ETL (Extract, Transform, Load)

1. Extract

• Data Sources

  - ETL begins with data extraction from various sources such as databases, APIs, files, or cloud services.

• Data Formats

  - Data can be in different formats (e.g., CSV, JSON, XML) and needs to be accurately extracted.

• Handling Errors

  - During extraction, it's crucial to handle errors like missing data, connectivity issues, and ensure data integrity.

2. Transform

• Data Cleaning

  - This involves removing duplicates, handling missing values, and filtering out irrelevant data.

• Data Conversion

  - Convert data into a common format or structure, standardizing date formats, currencies, etc.

• Data Aggregation

  - Summarizing or grouping data to prepare it for analysis, such as calculating averages, sums, or counts.

• Data Enrichment

  - Enhancing data by adding additional information or context, such as joining with reference tables.

• Business Logic Application

  - Applying specific business rules or calculations to transform the data according to the needs of the organization.

3. Load

• Data Destination

  - Loading the transformed data into a target system, such as a data warehouse, database, or data lake.

• Data Integrity

  - Ensuring data consistency and accuracy during the load process.

• Incremental Load vs. Full Load

  - Choosing between loading only new/changed data (incremental load) or reloading all data (full load).

• Error Handling

  - Monitoring the load process for failures, retries, and managing any errors that occur.

• Performance Optimization

  - Efficiently loading large volumes of data to minimize downtime and resource usage.

© Kevin Luzbetak