The Data Lakehouse: Bridging Information Gaps in the Enterprise
The lakehouse has huge potential for the enterprise, with the power and flexibility to handle modern analytics and enable businesses to discover new insights.
No matter what you call it — the new oil, a hot commodity, or some other buzz-worthy term — one thing is certain: data is vital. Although the way data is stored and computed has changed over the years, there are still many challenges to overcome when managing such a valuable asset.
In order to realize data’s true business value and unlock its full potential, data management needs to become a collaborative environment, and for modern analytics, it’s critical that data engineers and data scientists work closely together.
Businesses can no longer afford to operate in silos. More often than not, however, data teams tend to work in their own domains, with their own data and their own tools, creating inefficiencies that ultimately lead to information gaps within an organization.
Typically, we see data engineers house their structured data in a data warehouse, where they can use it for reporting, analytics, and business intelligence, among other functions. Meanwhile, data scientists have turned to the data lake, with its ability to combine both structured and unstructured data in its raw form, enabling data scientists to find new opportunities through deep insights, predictive analytics, and machine learning and AI pattern recognition.
A lack of collaboration between data engineers and data scientists remains one of the most critical barriers to business productivity and innovation. This division of labor duplicates effort and creates unnecessary steps that significantly delay unlocking the value in that data. For instance, data scientists often create experimental data products that have to be rebuilt by data engineers before they can be used in production.
Siloed teams working with separate data is a costly mistake that leads to information gaps that can derail business — but it is possible to bring data engineers and data scientists together thanks to the data lakehouse.
There’s been a lot of buzz about the lakehouse and for good reason. The lakehouse is a new data management paradigm that can change the way data teams work together by combining the capabilities of data warehouses and data lakes. Equipped with both the data structure and management features of a data warehouse as well as the ability to store data directly on the kind of low-cost storage used in traditional data lakes, the lakehouse presents an opportunity to unify data engineers and data scientists.
