Delta Lake or Hive on AWS – Making an Informed Decision

Amazon S3 has become the de facto storage layer for big data analytics on AWS. The challenge is deciding how to layer a query engine and metadata structure on top of that data to turn it into a usable data warehouse. Two popular choices are Delta Lake and Hive running on AWS EMR.

Both approaches query data stored in S3, but they differ in transaction guarantees, schema handling, performance optimizations, and ecosystem fit.

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Delta Lake on AWS S3

Delta Lake is an open-source storage format that adds ACID transactions and schema evolution to data lakes. On AWS, it is often paired with Apache Spark running on EMR to process large datasets stored in S3.

How it works on AWS:

1. Data Storage: Raw or processed files (Parquet, JSON, CSV) are stored in an S3 bucket. Example:

   s3://my-data-lake/sales/2024/10/part-0001.snappy.parquet
   

2. Delta Format: Delta Lake maintains a transaction log (_delta_log) in the same S3 path to track schema changes, data additions, and deletions.

3. Query Execution: Spark on EMR reads Delta tables directly from S3, applying schema enforcement, compaction, and time travel queries.

Advantages:

• ACID Transactions: Guarantees consistency for concurrent reads/writes.
• Schema Evolution: Supports adding or modifying columns without rewriting entire datasets.
• Time Travel: Query historical snapshots of data.
• Performance Optimizations: Data skipping and compaction reduce scan times.

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Hive on AWS EMR with S3 as the Data Warehouse

Hive is a mature data warehouse framework designed for batch analytics at scale. On AWS, Hive can run on EMR, with S3 serving as the underlying storage layer.

How it works on AWS:

1. Data Storage: Tables are stored as files in S3, typically in Parquet or ORC format. Example:

   s3://my-hive-warehouse/sales/year=2024/month=10/day=15/part-0001.orc
   

2. Metadata in Glue Data Catalog: AWS Glue stores table schemas and partition information, which Hive uses to plan queries.

3. Query Execution: Hive on EMR runs HQL (Hive Query Language) against S3 data. Execution can be done in batch (MapReduce or Tez) or via interactive engines like Hive LLAP.

Advantages:

• SQL Familiarity: HQL is close to ANSI SQL, making it accessible to analysts.
• AWS Integration: Directly integrates with S3, AWS Glue, Athena, and Redshift Spectrum.
• Partitioning: Efficiently queries large datasets via partition pruning.
• Cost Efficiency: EMR pricing is per-second for cluster runtime, and data stays in S3.

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Example: Saving Data to S3 for Hive Queries

If you have a Spark job writing to S3 for Hive consumption:

# PySpark example
df.write \
    .mode("overwrite") \
    .format("parquet") \
    .partitionBy("year", "month", "day") \
    .save("s3://my-hive-warehouse/sales")

Register the table in Hive (via AWS Glue):

CREATE EXTERNAL TABLE sales (
    order_id STRING,
    amount DECIMAL(10,2),
    customer_id STRING
)
PARTITIONED BY (year INT, month INT, day INT)
STORED AS PARQUET
LOCATION 's3://my-hive-warehouse/sales';

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Choosing Between Delta Lake and Hive on AWS

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Recommendations

• Choose Delta Lake if you need streaming + batch in the same pipeline, frequent schema changes, or ACID guarantees on S3.
• Choose Hive on EMR if you have large-scale batch analytics and want deep integration with AWS Glue, Athena, and other AWS services, while keeping costs predictable.

Both options let you store raw and processed data in S3 and query it directly, but they fit different operational and analytical patterns.

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