.png)
As organizations move toward data-driven decision making, the need for processing and analyzing real-time data in a scalable, consistent, and cost-effective way has become a central challenge. Modern data systems increasingly rely on data lakes to store vast amounts of structured, semi-structured, and unstructured data, but traditional data lakes fall short when it comes to handling fast-moving, continuously generated streaming data. Enter Apache Hudi, an open-source data lake framework designed to bring streaming, transactional capabilities to data lakes without the bloat of traditional data warehouse solutions.
In this deep-dive, we’ll explore what makes Apache Hudi a game-changer for developers and data engineers working with modern data lake architectures, how it simplifies incremental processing, enables upserts and deletes at scale, and why it is a cornerstone technology for anyone managing streaming data pipelines.
Apache Hudi, short for Hadoop Upserts Deletes and Incrementals, is a data lake storage framework that enables streaming ingestion, incremental processing, and ACID transactions in cloud-native data lakes. Created by Uber to address the limitations of existing data lake tools, Hudi brings database-like operations such as inserts, updates, deletes, and time travel directly to data lakes built on cloud object stores like Amazon S3, Google Cloud Storage, and Azure Data Lake.
Traditionally, data lakes were optimized for append-only batch jobs. Developers would run large ETL processes overnight to load fresh data, with little or no support for handling late-arriving data, partial updates, or deletions. This resulted in brittle, complex, and slow pipelines. With Apache Hudi, you get:
This allows developers to build modern data pipelines that are agile, resilient, and scalable without resorting to heavyweight, monolithic solutions.
Apache Hudi supports streaming ingestion using tools like DeltaStreamer, a lightweight ingestion service that allows you to continuously load data from various sources such as Kafka, Debezium, MySQL, or file systems. Unlike batch jobs that require manual scheduling, DeltaStreamer can operate in continuous mode, ingesting data in real time and writing it directly into Hudi-managed tables.
Developers can build end-to-end streaming data pipelines where new events are captured, transformed, and landed in data lakes with sub-minute latency. This is crucial for real-time applications such as:
The support for Change Data Capture (CDC) means that Hudi can work seamlessly with systems like Debezium, which track changes in source databases, making it a robust choice for database replication, audit trails, and data synchronization scenarios.
Apache Hudi offers two powerful table types that developers can choose based on workload needs:
This flexibility allows developers to optimize for write throughput or read performance depending on the use case. For example, a user behavior tracking system may prefer MoR to support high update frequency, while a BI dashboard may benefit from CoW for faster reads.
Apache Hudi brings ACID transaction support to cloud object storage, a feat previously reserved for databases and data warehouses. This means that all operations are atomic, consistent, isolated, and durable. When writing to a Hudi table, each commit is recorded in a timeline that guarantees isolation between reads and writes.
This is particularly helpful in environments where multiple writers and readers are active at the same time. With snapshot isolation, a query will always see a consistent view of the data, even if other updates are happening concurrently in the background.
For developers, this translates to:
In traditional pipelines, data might not be available for hours or even a day after it’s generated. Apache Hudi changes that by allowing streaming ingestion with commit-level visibility, meaning new data is queryable within seconds or minutes of arrival.
This is a boon for real-time analytics where businesses rely on up-to-date metrics, such as:
Developers can use tools like Apache Spark, Presto, Hive, or Trino to run snapshot queries that return consistent results from Hudi-managed tables, even as new data is continuously being ingested.
One of the most powerful aspects of Apache Hudi is its incremental query capability. Instead of reprocessing an entire dataset, developers can query for only the new or changed data since the last checkpoint. This reduces compute costs, shortens pipeline run times, and enhances scalability.
Imagine running daily aggregations on terabytes of logs. Instead of re-scanning the entire dataset, Apache Hudi allows you to fetch just the latest changes and process only those, dramatically increasing throughput and reducing cost.
Apache Hudi uses Avro schemas and maintains rich metadata for all files, records, and operations. This enables schema evolution without breaking downstream pipelines. Developers can:
This makes it easy to maintain data quality and consistency as applications evolve, ensuring that data lakes remain resilient to change and developer-friendly.
Hudi maintains a full commit timeline, recording every write operation to the data lake. Developers can use this timeline to:
This is essential for data governance, compliance, and debugging. A developer can inspect when a particular record was changed, what values it had previously, and who made the change, all within the data lake, using standard query engines.
To speed up queries and write operations, Hudi includes record-level indexing and partition pruning. This means developers can write upserts and deletes efficiently by avoiding full table scans. Instead, Hudi can quickly locate the file group where a record resides, minimizing I/O and latency.
In scenarios involving billions of rows, this optimization is vital for maintaining performance and responsiveness.
A Hudi table begins with defining key attributes such as:
Developers define this schema using Avro or JSON, along with configurations for cleaning, compaction, and indexing. This schema-first approach encourages clarity, consistency, and extensibility in data pipelines.
Data ingestion can be done through:
Each method gives developers fine-grained control over data transformations, validation, and output formats. You can ingest in near real-time, batch mode, or hybrid workflows depending on your operational needs.
Once data is ingested, querying becomes seamless. Developers can use:
These queries are supported by engines like Spark SQL, Trino, Hive, and Presto. You can even use AWS Athena or BigQuery with Hudi-compatible formats, making it a versatile choice for hybrid cloud ecosystems.
Apache Hudi supports lifecycle operations such as:
These operations ensure that your data lake remains fast, cost-efficient, and scalable even as the data grows.
In traditional data lake systems:
With Apache Hudi:
This shift allows developers to treat the data lake like a database, reducing complexity while increasing flexibility.
Apache Hudi is already powering production systems at scale:
These implementations validate Hudi’s maturity and versatility in the enterprise data ecosystem.
Apache Hudi allows developers to transform passive data lakes into active data platforms. With support for real-time ingestion, ACID transactions, scalable indexing, and full query support, it redefines what’s possible in open data architecture. Whether you're a data engineer, ML engineer, or analytics developer, Apache Hudi provides the building blocks to create low-latency, resilient, and auditable data pipelines that work at petabyte scale.
.png)
