![]() ![]() Imagine using data to improve operations across all areas of your business instantaneously. They can also audit who’s requesting data from the lakehouse, from where, across what roles. 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 allow you to exert more control over security, authorization levels, and more, thanks to the standardized open schema of lakehouses. We’ve seen this as a top concern for many of today’s IT and business leaders, according to our IT & Business Alignment Barometer.ĭata 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. With the right data lakehouse, businesses can drastically simplify data governance and compliance without slowing the pace of innovation. Like any powerful technology, a data lakehouse should adapt to changes in your business requirements - not box you in. You can then begin to phase out obsolete data management tools that require a lot of care and feeding on your timetable. 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. There’s no need to “rip and replace” when adopting a data lakehouse. What about all my existing investments in data solutions? This is a very cost-effective way to extend analytics efforts because the expense involved in storing data remains low. By separating computing and storage, they allow businesses to easily add more storage without having to augment computing power. Data lakehouses can do both.īest of all, data lakehouses can help your business lower costs, reduce developer backlogs, and become more efficient - helping you do more with less. Every business is looking for ways to get their products to market faster, and deliver more value for their customers. 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. 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. We’re talking 976 versions of one customer, when only one will do. It’s no wonder they have invested in a variety of solutions to keep up: 976 different applications on average, all to track customers.īut all these apps can lead to data silos across a business. Sign up now Why do I need a data lakehouse now?īusinesses need to manage growing volumes of customer data - petabytes of data, generated across hundreds of thousands of daily interactions. Get articles selected just for you, in your inbox 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. 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. 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. But extracting useful insights often requires expensive data science resources, and can present security and compliance challenges. But they have required time-consuming extract, transform, and load (ETL) tools to import data from other systems of record.ĭata 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. Traditionally, data warehouses have been very good at applying business intelligence to structured data (such as organized content like tables of numbers). 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. A data lakehouse combines the best features of data warehouse and data lake technology while also overcoming their limitations.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 warehouse is a repository of data, housing large amounts of information that have already been processed.First, let’s break down the evolution of the data lakehouse: ![]()
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