Modern Data Lakes: Comparing HDFS and Amazon S3 for Scalable Analytics

Choosing the right storage layer for your data lake has a direct impact on scalability, cost, and ecosystem compatibility. Traditionally, HDFS (Hadoop Distributed File System) has powered many on-premise and early cloud data lakes. Today, Amazon S3 has emerged as a leading alternative—offering object storage that integrates with modern analytics engines and open formats.

This article compares HDFS and Amazon S3 as data lake foundations and explains how S3 supports Parquet, Avro, Hive queries, and Delta Lake workflows out of the box.

Core Comparison: HDFS vs S3

Open File Format Support

Amazon S3 supports a wide range of formats—making it ideal for diverse analytics workloads:

• Parquet: Columnar, compressed, ideal for scans
• Avro: Row-based, schema evolution built-in
• ORC, CSV, JSON: Readable by Hive, Trino, and Athena

These files can be written using Spark, Flink, Pandas, or directly uploaded via AWS CLI or SDKs.

Querying with Hive, Athena, and Trino

To make data queryable, you can define external Hive-compatible tables referencing your S3 paths:

CREATE EXTERNAL TABLE logs (
  user_id STRING,
  event_time TIMESTAMP,
  event_type STRING
)
STORED AS PARQUET
LOCATION 's3://your-bucket/logs/';

You can manage metadata using:

• AWS Glue Catalog (Hive-compatible)
• Apache Hive Metastore
• Iceberg or Delta Lake catalog systems

Amazon Athena, Trino, and EMR Presto can query this data directly using SQL, without moving it.

Using Amazon S3 as a Delta Lake

You can configure S3 to support Delta Lake—a storage format that adds ACID transactions, versioning, and time-travel to your data lake:

1. Install Delta Lake libraries with Spark.

2. Save data to S3 paths using Delta format:

   spark.write.format("delta").save("s3a://your-bucket/my-table")
   

3. Query with:

   spark.read.format("delta").load("s3a://your-bucket/my-table")
   

4. Use AWS Glue for schema discovery, and enable S3 versioning for rollback safety.

This enables a full lakehouse architecture on S3.

Data Movement with Amazon SDKs

S3 is often referred to as Amazon S3 to emphasize its rich SDK support across:

• Python (boto3)
• Java (AWS SDK)
• JavaScript (AWS SDK v3)
• Go, Rust, C++

You can easily automate object uploads, replication, compression, and metadata tagging programmatically.

Recommended Use Cases

Final Thoughts

While HDFS remains useful for traditional Hadoop clusters, Amazon S3 offers broader compatibility, easier scaling, and better cost control—especially when paired with modern engines like Spark, Athena, or Trino.

With support for Parquet, Avro, Hive, Delta Lake, and Amazon SDKs, S3 has become the de facto foundation for modern cloud-based data lakes.

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