Guide to Data Lakehouses

As you generate more and more data each year, figuring out how to gain the most value from it is a constant challenge. It’s important to manage this data, which has evolved from simple systems into data warehouses, data lakes, and now data lakehouses.

Data lakehouses help you manage massive volumes of data and act on it—fast. As CIOs look to consolidate apps, streamline workflows, and become more efficient, data lakehouses can make a significant impact on their bottom lines. See how you can future-proof your customer personalization and automation efforts with data lakehouses.

A data lakehouse is a modern architecture that gathers all of your organization’s unstructured, structured, and semi-structured data and stores it at a low cost—while keeping it highly accessible to users. A data lakehouse essentially takes the best features of data warehouses and data lakes, bridging the gap between structured and unstructured data in one system.

For example, imagine a marketing team analyzing campaign performance. A data lakehouse allows them to combine structured data (e.g., sales figures) with unstructured data (e.g., customer sentiment from social media) to build more personalized campaigns—all without switching between systems.

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

Let’s take a closer look at how data lakehouses use the best of data lakes and data warehouses.

A data warehouse can house large amounts of data that has already been processed. Data warehouses are very good at storing and applying business analytics to structured data (such as numbers and addresses). But they require time-consuming ETL tools to import data from other systems of record.

A data lake is a pool of raw data you can use to centrally store data in its raw form. Data lakes were built to capture the vast (and continually growing) wealth of unstructured data, such as social media posts, images and audio files. Extracting useful insights often requires data science skills.

A data lakehouse combines the best features of data warehouse and data lake technology while overcoming their limitations. Data lakehouses make it much faster and easier for you to extract insights from all of your stored data, no matter what format it is in or how large its volume. You get the low-cost, flexible storage of a data lake with the data management, schema, and governance of a data warehouse.

The volume of data businesses generate is growing at an unprecedented rate. Organizations handle petabytes of data across hundreds of systems, whether it’s customer interactions or IoT sensor data. In fact, the average business uses 976 applications to track customers, according to recent studies.

The challenge? Each of these applications creates its own siloed version of customer records, leaving businesses with fragmented insights. We’re talking 976 versions of one customer when only one will do. These silos slow decision-making, increase operational costs, and limit innovation.

By unifying structured and unstructured data in a single system, a data lakehouse offers benefits that help organizations work faster, smarter, and more efficiently.

Here’s how a data lakehouse can help your business.

• Scale and flexibility, with structure and schema: Handle growing volumes of data—both organized records and unstructured content—without the limitations of traditional systems. Organize data as needed to make it ready for analysis or AI.
• Reduce silos and improve operational efficiency: Integrate data from across hundreds of apps and systems, breaking down barriers and providing a unified view. This speeds up decision-making and eliminates duplicate efforts.
• Lower costs: Store raw and processed data affordably by separating storage and computing. Need more storage? Add it without paying for extra computing power. Need more processing? Scale it without unnecessary storage costs.
• Enable real-time insights: Get up-to-date insights without the delays caused by moving and reformatting data between systems. For example, sales reps can act on the latest customer interactions to close deals faster, and marketers can adjust campaigns on the fly.
• Support for advanced analytics, AI, and agentic AI: Use unified customer profiles to power predictions or AI-based tools—all from one system. No need for expensive engineering or extra platforms.

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, 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 strategy — not box you in.

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 the number of hands data travels through. They allow you to exert more control over security, authorization levels, and more, thanks to their open schema.

As an IT leader, you can implement role-based access, so that marketing teams only have access to segmentation data, and sales teams only have access to order data. You can also audit who’s requesting data from the lakehouse, from where, and from which functions.

A data lakehouse can make your data work smarter—no matter what industry you’re in.

• Healthcare: Bring together patient records and data from connected devices such as wearables to improve diagnostics and care. Hospitals can use AI tools to predict readmission risks and act faster to better serve patients.
• Finance: Spot fraud faster by analyzing transaction data and customer call records side by side. Banks can reduce losses by identifying suspicious activity in seconds instead of hours.
• Manufacturing: Combine sensor data from factory equipment with supply chain data to predict delays or shortages. Automakers can prevent bottlenecks and keep production running smoothly.

You have a massive volume of information, and every decision you make relies on the quality and accessibility of your data. A data lakehouse simplifies the way you store, manage, and use your data so that the right people can use it when they need it most.

The right data platform gives you the freedom to unify your data, personalize customer experiences, and inform sharp decisions. A data lakehouse is a great choice if you want to make your data work smarter for you. And because your data will continue to grow, take a look at Data 360, a hyperscale engine inside Salesforce that fuels intelligent decisions and agentic AI.