaming modes are supported. ETL manages data lineage tracking, recovery, integrity checks, logging, and operationalization of the overall workflow.

Why is it important? Where is it used?

ETL is key for building analytics datasets, data warehouses, and data lakes. It collects and processes raw data into an analysis-ready form. ETL workflows integrate data from across departments and systems, standardize formats, enforce data quality, and populate central repositories.

ETL enables deriving business value from data. It is used extensively within data engineering to efficiently deliver clean, integrated data.

FAQ

What are main challenges with ETL processes?

  • Complexity of transforming large datasets
  • Maintaining workflows with evolving sources
  • Handling variety of source formats and data types
  • Ensuring data quality and integrity
  • Scaling orchestration of large flows
  • Achieving high fault tolerance and recoverability

How is ETL evolving?

  • Increased scale and complexity
  • Shift to streaming and real-time processing
  • Automation using workflow engines
  • Integration with big data technologies
  • Leveraging cloud-based services
  • Inclusion of additional steps like ML-based enrichment

When is ETL not optimal?

  • When ingesting raw data for lakehouse paradigm
  • For transient or temporary datasets
  • When source formats frequently change
  • For simple aggregation or shaping tasks

References:

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