The New Data Lakehouse: An Overdue Paradigm Shift for Data
Fundamental changes in the way we work with data come along very rarely. For example, the database model that has remained the industry standard for decades—the relational database—was first conceived in 1970. While there have been many database-related innovations over the years, the same old data paradigm has been shoehorned into a modern context that looks very different from yesteryear. Changes to data storage and computation have expanded what data teams can accomplish, but without a paradigm shift, the data world is left with the same core challenges.
The different arms of data teams are accustomed to working separately in their own domains, with their own data and their own tools. But this creates inefficiencies that ultimately lead to information gaps within an organization. Businesses can no longer afford to operate in these silos. They now realize the critical role data can play, and obtaining and leveraging the data generated across the entire business requires these information gaps to be minimal.
In order to realize the full business value of data and unlock its potential, data management needs to become a collaborative environment. A complete culture change driven by new technology can transform businesses—bringing together data engineers, data scientists, business analysts, and anyone else who depends on quality data, to work together to reduce costs, drive innovation, and shorten time to market. This transformation requires breaking down barriers between data teams and centering the data challenges and tools of today. And this paradigm shift is already underway.
Enter the data lakehouse. There’s been a lot of hype recently around the concept of a data lakehouse, and for good reason. Essentially, it is a new data management paradigm that combines the capabilities of data warehouses and data lakes, changing the way data teams operate together. This new architecture represents a significant fundamental shift in the way we work with data.
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. This new paradigm will move organizations into the future by solving some of those core challenges remaining from holding too tightly to the status quo. What’s needed is the ability to prepare data, derive insights and make transformative decisions in a timely manner; equipping engineers, data scientists, and business users with quality data that can be easily accessed; and bringing different types of data workers together by providing a truly collaborative environment in which data culture can flourish.
One of the most pervasive of the challenges not yet solved by older paradigms is eliminating silos and bringing different types of data workers together in a collaborative environment to build a thriving data culture. This pain point may get less attention than things processes such data quality and preparation, but it is perhaps the most important to the foundation of modern analytics.
For successful analytics in a modern context, it’s critical for data engineers and data scientists to be on the same page. But until the recent introduction of the lakehouse, data teams worked in their own domains, with their own data. Data engineers played mostly in data warehouses, where their structured data lived and could be used for reporting, analytics, and business intelligence. Data scientists preferred the data lake for its ability to combine both structured and unstructured data in its raw form, where it could be used to find new opportunities through deep insights, predictive analytics, and machine learning and AI pattern recognition.
This lack of collaboration between data engineers and data scientists is a critical barrier to business productivity and innovation.
