![]() ![]() Since the preview, customers have also used Spark, Trino and Flink to process Iceberg tables and make those tables available to their BigQuery users. You can use open-source engines to process and ingest data into Iceberg tables, and BigQuery can query those tables. Unify analytics, streaming and AI use cases over a single copy of data Since its preview, many customers have started building lakehouse workloads using Apache Iceberg as their data management layer, and this support is now generally available. To help customers on this journey, we announced support for Iceberg through BigLake in October, 2022. A growing community of data engineers, customers, and industry partners are contributing, integrating, and deploying Iceberg, making it the standard for organizations building open-format lakehouses. Apache Iceberg is an open table format that provides data management capabilities for data hosted on object stores and enables organizations to run analytics and AI use cases over a single copy of data. While some organizations will build a data lakehouse, others will purchase a data lakehouse cloud service.When your data is siloed data across lakes and warehouses, it can be hard to transform outcomes with your data. This creates cost-effective scaling with the simple use of low-cost data storage. ![]() Increased cost-effectiveness: Data lakehouses are built with infrastructure that separates compute and storage, which allows for easy addition of storage without the need to augment compute power.Today, open schema standards exist for many types of data, and data lakehouses take advantage of that by ingesting multiple data sources with an 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 a standardized open schema, which allows for greater control over security, metrics, role-based access, and other crucial 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 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. ![]()
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