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. Additionally, new technologies such as generative AI can effortlessly extract insights from previously untapped unstructured data - such as customer conversations on social media, vendor contracts and competitor reports, which constitute 90% of all available data. And a business that wants to stay ahead has to store and manage this considerably larger slice of relevant data effectively. To do this, the earlier simple systems have evolved into data warehouses, data lakes, and now data lakehouses. But what is a data lakehouse?

It is how enterprises can manage massive volumes of varied data and act on it, fast. And as CIOs look to consolidate apps, streamline workflows, and make their IT ecosystem more efficient, data lakehouses, like Salesforce Data Cloud, can help achieve these multiple objectives.

Data architecture is always evolving, and it acts as a catalyst for your data strategy to evolve at the same pace. In a world where data drives the speed of business, a data lakehouse will help future-proof your business intelligence (BI), artificial intelligence (AI), personalisation, and automation efforts. With a data lakehouse, you can become more efficient and lower costs — simultaneously driving innovation.

• What is a data lakehouse?
• How does a data lakehouse work?
• Why do I need a data lakehouse now?
• Do I need to replace my existing data solutions to use a data lakehouse?
• Does a data lakehouse meet security and compliance requirements?
• Summing up: Key data lakehouse features that drive business success

A data lakehouse is a centralised data store that hosts varied unstructured and structured data in a manner that is secure, compliant, and ready for reporting and analytics.

Now, let’s break down the evolution of this technology:

• 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 organisations can use and process to meet their needs — allowing for more flexibility in how it’s used.

A data lakehouse combines the best features of data warehouse and data lake technologies 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 organised 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 unorganised data like social media posts, sensor logs, and mobile coordinates) that organisations 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 of these solutions 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.

Let’s take a closer look at how this technology that unites the best of a lake and warehouse optimises data management and analytics for your business:

Like a data lake, a data lakehouse stores all structured, semi-structured, and unstructured data in the cloud. This is the storage layer which is topped by a metadata layer that offers the transactional structure and data management features usually found in data warehouses. This enables many analytical tools and applications to access and process the data straight from the lake, eliminating ETL (Extract, Transform, Load) processes to move data into a different data warehouse.

Above this, via query engines and APIs, a processing/API layer facilitates various kinds of data access, transformation, and computation. Finally, a consumption layer supports BI tools and analytics applications that empower end-users to harness the data for workflows and decision-making.

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: 897 different applications on average all to provide customers with a unified experience.

But all these apps can lead to data silos across a business. We’re talking of nearly 900 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 organised.

This technology can make a real impact on a company’s bottom line by 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, these platforms can help your business lower costs, reduce developer backlogs, and become more efficient, all by helping you access any data you need instantly in convenient, usable formats. A lakehouse also separates computing and storage, so you can 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 optimal.

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

Thanks to their open data protocols, these platforms 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 architecture, 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.

India will soon implement a new data protection law called the Digital Personal Data Protection Act (DPDP) that will make sure your customer data is safe and private. It sets clear guidelines on how to handle data, where it can be stored, and what to do in case of a breach. While the law is still being finalised, you can prepare by using tools that keep your data secure and compliant. A data lakehouse helps you do this by consolidating your data, controlling who can see what, and keeping track of all data access. This way, you can focus on growing your business while staying on top of data rules.

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, and 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 centre to the field) to intervene quickly and engage with customers anywhere in the country 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. Even while on the field, reps can tailor conversations based on other product pages the customer has browsed across your site.

A data lakehouse is a way for you to integrate customer data from every step in the customer experience. A really sophisticated one will guarantee compliance and security as you expand outside India, too.

And now, let’s take a final look at the features of this technology architecture that bring together the flexibility of a data lake and the structure of a data warehouse - to unearth hidden value from all your data:

• You can store both structured data (organised tables of inventory, sales etc.) and unstructured data (social media posts, sensor logs, mobile coordinates) in one place for unified analytics and reporting - eliminating data silos across your business.
• The Zero-Copy Principle removes the need for data copies and cumbersome ETL tools to improve compute performance. This optimises time, effort and latency in managing data.
• Add more storage as your data grows without augmenting computing power. This will keep your storage costs optimally low as you scale analytics efforts.
• Your lakehouse can integrate easily with legacy apps and systems, whether pulling first-party data, BI tools, or proprietary AI models. This way, you will continue to benefit from your existing data investments.
• Implement granular security where marketing teams access only segmentation data, commerce teams see order data, and so on - ensuring compliance with regulations globally.

Genpact leveraged these features to significantly transform its data strategy. It connected vast amounts of enterprise data from seven sources into five lakh unified customer profiles. Salesforce Data Cloud harmonises this complex data. Its zero copy capabilities seamlessly integrate data outside the CRM, such as financial information, without physically duplicating it, improving data agility while maintaining strong security. This unified data now powers Einstein AI insights that help sales and marketing teams engage customers more effectively and close deals faster.

Ready to harness your data for valuable insights? A data lakehouse can help you.