Lakehouse design6/7/2023 One of its most significant innovations is the use of a flexible and powerful schema evolution mechanism, which allows users to evolve table schema without rewriting existing data. Iceberg aims to provide a more robust and efficient foundation for data lake storage, addressing the limitations of existing storage solutions like Apache Hive and Apache Parquet. With its flexible storage and indexing mechanisms, Hudi supports a wide range of analytical workloads and data processing pipelines.Īpache Iceberg is an open table format for large-scale, high-performance data management, initially developed by Netflix. Hudi provides upserts and incremental processing capabilities to handle real-time data ingestion, allowing for faster data processing and improved query performance. Delta Lake has quickly gained traction in the big data community due to its compatibility with a wide range of data platforms and tools, as well as its seamless integration with the Apache Spark ecosystem.Īpache Hudi (Hadoop Upserts Deletes and Incrementals) is an open-source project developed by Uber to efficiently manage large-scale analytical datasets on Hadoop-compatible distributed storage systems. It was designed to bring ACID transactions, scalable metadata handling, and unification of batch and streaming data processing to Data Lakes. Although they share some common goals and characteristics, each solution has its unique features, strengths, and weaknesses.ĭelta Lake was created by Databricks and is built on top of Apache Spark, a popular distributed computing system for big data processing. The three Data Lakehouse solutions we will discuss in this article - Delta Lake, Apache Hudi, and Apache Iceberg - have all emerged to address the challenges of managing massive amounts of data and providing efficient query performance for big data workloads. Data Lakehouse Innovations: Exploring the Genesis and Features of Delta Lake, Apache Hudi, and Apache Iceberg We will explore the key features, strengths, and weaknesses of each solution to help you make an informed decision about the best fit for your organization's data management needs. In this article, we will focus on three popular Data Lakehouse solutions: Delta Lake, Apache Hudi, and Apache Iceberg. In the first article, we highlighted key benefits of Data Lakehouses for businesses, while the second article delved into the architectural details. Data Lakehouses have emerged as a powerful tool to help organizations harness the benefits of both Data Lakes and Data Warehouses. As data becomes increasingly important for businesses, the need for scalable, efficient, and cost-effective data storage and processing solutions is more critical than ever.
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