> ## Documentation Index
> Fetch the complete documentation index at: https://rajanand.org/llms.txt
> Use this file to discover all available pages before exploring further.

# Apache Hudi

## 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](/glossary/apache-hadoop) and [Apache Spark](/glossary/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](/glossary/data-lake)** with near real-time capabilities.

## 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](/glossary/incremental-processing) Processing**: Enables processing only the changed data (deltas) instead of reprocessing entire datasets.
* **[ACID](/glossary/acid-properties.mdx) 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.

## 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.

## 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.

## 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.

## 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.

## 7. **Apache Hudi vs. Traditional Data Lakes**

| **Feature**                | **Traditional Data Lakes**     | **Apache Hudi**                    |
| -------------------------- | ------------------------------ | ---------------------------------- |
| **Upserts/Deletes**        | Not natively supported         | Supported efficiently              |
| **Incremental Processing** | Requires full reprocessing     | Processes only deltas              |
| **ACID Compliance**        | Limited or absent              | Fully ACID-compliant               |
| **Query Performance**      | Slower due to lack of indexing | Faster with optimized storage      |
| **Real-Time Capabilities** | Batch-oriented                 | Supports near real-time processing |

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

| **Feature**          | **Apache Hudi**      | **Delta Lake**                 | **Apache Iceberg**     |
| -------------------- | -------------------- | ------------------------------ | ---------------------- |
| **Upserts/Deletes**  | Supported            | Supported                      | Supported              |
| **ACID Compliance**  | Yes                  | Yes                            | Yes                    |
| **Storage Format**   | Parquet + Avro logs  | Parquet + Delta logs           | Parquet                |
| **Time Travel**      | Yes                  | Yes                            | Yes                    |
| **Primary Use Case** | Real-time data lakes | Batch and streaming data lakes | Large-scale data lakes |

## 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.

## 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.