Why Data Lakes Like Apache Hudi and Iceberg Are Gaining Popularity

In the evolving landscape of big data, traditional data warehouses are increasingly giving way to modern data lakes that offer greater flexibility, scalability, and cost-effectiveness. Technologies like Apache Hudi, Apache Iceberg, and Delta Lake are rapidly gaining traction as they address long-standing challenges in managing large-scale, real-time, and transactional data.

But what exactly makes these new-age data lakes so popular? Let’s explore the key reasons behind their growing adoption.

1. ACID Transactions on Data Lakes

One of the biggest drawbacks of traditional data lakes (built on Apache Hadoop or cloud storage like S3, ADLS, and GCS) was the lack of ACID (Atomicity, Consistency, Isolation, Durability) transactions. This made data consistency a challenge, especially for real-time updates and deletes.

Hudi, Iceberg, and Delta Lake introduce ACID guarantees, ensuring that data modifications (like inserts, updates, and deletes) can happen safely and reliably within data lakes. This is crucial for use cases like:

• Real-time data ingestion

• GDPR compliance (handling data deletions)

• Change data capture (CDC)

2. Efficient Data Management: Upserts & Deletes

Traditional data lakes mostly supported append-only writes, making it hard to modify existing data efficiently. This led to performance bottlenecks and increased storage costs due to duplicate data.

Hudi, Iceberg, and Delta Lake provide record-level operations like upserts and deletes, allowing incremental updates instead of rewriting entire partitions. For example:

• Hudi’s Merge-on-Read (MoR) enables near real-time updates without rewriting entire files.

• Iceberg’s hidden partitioning prevents expensive metadata lookups when querying updated data.

This drastically improves performance while reducing storage footprint.

3. Better Query Performance & Schema Evolution

One of the main reasons for the shift towards these modern data lakes is query efficiency. Unlike traditional data lakes that rely on Hive Metastore for metadata, Hudi and Iceberg offer optimized metadata handling.

For instance:

• Iceberg's metadata is stored in a single place (instead of relying on multiple partition files like Hive), leading to faster query performance.

• Hudi supports column pruning and indexing, significantly improving read speeds.

• Schema Evolution allows users to modify table structures (adding/removing columns) without breaking existing queries.

These capabilities make them ideal for high-performance analytical workloads.

4. Cost Optimization with Open Formats

Unlike proprietary formats like Apache ORC and Parquet, Hudi and Iceberg follow an open table format that integrates seamlessly with modern query engines like Apache Spark, Trino, Presto, and Flink.

Benefits of open formats:✅ No vendor lock-in (e.g., unlike Snowflake or BigQuery)✅ Optimized file compaction reduces storage costs✅ Native support for cloud object storage (S3, GCS, ADLS)

This makes it easier for organizations to build cloud-native, cost-effective analytics architectures.

5. Seamless Integration with Streaming & Batch Workloads

Modern data platforms require both streaming and batch processing capabilities. Traditional data lakes were primarily batch-oriented, which made real-time processing difficult.

Hudi, Iceberg, and Delta Lake bridge this gap by supporting:

• Streaming ingestion from Kafka, Flink, and Spark Streaming

• Incremental processing, reducing unnecessary re-computation

• Hybrid workloads, combining real-time updates with batch analytics

This makes them perfect for use cases like fraud detection, real-time dashboards, and log analytics.

6. Adoption by Cloud Providers & Open Source Community

Cloud providers like AWS, Google Cloud, and Azure are actively supporting these technologies:

• AWS Glue and Athena support Iceberg and Hudi natively

• Google BigLake supports Iceberg and Delta Lake

• Databricks natively supports Delta Lake

Additionally, companies like Netflix, Uber, and LinkedIn have adopted these technologies at scale, proving their reliability in production environments.

Conclusion: The Future of Data Lakes

The rise of Hudi, Iceberg, and Delta Lake signals a major shift towards intelligent, cost-efficient, and high-performance data lakes. These technologies solve critical challenges in data consistency, query performance, and real-time data processing, making them the preferred choice for modern enterprises.

As organizations continue to embrace open-source, cloud-native architectures, we can expect even greater adoption of these data lake technologies in the future.

🚀 Are you considering implementing a modern data lake? Let’s discuss your use case!