![]() As a result, the vast majority of the data of most organizations is stored in cloud data lakes. AWS S3, Azure Data Lake Storage (ADLS), Google Cloud Storage (GCS). In the cloud, every major cloud provider leverages and promotes a data lake, e.g. Recently, Bill Inmon, widely considered the father of data warehousing, published a blog post on the Evolution of the Data Lakehouse explaining the unique ability of the lakehouse to manage data in an open environment while combining the data science focus of the data lake with the end-user analytics of the data warehouse.Ī data lake is a low-cost, open, durable storage system for any data type - tabular data, text, images, audio, video, JSON, and CSV. Vendors who provide Data Warehouses include, but are not limited to, Teradata, Snowflake, and Oracle. Vendors who focus on Data Lakehouses include, but are not limited to Databricks, AWS, Dremio, and Starburst. The key technologies used to implement Data Lakehouses are open source, such as Delta Lake, Hudi, and Iceberg. ![]() It also has direct file access and direct native support for Python, data science, and AI frameworks without ever forcing it through a SQL-based data warehouse. Namely, it has the SQL and performance capabilities (indexing, caching, MPP processing) to make BI work fast on data lakes. It has specific capabilities to efficiently enable both AI and BI on all the enterprise’s data at a massive scale. The Data Lakehouse enables storing all your data once in a data lake and doing AI and BI on that data directly. Historically, to accomplish both AI and BI, you would have to have multiple copies of the data and move it between data lakes and data warehouses. which of my customers will likely churn, or what coupons to offer at what time to my customers). ![]() These data warehouses primarily support BI, answering historical analytical questions about the past using SQL (e.g., what was my revenue last quarter), while the data lake stores a much larger amount of data and supports analytics using both SQL and non-SQL interfaces, including predictive analytics and AI (e.g. ![]() Separately, for Business Intelligence (BI) use cases, proprietary data warehouse systems are used on a much smaller subset of the data that is structured. These data lakes are where most data transformation and advanced analytics workloads (such as AI) run to take advantage of the full set of data in the organization. Today, the vast majority of enterprise data lands in data lakes, low-cost storage systems that can manage any type of data (structured or unstructured) and have an open interface that any processing tool can run against. In short, a Data Lakehouse is an architecture that enables efficient and secure Artificial Intelligence (AI) and Business Intelligence (BI) directly on vast amounts of data stored in Data Lakes. How does the Data Mesh relate to the Data Lakehouse? What data governance functionality do Data Lakehouse systems support?ĭoes the Data Lakehouse have to be centralized or can it be decentralized into a Data Mesh? How do Data Lakehouse systems compare in performance and cost to data warehouses? How easy is it for data analysts to use a Data Lakehouse? How is the Data Lakehouse different from a Data Lake? How is a Data Lakehouse different from a Data Warehouse?
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