Ask On Data

How to create a scalable and high performance Data Warehouse

How to create a scalable and high performance Data Warehouse

Creating a data warehouse is a very time consuming and complex job. In this blog we are going to cover how to create a data warehouse which is high performance and scalable despite regular increasing data size. Aside towards the end we will also mention what can be done so that you can fast track and save time in creating the data pipelines.

Below are some of the best practices that we generally follow which can help in ensure scalability and high performance even if the volume of the data grows:

  • Partitioning large tables into smaller partitions resulting in more efficient querying, maintenance and data loading operations, as only the relevant partitions need to be accessed or processed.
  • Implement indexing at db level for fast data retrieval
  • Load balancing can be implemented to distribute the load and increase processing power. This approach can be particularly effective for handling large volumes of data and computationally intensive workloads.
  • Data compression techniques can be used which can reduce the storage requirements and improve query performance by reducing the amount of data that needs to be read from disk. In some cases some of the DW appliance’s or DBs also have some inbuilt data compression techniques which can also be used.
  • Columnar storage like column-oriented databases or columnar file formats (e.g., Parquet, ORC), can significantly improve query performance.
  • Caching frequently accessed data can help in improving performance.
  • Creating pre-calculated tables (which has pre-computed query results) can significantly improve query response times, especially for complex queries.
  • Regular query optimization by reviewing it regularly, indexing, partitioning, and joins, can greatly enhance query performance as data volumes grow.
  • Proper h/w sizing such as CPU, memory, and disk I/O, can help ensure that the data warehouse can handle increasing workloads without performance degradation.
  • Having a proper Data archiving strategy in which we purge/archive less frequently accessed data, can help control data growth and ensure that the active data warehouse performance remains fine.

Tools like Ask On Data, with its simple chat interface powered by AI, can help you simply type and load the data into the data warehouse as well as do the required transformations. It can help in saving around 93% time in creating data pipelines as compared to tradition ETL tools.

If you are looking for some professional guidance you can reach out on

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