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1. What is Apache Hudi?

Apache Hudi (Hadoop Upserts, Deletes, and Incrementals) is an open-source data management framework designed to simplify incremental data processing on big data platforms like Apache Hadoop and Apache Spark. It provides mechanisms for efficiently managing large datasets, enabling upserts (update/insert), deletes, and incremental data processing. Hudi is particularly useful for building data lakes with near real-time capabilities.

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2. Key Features of Apache Hudi

  • Upserts and Deletes: Allows efficient updates and deletes on large datasets, which are traditionally challenging in big data systems.
  • Incremental Processing: Enables processing only the changed data (deltas) instead of reprocessing entire datasets.
  • ACID Compliance: Ensures atomicity, consistency, isolation, and durability for transactions on big data.
  • Schema Evolution: Supports evolving schemas over time without disrupting data pipelines.
  • Optimized Storage: Uses columnar storage formats like Parquet and Avro for efficient querying and storage.
  • Time Travel: Allows querying data as it existed at a specific point in time.
  • Integration: Works seamlessly with Apache Spark, Apache Flink, and other big data tools.

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3. Core Concepts in Apache Hudi

  • Table Types:
    • Copy-on-Write (CoW): Updates are written to new files, ensuring consistent snapshots. Best for read-heavy workloads.
    • Merge-on-Read (MoR): Updates are written to log files and merged during reads. Best for write-heavy workloads.
  • File Layout:
    • Base Files: Columnar files (e.g., Parquet) storing the bulk of the data.
    • Log Files: Store incremental changes (e.g., updates, deletes) in Avro format.
  • Timeline: Tracks all changes to the dataset over time, enabling features like time travel.
  • Indexing: Maintains an index to quickly locate records for upserts and deletes.
  • Compaction: Merges log files with base files to optimize storage and query performance.

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4. How Apache Hudi Works

  1. Ingestion: Data is ingested into Hudi tables, which can be stored in HDFS, S3, or other storage systems.
  2. Upserts/Deletes: Hudi uses indexing to efficiently apply updates and deletes to the dataset.
  3. Storage: Data is stored in base files (Parquet) and log files (Avro) for efficient querying and updates.
  4. Querying: Users can query the latest snapshot of the data or perform time-travel queries.
  5. Compaction: Periodically merges log files with base files to optimize storage and performance.

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5. Benefits of Apache Hudi

  • Efficient Data Management: Simplifies upserts, deletes, and incremental processing on large datasets.
  • Real-Time Capabilities: Enables near real-time data ingestion and processing.
  • Cost Savings: Reduces storage and compute costs by processing only incremental changes.
  • Improved Query Performance: Optimizes storage and indexing for faster queries.
  • Data Consistency: Ensures ACID compliance for reliable data operations.
  • Flexibility: Supports both batch and streaming data processing.

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6. Use Cases for Apache Hudi

  • Data Lakes: Building efficient and scalable data lakes with support for updates and deletes.
  • Change Data Capture (CDC): Capturing and processing changes from transactional databases.
  • Real-Time Analytics: Enabling near real-time analytics on large datasets.
  • Data Archiving: Managing historical data with time-travel capabilities.
  • Machine Learning: Providing consistent and up-to-date datasets for training ML models.

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7. Apache Hudi vs. Traditional Data Lakes

FeatureTraditional Data LakesApache Hudi
Upserts/DeletesNot natively supportedSupported efficiently
Incremental ProcessingRequires full reprocessingProcesses only deltas
ACID ComplianceLimited or absentFully ACID-compliant
Query PerformanceSlower due to lack of indexingFaster with optimized storage
Real-Time CapabilitiesBatch-orientedSupports near real-time processing

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8. Apache Hudi vs. Delta Lake vs. Apache Iceberg

FeatureApache HudiDelta LakeApache Iceberg
Upserts/DeletesSupportedSupportedSupported
ACID ComplianceYesYesYes
Storage FormatParquet + Avro logsParquet + Delta logsParquet
Time TravelYesYesYes
Primary Use CaseReal-time data lakesBatch and streaming data lakesLarge-scale data lakes

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9. Getting Started with Apache Hudi

  1. Setup:
    • Install Apache Hudi using Maven or Spark packages.
    • Configure Hudi with your storage system (e.g., HDFS, S3).
  2. Create a Hudi Table: Use Spark or Flink to create a Hudi table with a specified schema.
  3. Ingest Data: Insert, update, or delete records using Hudi APIs.
  4. Query Data: Use Spark SQL, Hive, or Presto to query Hudi tables.
  5. Optimize: Schedule compaction and cleaning tasks to optimize storage and performance.

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10. Key Takeaways

  • Apache Hudi: A framework for efficient data management on big data platforms.
  • Key Features: Upserts, deletes, incremental processing, ACID compliance, and time travel.
  • Core Concepts: Copy-on-Write, Merge-on-Read, base files, log files, and indexing.
  • Benefits: Efficient data management, real-time capabilities, cost savings, and improved query performance.
  • Use Cases: Data lakes, CDC, real-time analytics, data archiving, and machine learning.
  • Comparison: Outperforms traditional data lakes and complements Delta Lake and Iceberg.
  • Getting Started: Setup, create tables, ingest data, query, and optimize.