The Complexity of Modern Data Environments
Most enterprises today lock away data behind multiple silos. When most people think of these silos, data marts and other old school data architecture approaches usually come to mind. But the modern cloud environment has made things much more complex.
Fractured, siloed data environments are not beneficial to any business looking to actually drive value from their data and use it to improve decision-making across the board. In order to empower employees, data must be clean, updated and accessible at all times. For some organizations – especially those with a history of data being locked away by specific departments – getting data to a useful state can be a monumental task.
While there are two common approaches to overcoming these data silos – data lakehouses and data warehouses – there has long been a debate about which is better (and why).
To investigate further, we need to start by looking at the traditional definition of each.
According to industry publication TechTarget, a data lakehouse is a data management architecture that combines the benefits of a traditional data warehouse and a data lake. It seeks to merge the ease of access and support for enterprise analytics capabilities found in data warehouses with the flexibility and relatively low cost of the data lake.
The major attribute of a data lakehouse is that it’s usually made up of unstructured data, stored in its native format, without there being a specific purpose in mind when it was stored.
On the other hand, a data warehouse is a database which is optimized for analytics, scale and ease of use. Data warehouses often contain a large amount of historical data, intended for queries and analysis.
The major difference between a data warehouse and a data lakehouse is that the data warehouse is made up of structured data; i.e., data that has already undergone a transformation process to get where it is today.
