Apache Hudi Description
Hudi serves as a robust platform for constructing streaming data lakes equipped with incremental data pipelines, all while utilizing a self-managing database layer that is finely tuned for lake engines and conventional batch processing. It effectively keeps a timeline of every action taken on the table at various moments, enabling immediate views of the data while also facilitating the efficient retrieval of records in the order they were received. Each Hudi instant is composed of several essential components, allowing for streamlined operations. The platform excels in performing efficient upserts by consistently linking a specific hoodie key to a corresponding file ID through an indexing system. This relationship between record key and file group or file ID remains constant once the initial version of a record is written to a file, ensuring stability in data management. Consequently, the designated file group encompasses all iterations of a collection of records, allowing for seamless data versioning and retrieval. This design enhances both the reliability and efficiency of data operations within the Hudi ecosystem.
Apache Hudi Alternatives
Google Cloud BigQuery
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Gemini within BigQuery brings AI-powered tools that enhance collaboration and productivity, such as code recommendations, visual data preparation, and intelligent suggestions aimed at improving efficiency and lowering costs. The platform offers an all-in-one environment with SQL, a notebook, and a natural language-based canvas interface, catering to data professionals of all skill levels. This cohesive workspace simplifies the entire analytics journey, enabling teams to work faster and more efficiently.
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AnalyticsCreator
Accelerate your data journey with AnalyticsCreator. Automate the design, development, and deployment of modern data architectures, including dimensional models, data marts, and data vaults or a combination of modeling techniques.
Seamlessly integrate with leading platforms like Microsoft Fabric, Power BI, Snowflake, Tableau, and Azure Synapse and more.
Experience streamlined development with automated documentation, lineage tracking, and schema evolution. Our intelligent metadata engine empowers rapid prototyping and deployment of analytics and data solutions.
Reduce time-consuming manual tasks, allowing you to focus on data-driven insights and business outcomes. AnalyticsCreator supports agile methodologies and modern data engineering workflows, including CI/CD.
Let AnalyticsCreator handle the complexities of data modeling and transformation, enabling you to unlock the full potential of your data
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Improvado
Improvado, an ETL solution, facilitates data pipeline automation for marketing departments without any technical skills. This platform supports marketers in making data-driven, informed decisions. It provides a comprehensive solution for integrating marketing data across an organization.
Improvado extracts data form a marketing data source, normalizes it and seamlessly loads it into a marketing dashboard. It currently has over 200 pre-built connectors. On request, the Improvado team will create new connectors for clients.
Improvado allows marketers to consolidate all their marketing data in one place, gain better insight into their performance across channels, analyze attribution models, and obtain accurate ROMI data.
Companies such as Asus, BayCare and Monster Energy use Improvado to mark their markes.
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Apache Iceberg
Iceberg is an advanced format designed for managing extensive analytical tables efficiently. It combines the dependability and ease of SQL tables with the capabilities required for big data, enabling multiple engines such as Spark, Trino, Flink, Presto, Hive, and Impala to access and manipulate the same tables concurrently without issues. The format allows for versatile SQL operations to incorporate new data, modify existing records, and execute precise deletions. Additionally, Iceberg can optimize read performance by eagerly rewriting data files or utilize delete deltas to facilitate quicker updates. It also streamlines the complex and often error-prone process of generating partition values for table rows while automatically bypassing unnecessary partitions and files. Fast queries do not require extra filtering, and the structure of the table can be adjusted dynamically as data and query patterns evolve, ensuring efficiency and adaptability in data management. This adaptability makes Iceberg an essential tool in modern data workflows.
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Integrations
Company Details
Company:
Apache Corporation
Year Founded:
1954
Headquarters:
United States
Website:
hudi.apache.org
Media
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Product Details
Platforms
Web-Based
Types of Training
Training Docs
Customer Support
Online Support
Apache Hudi Features and Options
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