What do you do if your Data Warehousing performance needs a boost?

To kick things off, looking at how our data is organized is crucial. A well-thought-out data model makes everything smoother, from how fast we can pull up information to keeping our storage costs down. We need to make sure our setup is clean, follows a logical structure like dimensional modeling, and uses the right kind of indexes. If the foundation isn't solid, we're going to run into all sorts of speed bumps down the line.

Define the business needs. Choose the right model and schema, a star schema can be considered as suitable, but it depends on the business needs. Delimit your data sources. Include only the necessary dimensions. Clean the data. Minimize the number of joins in the queries. Optimize your queries to reduce execution time and improve performance.

If your Data Warehousing is slow, there are a few things you can do to make it faster. First, make sure your data is organized and indexed efficiently. Then you can also streamline your data processing steps and look for ways to store data more effectively. Lastly, consider using techniques like caching or in-memory databases to speed up access to your data.

If you run cloud DWH service, like Snowflake, Redshift, BigQuery or Synapse, you probably don’t have primarily problem with data model per say. If your query was tuned for some use case and worked well, scaling up in cloud to get better performance with increased volume of data perhaps is the best approach how to get faster results. The more subtle and profound problems come with changing parameters of the use case beeing solved. So the root cause is hidden in too rigid model tailored to a specific query optimization or execution. You must decide before designing a model what is your preferred trade-off option: resilient or optimized-to-a-query model. With that choose appropriate technique ranging from Data Vault to indexed flat table.

Review what is slow - is it some specific jobs, report, batch, adhoc query or is it an issue of scale - it slows with increasing loads. If the former, identify the cause of slowness, and if the source is a single data platform, work on optimizing. Your source may not be a single platform. If it is a scale issue, think if the data structure is optimum before adding resources as often, more resource - cpu/mem/disk will hide your design inefficiencies

In short, an optimized ETL process leads to: Faster data processing Better data quality Increased scalability Enhanced data governance Empowered business intelligence

Remember - the queries you originally implemented may not be efficient enough anymore as the complexity and scope of data increases in every solution over time. Check how you are joining and consider making new mapping tables if you are dealing with complex m:n joins or with repeating joins to one object.

Optimizing SQL queries in Data Warehouse is crucial for several reasons: Improved performance Increased productivity Scalability Cost savings Better user experience Improved data reliability

Importance of updated hardware and software for data warehouse: Improved performance: Faster data processing and querying. Increased scalability: Handle larger data volumes and workloads. Enhanced security: Protect data from cyber threats and vulnerabilities. Better reliability: Reduce downtime and data loss risks. Support for new technologies: Utilize latest data analytics tools and techniques. Investing in updated hardware and software ensures your data warehouse remains efficient, secure, and scalable, meeting your evolving business needs.