Modern data engineering requires more than just batch ingestion and static datasets. Today’s platforms need real-time updates, change data capture (CDC), and reliable transactional semantics to power machine learning, analytics, and business intelligence.

Enter Apache Hudi — a powerful data lakehouse engine that enables ACID-compliant, transactional pipelines on cloud-native storage like Amazon S3, HDFS, and Azure ADLS.

In this blog, we’ll walk through how to build transactional data pipelines using Apache Hudi, covering ingestion modes, write operations, compaction, incremental queries, and best practices for production systems.

What Makes a Pipeline Transactional?

A transactional pipeline provides:

  • Atomicity: Data is either fully written or not at all
  • Consistency: Reads see a consistent snapshot
  • Isolation: Concurrent writes don’t interfere
  • Durability: Once committed, data persists

Hudi brings these ACID guarantees to data lakes by managing file versions, write markers, and commit logs internally.

Key Features of Apache Hudi for Transactions

  • Upserts and Deletes: Update or remove records like a database
  • Merge-on-Read (MOR) and Copy-on-Write (COW) storage models
  • Instant-based timeline: Guarantees data consistency across writers
  • Compaction and clustering: Maintain performance over time
  • Incremental queries: Process only new or updated data

Choosing the Right Write Operation

Hudi supports multiple operations:

Operation Use Case
insert Append-only data
upsert New + updated data (CDC use cases)
delete Remove records by primary key
bulk_insert Fast first-time loads without merging

Example PySpark:

df.write.format("hudi") \
.option("hoodie.datasource.write.operation", "upsert") \
.option("hoodie.table.name", "users") \
.option("hoodie.datasource.write.recordkey.field", "user_id") \
.option("hoodie.datasource.write.precombine.field", "updated_at") \
.mode("append") \
.save("s3://my-data-lake/hudi/users")

Building the Transactional Pipeline

  1. Ingest Data
    From Kafka, CDC tools (e.g., Debezium), or logs into Spark/Flink/Glue.

  2. Apply Transformations
    Enrich, filter, or join with reference datasets.

  3. Write to Hudi Table
    Use upsert/delete modes with proper keys and timestamps.

  4. Sync with Hive/Glue Catalog
    Enable Hudi’s metadata sync to make tables queryable via Presto, Athena, or Hive.

  5. Query for Increments
    Downstream jobs read only changed records using incremental reads.

Enabling Incremental Queries

You can process only new data since the last commit:

spark.read.format("hudi") \
.option("hoodie.datasource.query.type", "incremental") \
.option("hoodie.datasource.read.begin.instanttime", "20240101000000") \
.load("s3://my-data-lake/hudi/users")

This drastically improves performance for ETL pipelines and CDC handling.

Compaction and Clustering

In Merge-on-Read tables, updates are logged in delta files and compacted later.

Enable inline compaction:

hoodie.compact.inline=true  
hoodie.compact.inline.max.delta.commits=5

Or run async compaction with schedulers like Airflow or AWS Glue workflows.

Clustering improves file sizing and query performance:

hoodie.clustering.inline=true  
hoodie.clustering.plan.strategy.sort.columns=updated_at

Ensuring Pipeline Reliability

  • Enable metadata table for faster file listing
  • Monitor commit timeline and rollback states
  • Use deduplicated recordkey + precombine field to resolve conflicts
  • Track ingestion using HoodieTimeline and HoodieCommitMetadata

Best Practices

  • Prefer COW for frequent reads, MOR for heavy writes
  • Align recordkey, precombine, and partitioning with data model
  • Tune file sizes and commit frequency for S3 I/O efficiency
  • Use Hive sync mode = glue for AWS-native integration
  • Schedule compaction and cleaning to avoid small file issues

Conclusion

Apache Hudi enables modern data teams to build transactional data pipelines that scale with the volume, velocity, and variety of today’s data. With support for real-time upserts, incremental processing, and time-travel capabilities, Hudi turns your data lake into a lakehouse — without compromising on ACID guarantees.

Implementing these practices will help you create reliable, efficient, and future-proof pipelines ready for production analytics and machine learning.