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.
- By David Langton
- September 30, 2021
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.
Divided Data Teams
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.
What is a 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.
Ultimately, enterprises that successfully bridge these two worlds of data can close the information gaps in their organizations.
Data Lakehouse Advantages
Information gaps, in both data and communication, can delay an enterprise's progress and hinder data analysis. Data is critical, yes, and obtaining and leveraging the data generated across the entire business requires these gaps to be minimal. That’s where the data lakehouse architecture can come into play.
There are many reasons for -- and benefits to -- switching from a siloed, one-platform approach.
The type of data we collect is changing. Now more than ever, data teams need to work with different types of structured, semistructured, and unstructured data thanks to IoT sensors and devices and audio/video tools. Even existing data sets are different from one moment to the next with constant schema changes. The lakehouse moves with these data types and schema changes and blurs the line between structured and unstructured, allowing all raw data to be stored in one central location while maintaining a storage layer on top.
It drives innovation. Machine learning and artificial intelligence are no longer abstract ideas or sci-fi plots. The technology is here and has become a reality for many organizations due to the volume and evolution of data diversification. To keep up with the increasing demand and speed at which data needs to be analyzed, the lakehouse provides data scientists a “data playground” so they can access large quantities of structured and unstructured data and build advanced analytics models.
It accelerates time to value. Data engineers and data scientists both need increasingly faster access to shared, secure, and connected data. Aside from helping enterprises to better align with modern analytics, the lakehouse approach enables all roles of the data team to have the most complete and up-to-date data available while sharing assets from the same tools, facilitating closer collaboration and faster time to insights.
The lakehouse has huge potential for the enterprise, with the power and flexibility to handle modern analytics and enable businesses to be descriptive, predictive, and prescriptive with their insights. The path to mass adoption and success, and the path to real innovation, lies in a consistent approach to data management, and the cultivation of a true data culture for those who work and benefit from data daily. By moving all of an organization's data into a data lakehouse, your business starts with the foundation of a unified environment where the entire team can use data more efficiently and effectively, and unleash its true business value at last.
David Langton is a seasoned software professional with over 20 years of experience creating award-winning technology and products. David currently serves as the VP of Product at Matillion, a data transformation solution provider. Prior to his role at Matillion, he worked as a data warehouse manager and contractor in the financial industry.