Amazon Athena VS Amazon Redshift

1. Architecture & Processing Model

• Athena:

  • Serverless query service

  • Built on Presto/Trino

  • No infrastructure to manage

  • Pay per query/data scanned

• Redshift:

  • Provisioned data warehouse cluster

  • Requires cluster management

  • Dedicated computing resources

  • Pay for running clusters

1. Data Storage

• Athena:

  • Queries data directly from S3

  • No data loading required

  • Data remains in S3

• Redshift:

  • Stores data on managed storage

  • Requires data loading

  • Data copied into Redshift storage

1. Use Cases

• Athena:

  • Ad-hoc queries

  • Sporadic/intermittent analysis

  • Quick data exploration

  • Simple ETL jobs

• Redshift:

  • Complex queries

  • Regular reporting

  • Heavy data transformation

  • Business intelligence applications

  • Enterprise data warehousing

1. Performance

• Athena:

  • Better for occasional queries

  • Performance depends on S3 data organization

  • Columnar formats (Parquet/ORC) improve speed

• Redshift:

  • Better for frequent, complex queries

  • Optimized for high performance

  • Uses distribution keys and sorting keys

  • Maintains statistics and indexes

1. Cost Model

• Athena:

  • Pay per TB of data scanned

  • No upfront costs

  • Cost increases with query complexity and data size

• Redshift:

  • Pay for compute nodes hourly

  • Reserved instances available

  • Predictable costs for consistent workloads

1. Query Optimization

• Athena:

  • Limited optimization options

  • Relies on data organization in S3

  • Partitioning and file format choices

• Redshift:

  • Advanced optimization features

  • Distribution styles

  • Sort keys

  • Compression encoding

  • Vacuum operations

1. Concurrency

• Athena:

  • Limited concurrent queries

  • Best for lower concurrency needs

• Redshift:

  • High concurrency capability

  • Workload Management (WLM)

  • Concurrency scaling

1. Data Freshness

• Athena:

  • Real-time access to S3 data

  • No data loading lag

• Redshift:

  • Requires data loading

  • Potential delay in data availability

1. Integration

• Athena:

  • Native S3 integration

  • Works with AWS Glue catalog

  • Easy Lambda integration

• Redshift:

  • Broader AWS service integration

  • Redshift Spectrum for S3 data

  • Advanced ETL capabilities

Choose Athena when:

• You need ad-hoc querying

• Have irregular/unpredictable workloads

• Want minimal management overhead

• Data already exists in S3

• Cost-sensitivity to infrastructure

Choose Redshift when:

• You need complex query performance

• Have predictable, heavy workloads

• Require high concurrency

• Need advanced optimization features

• Building an enterprise data warehouse