But what is a data lakehouse and why should we develop one? This creates cost-effective scaling with the simple use of low-cost data storage.First we had data warehouses, then came data lakes, and now the new kid on the block is the data lakehouse.

Increased cost-effectiveness: Data lakehouses are built with modern infrastructure that separates compute and storage, which allows for easy addition of storage without the need to augment compute power.

Today, open standards for schema exist for many types of data, and data lakehouses take advantage of that by ingesting multiple data sources with overlapping standardized schema to simplify processes. Simplified standards: Data warehouses originated in the 1980s, when connectivity was extremely limited, meaning localized schema standards were often created within organizations, even departments.Better data governance: Data lakehouses simplify and improve governance by consolidating resources and data sources, and are built with standardized open scheme, which allows for greater control over security, metrics, role-based access, and other critical management elements.Less administration: By using a data lakehouse, any sources connected to it can have their data accessible and consolidated for usage, as opposed to extracting it from raw data and preparing to work within a data warehouse.This integration creates a much more efficient end-to-end process over curated data sources. A data lakehouse can take the place of individual solutions by breaking down the silo walls between multiple repositories. From business reporting to data science teams to analytics tools, the inherent qualities of a data lakehouse can support different workloads within an organization.Īdvantages of a data lakehouse: A modern data platformīy building a data lakehouse, organizations can streamline their overall data management process with a unified modern data platform. Diverse workloadsīecause a data lakehouse integrates the features of both a data warehouse and a data lake, it is an ideal solution for a number of different workloads. As the world becomes more integrated with Internet of Things devices, real-time support is becoming increasingly important. A data lakehouse is built to better support this type of real-time ingestion compared to a standard data warehouse. Many data sources use real-time streaming directly from devices. The ability to separate compute from storage resources makes it easy to scale storage as necessary. Using open and standardized storage formats means that data from curated data sources have a significant head start in being able to work together and be ready for analytics or reporting.

These are brought into a data lakehouse as a means of rapidly preparing data, allowing data from curated sources to naturally work together and be prepared for further analytics and business intelligence (BI) tools. Data management featuresĪ data warehouse typically offers data management features such as data cleansing, ETL, and schema enforcement. A data lakehouse offers many pieces that are familiar from historical data lake and data warehouse concepts, but in a way that merges them into something new and more effective for today’s digital world. With an understanding of a data lakehouse’s general concept, let’s look a little deeper at the specific elements involved. In a way, data lakehouses are data warehouses-which conceptually originated in the early 1980s-rebooted for our modern data-driven world. By providing the space to collect from curated data sources while using tools and features that prepare the data for business use, a data lakehouse accelerates processes.

The result creates a data repository that integrates the affordable, unstructured collection of data lakes and the robust preparedness of a data warehouse.

This means data can be easily moved between the low-cost and flexible storage of a data lake over to a data warehouse and vice versa, providing easy access to a data warehouse’s management tools for implementing schema and governance, often powered by machine learning and artificial intelligence for data cleansing. So, how does a data lakehouse combine these two ideas? In general, a data lakehouse removes the silo walls between a data lake and a data warehouse. A data warehouse typically includes data management features such as data cleansing and extract/load/transform (ETL). This data is typically queried by business users, who use the prepared data in analytics tools for reporting and projections. Data warehouse (the “house” in lakehouse): A data warehouse is a different kind of storage repository from a data lake in that a data warehouse stores processed and structured data, curated for a specific purpose, and stored in a specified format.

Data lakehouse vs data lake