ETL VS Data Orchestration:
Modern data management solutions

ETL VS Data Orchestration: Modern data management solutions

ETL is one of the well-known data management methodologies over the years and almost every company leverages one of ETL tool to effectively manage their data. Data orchestration is gaining popularity these days to compensate modern data requirements. We recently started working on modern technology stack like Azure Services (Azure data factory, Synapse), Apache Airflow, AWS Services (Glue, Lambda, Step functions) etc. and would like to share few insights on difference between traditional data management and modern data management.

ETL (Extract, Transform, Load):

Focuses on the specific process of data from one system to another, typically in batches.

  • Extract: Retrieves data from various sources.
  • Transform: Cleans, filters, and formats the data to fit the target system.
  • Load: Loads the transformed data into the destination (Datawarehouse, database, etc.)

Strengths:

Well-suited for structured, batch-based data processing. Easier to learn and use for simpler data pipelines.

ETL Tools:

  1. Informatica
  2. Microsoft SSIS
  3. Talend
  4. Apache Spark
  5. AWS Glue

Data Orchestration:

Acts like a conductor, managing and automating the execution of multiple data tasks and workflows.

  • Focus: Oversees the bigger picture, ensuring different data processes run smoothly and efficiently.
  • Capabilities: Handles complex data pipelines with diverse tools and systems. Can manage real-time or batch data processing. Integrates data from various sources, including structured, semi-structured, and unstructured formats.
  • Strengths: Offers greater flexibility and scalability for intricate data landscapes. Enables real-time data processing for up-to-date insights.

Famous Tools

  1. Azure Data Factory
  2. Apache Airflow
  3. Kestra
  4. Kafka

Choosing Between ETL and Data Orchestration:

  • ETL is ideal for established data warehouses with structured data. Batch-oriented data processing needs. Simpler data pipelines with minimal dependencies.
  • Data Orchestration is a better fit for complex data ecosystems with multiple tools and sources. Real-time data analytics requirements. Handling diverse data formats (structured, semi-structured, unstructured).

In essence, ETL provides the core data transformation functionality, while data orchestration manages the broader workflow and execution. They can be complementary tools, with ETL processes being orchestrated within a larger data management framework.

we would like to discuss more on this area in upcoming sessions.

#DataEngineering #DataManagement #ETL #DataProcessing #DataIntegration #BigData #Analytics





Koenraad Block

Founder @ Bridge2IT +32 471 26 11 22 | Business Analyst @ Carrefour Finance

8 个月

ETL - where raw data becomes valuable insights. Fascinating! ????

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