Tech Corner June 17, 2023

Is a data lakehouse right for your digital transformation strategy?

by Bethany Walsh

Website Data Lakehouse

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?

A house on the lake

Before we pack our bags for the long weekend, let's first explain the terms data warehouse and data lake.

  1. Data Warehouse: This is a large store of data that has been transformed for analysis. Data in a warehouse is usually structured, meaning it's organized in a specific way (e.g., in tables with rows and columns) which allows it to be used by other programs and business systems.
  2. Data Lake: This is also a large store of data, but includes data that hasn’t been transformed, enriched, or validated — and therefore may not be usable. Data lakes can hold data in any form: structured, semi-structured, or unstructured (like images, audio files, etc.). This makes the data within data lakes difficult to analyze or use.

    In summary: both data warehouses and data lakes store data, but warehouses store it in an organized, predictable way while lakes store it freely.

  3. A Data Lakehouse seeks to combine the structure and predictability of a data warehouse with the versatility and large-scale capacity of a data lake. Like a data warehouse, a lakehouse is designed to store data such that it can easily be used for a variety of purposes — like analytics, reports, or API integrations. But, similar to a data lake, a lakehouse can handle all kinds of data (including unstructured data). This makes it a versatile solution for organizations who handle data in many different, unstructured formats. Data lakes are also more cost-effective, particularly at large scale, relative to data warehouses.

How it works

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:

  • Business intelligence
  • Data sharing via API
  • ACID transaction support
  • Advanced data governance
  • Decoupled storage and compute

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.

Machine Learning

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

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

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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