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Last updated on Feb 19, 2025
  1. All
  2. Engineering
  3. Data Warehousing

Your data warehouse is struggling with growing data volumes. How can you boost ETL performance?

As your data warehouse grows, efficient ETL (Extract, Transform, Load) processes are crucial to handle the increasing data volumes. Here’s how you can enhance ETL performance:

  • Optimize ETL jobs: Streamline processes by eliminating unnecessary steps and using parallel processing.

  • Use incremental data loading: Load only the data that has changed since the last update to save time and resources.

  • Invest in scalable infrastructure: Upgrade to more powerful hardware or cloud solutions to handle larger data loads efficiently.

What methods have you found effective for boosting ETL performance?

Data Warehousing Data Warehousing

Data Warehousing

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Last updated on Feb 19, 2025
  1. All
  2. Engineering
  3. Data Warehousing

Your data warehouse is struggling with growing data volumes. How can you boost ETL performance?

As your data warehouse grows, efficient ETL (Extract, Transform, Load) processes are crucial to handle the increasing data volumes. Here’s how you can enhance ETL performance:

  • Optimize ETL jobs: Streamline processes by eliminating unnecessary steps and using parallel processing.

  • Use incremental data loading: Load only the data that has changed since the last update to save time and resources.

  • Invest in scalable infrastructure: Upgrade to more powerful hardware or cloud solutions to handle larger data loads efficiently.

What methods have you found effective for boosting ETL performance?

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3 answers
  • Contributor profile photo
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    Ahmed Fakeeh

    IT Infrastructure Management | IT service management (ITIL) | Digital Transformation | Project Management | Collaboration professional ( Cisco and Avaya call managers) | Vendor Management | Scrum Master

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    - Perform Data cleansing or archiving of historical data. - Check upgrade options or move to cloud. - Use compression techniques. - Use indexing and portioning methods.

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    Nandakumar Sathuragiri

    Data Solutions Engineer | BI & ETL | Data warehouse | Snowflake | Azure certified | Salesforce Certified

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    Agree on the mentioned points. Additional points: A. Identify the step in the ETL flow which taking more time to process. B. Analyze the high resource consuming step for the actual bottleneck. C. If the table is not partitioned, based on the usability pattern partition the table. D. Better to use data compression. E. Follow life cycle policy: Data which expiring the policy, move it to archive

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    Pavan Kumar Kolukuluri

    Data & AI - SQL, PySpark, Azure and Databricks

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    Basically its a more generalized question, in this situation we need to strategize below, 1: Infra Upgrade: - Scale out the infra - Cloud Migrate to Cloud based solutions to take advantage of scalable infra and optimized performance. 2: Data Management: - Archiving and Purging historical or unused data. - Apply Data Compression Techniques 3: ETL Process validation: - Identify the Issue and streamline the ETL Pipeline process. - Optimize Data transformations using Indexing, Caching, Bucketing or Partitioning methods. 4: Regular Monitoring and Maintenance

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Data Warehousing

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