.jpg)
As businesses increasingly strive to make better data-driven decisions, they often find themselves looking for improved ways to store and access large volumes of data. One common data storage strategy involves using Data Lakes and Data Warehouses in tandem to manage raw and processed data.
Some new technologies exist that bridge the strengths of Data Lakes and Data Warehouses together in integrated solutions. These “Data Lake Houses,” provide the data structures and management features Data Warehouses offer, but for Data Lakes and can be more cost-effective for managing data storage.
In this post, we’ll evaluate the strengths and weaknesses of Data Lakes and Data Warehouses, introduce Data Lake Houses, and identify factors to consider when establishing your data storage strategy.
A Data Lake is a centralized location for storing, processing, and securing large amounts of structured, semi structured, and unstructured data. It can store and process data in a variety of formats, without size or volume limitations. Data Lakes are often a good place to store data until it’s ready for reporting and other uses.
Sometimes data engineers process data in a Data Lake and feed it into a data Warehouse. One example of this use scenario is storing event-level Clickstream data in a Data Lake, then aggregating weekly KPI performance at the channel level of detail for a Data Warehouse to use in reporting.
While certain risks are associated with the less structured data stored in a Lake, a solid data governance strategy will ensure that access to sensitive data is appropriately regulated (to avoid creating a data swamp).
A Data Warehouse integrates data and information from different sources on a regular schedule and stores it in one comprehensive repository of structured data. A Data Warehouse might combine customer information from an organization’s point-of-sale systems or other transaction technology, CRM, website, and customer feedback. This data is readily available for analysts to access and use to make more informed business decisions.
Risks associated with Data Warehouses include higher upfront time investments to process the data before it can be used and the added expenses of storing large amounts of data.
As new data storage technologies emerge, users can integrate their Data Lake and Data Warehouse strategies. These provide the scalability of a Data Lake and the accessibility of a Data Warehouse in one tool/product. The following bridge the two environments in a Data Lake House, which enables greater flexibility in processes and workflows:
The Evolytics Data Engineering team brings extensive experience in managing and developing data storage strategies. We develop Data Lakes and Data Warehouses solutions, and will integrate for the flexibility of a Data Lake House environment. We’ll evaluate which solutions best serve your business needs and help you get the most out of your data storage strategy.
Whether you plan to establish a new Data Lake, Warehouse, or Lake House, need to audit your existing data strategy for recommended improvements, or want to create or improve ETL or ELT processes for your data, we’re here to partner with you.
Not sure on your next step? We'd love to hear about your business challenges. No pitch. No strings attached.
