Is your organization missing out on valuable insights because of limitations in your existing data warehouse? By integrating a data lake with your current data warehouse, you can unlock powerful analytics that were previously out of reach.
Modernizing your enterprise’s data management with a data lake is highly recommended due to its immense value. However, this doesn’t mean you need to replace your current data warehouse. Data warehouses are reliable assets that can be integrated into a data lake architecture to enhance their value. Today, data management environments are moving towards integrated approaches that leverage the strengths of both data warehouses and data lakes.
Adding a data lake can extend the functionality of your data warehouse while respecting the investment you’ve already made. Using both systems allows your data scientists and developers to choose the best option for storage, processing, and analytics.
Here’s an overview of common approaches to integrating a data lake with your existing data warehouse architecture:
Ingest and Process in the Data Lake
In this approach, all data is ingested and stored in the data lake, serving as the initial staging area. Cheaper computing resources then process the data, and the results can be saved to the data warehouse, while the raw data remains in the lake. This method leverages the cost-efficiency of data lakes for processing tasks.
Warehouse as a Data Source in the Data Lake
Another strategy is to process all data, including data from warehouse sources, using data lake resources. This provides a standardized interface and processing API for all data repositories in your enterprise. Your team can learn one technology for most data processing needs, using tools that run massively parallel SQL queries while integrating with advanced computation and algorithm libraries. However, it’s crucial to understand the performance trade-offs, as not all SQL queries are efficiently executed on a cluster or grid of nodes.
Data Warehouse for Reporting
Keep your data warehouse as the core platform for standard reporting. Significant testing and due diligence ensure the accuracy of the queries and computations for these reports, especially for financial or other critical information. A common practice is to store raw data in the lake, process and transform it using lake resources, and then store the refined data in the data warehouse. This way, your reports and dashboards remain unchanged and accurate.
Archive Data
Use the data lake to store archived data that once resided in the warehouse. This allows you to store data at a lower cost using commodity hardware while maintaining access to historical information.
By integrating a data lake into your existing data warehouse infrastructure, you can significantly enhance your data storage capabilities and analytics potential.
In our next and final article in the data lake series, we’ll cover best practices in data lake architecture. If you missed the first three articles, check them out:
