Tech Corner June 17, 2023
The summer brings long weekends — Memorial Day, 4th of July, Labor Day — and long weekends bring trips to the beach or the lake. Who could say no to relaxing by the water on a hot, sunny day?
That’s not what a data lakehouse is.
A data lakehouse is actually a new technology architecture in the field of data processing. It attempts to combine the data management and business intelligence capabilities of traditional data warehouses with the flexibility, scale, and cost structure of data lakes.
While this technology is certainly powerful, how can you determine whether it’s a good fit for your business?
Before we pack our bags for the long weekend, let's first explain the terms data warehouse and data lake.
Real-life lakehouses are often built right on the water to maximize their exposure to the lake’s natural beauty. Similarly, data lakehouse are designed to take maximum advantage of the expansiveness and depth of the data lake.
The reason organizations use data lakes in the first place is because they can store vast quantities of structured and unstructured data at low cost. The lakehouse preserves this benefit while also enabling data warehousing techniques. This is possible thanks to the inclusion of a metadata layer.
Essentially, the metadata layer is a separate database which contains information about every object in a data lake. While the lake itself contains data in a variety of incompatible or nonuniform formats, the metadata layer provides the necessary information to treat this data as if it were uniform by providing additional context to every piece of data.
You can think of the metadata layer as a labeling system for all the data hiding beneath the surface of your lake. And once the data is labeled, it becomes just as actionable as the structured, carefully-organized data in your data warehouse.
What kind of advanced features does having a metadata layer unlock? To name just a few:
These capabilities are more powerful when applied to a data lakehouse rather than a traditional data warehouse. This is because the lakehouse still enjoys the scale and versatility of a traditional data lake. Complex efforts that rely on large datasets — like machine learning or predictive analytics, for instance — are therefore good candidates to warrant a data lakehouse.
Let’s illustrate this point with a couple of examples from the financial services industry.
Investors can employ machine learning algorithms to analyze vast amounts of financial data and detect complex patterns. These algorithms assist in creating sophisticated investment strategies by predicting market trends, analyzing risk, identifying investment opportunities, and optimizing trade executions.
Predictive analytics allow investors to make data-driven market predictions. This involves applying statistical techniques to historical and real-time data to forecast market trends, customer behavior, and potential investment risks and opportunities.
Business Intelligence (BI) is used by investors to generate actionable insights based on data. With reliable, real-time data points, a BI system can create reports or dashboards that are ready for consumption by end users. Because there is a layer of abstraction between the database and the end product, however, it’s especially important that good data governance policies are in place to support a BI program.
These are just some of the tools at the disposal of firms with robust data operations. Keep in mind — these tools depend on the integrity and completeness of the underlying data which fuels them. For that reason, a data lakehouse may yield better ultimate results than either a data lake or a data warehouse.
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