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 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:
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.
