What Is a Data Lakehouse?

As businesses generate more and more data each year, figuring out how to gain the most value from that information is a constant challenge. One survey shows that 95% of businesses polled identified a need to manage unstructured data. To do this, simple systems have evolved into data warehouses, data lakes, and now data lakehouses. But what is a data lakehouse?

This is how enterprises can manage massive volumes of data and act on it, fast. And as CIOs look to consolidate apps, streamline workflows, and become more efficient, data lakehouses, like Salesforce Data Cloud, can make a significant impact on their bottom lines.

Data architecture is evolving, and your data strategy needs to evolve with it. In a world where data drives the speed of business, a data lakehouse will help future-proof your business intelligence (BI), artificial intelligence (AI), personalization, and automation efforts. With a data lakehouse, you can become more efficient and lower costs — without sacrificing innovation.

First, let’s break down the evolution of the data lakehouse:

• A data warehouse is a repository of data, housing large amounts of information that have already been processed.
• A data lake is a pool of raw data that organizations can use and process to meet their needs — allowing for more flexibility in terms of how it’s used.
• A data lakehouse combines the best features of data warehouse and data lake technology while also overcoming their limitations. This makes it much faster and easier for businesses to extract insights from all of their data, no matter what format it is in or how large it is in volume.

Traditionally, data warehouses have been very good at applying business intelligence to structured data (such as organized content like tables of numbers). But they have required time-consuming extract, transform, and load (ETL) tools to import data from other systems of record.

Data lakes were built to capture the vast (and continually growing) wealth of unstructured data (like unorganized data like social media posts, sensor logs, and mobile coordinates) that organizations would like to use. But extracting useful insights often requires expensive data science resources, and can present security and compliance challenges.

Which brings us back to the main question: what is a data lakehouse? A data lakehouse removes the walls between lakes and warehouses — marrying the low-cost, flexible storage of a data lake with the data management, schema, and governance of a warehouse.

Some data lakehouses even benefit from a “zero-copy principle,” which allows IT teams to avoid the need for data copies and cumbersome ETL tools to improve compute performance. The end result is less time, less effort, less cost, and less latency involved in not just managing data, but quickly getting insight and value from it.

Businesses need to manage growing volumes of customer data — petabytes of data, generated across hundreds of thousands of daily interactions. It’s no wonder they have invested in a variety of solutions to keep up: 976 different applications on average, all to track customers.

But all these apps can lead to data silos across a business. We’re talking 976 versions of one customer, when only one will do.

This is exactly the challenge a data lakehouse solves, delivering the scale and flexibility CIOs need to handle all this data, with the structure and schema to keep it organized.

This isn’t empty talk, either. Data lakehouses can make a real impact on a company’s bottom line, reducing silos and increasing operational efficiency — core concerns for IT and business decision-makers. Every business is looking for ways to get their products to market faster, and deliver more value for their customers. Data lakehouses can do both.

Best of all, data lakehouses can help your business lower costs, reduce developer backlogs, and become more efficient — helping you do more with less. By separating computing and storage, they allow businesses to easily add more storage without having to augment computing power.

This is a very cost-effective way to extend analytics efforts because the expense involved in storing data remains low.

Your existing solutions can stay put. There’s no need to “rip and replace” when adopting a data lakehouse.

Thanks to their open data protocols, data lakehouses can integrate easily with legacy apps and systems, whether they’re pulling in first-party ad data, or business intelligence (BI) tools, or proprietary AI models. You can then begin to phase out obsolete data management tools that require a lot of care and feeding on your timetable.

Like any powerful technology, a data lakehouse should adapt to changes in your business requirements — not box you in.

With the right data lakehouse, businesses can drastically simplify data governance and compliance without slowing the pace of innovation. We’ve seen this as a top concern for many of today’s IT and business leaders, according to our IT & Business Alignment Barometer.

Data lakehouses can consolidate multiple systems for data management into one platform — reducing the amount of data spread across systems, and reducing the number of hands data travels through. They can allow you to exert more control over security, authorization levels, and more, thanks to the standardized open schema of lakehouses.

What does that look like in practice? CIOs and IT leaders can implement role-based access, so that marketing teams only have access to segmentation data, commerce teams only have access to order data, and more. They can also audit who’s requesting data from the lakehouse, from where, across what roles.

Imagine using data to improve operations across all areas of your business instantaneously. For example:

• Service: You can automate proactive alerts that enable every service rep (from the contact center to the field) to intervene quickly and engage with customers if there’s a supply chain delay or a part recall. This leads to quick resolutions and improved customer satisfaction.
• Sales: You can provide real-time guidance during voice and video calls with customers. Reps can tailor conversations based on other product pages the customer has browsed across your site.

What is a data lakehouse? It’s a way for you to integrate data from every step in the customer experience. It’s more agile than legacy data processing methods, allowing you to personalize how your teams access and make use of customer data.

If you’re looking for a powerful way to do more with less and improve customer relationships, a data lakehouse can help you.