Athena To Redshift

Migrating Queries from Athena to Redshift: Streamlining Your Data Warehouse Performance

Think of Athena as a quick, flexible scout for data exploration, but when it’s time to deploy the heavy artillery, Redshift takes the lead. Learn the nuanced art of migrating your analytics from Athena to Redshift to unlock superior speed and cost controls—without losing agility.

As organizations accumulate ever-larger and more complex datasets, many start their journey with Amazon Athena — a serverless, on-demand query service that lets you analyze data directly in Amazon S3 using standard SQL. It’s quick to set up, requires no infrastructure management, and fits perfectly for ad hoc queries or exploratory data analysis.

However, as query volumes increase and data workflows mature, sticking with Athena can become expensive and sometimes slower than desired because of the underlying serverless architecture. Enter Amazon Redshift—a fully managed, scalable data warehouse designed for heavy analytical workloads. Redshift offers consistent high performance, better concurrency handling, advanced optimization features, and importantly, cost control due to reserved pricing options.

If your organization is at this crossroads, migrating query workloads from Athena to Redshift is a strategic move that can deliver immense benefits—but it requires careful planning and execution. In this post, I’ll walk you through practical steps on how to make this transition seamless.

Step 1: Understand the Core Differences Between Athena and Redshift

Before diving into migration techniques, it’s vital to know where Athena and Redshift differ most:

• Data Location: Athena queries data directly in S3 in an open format (like Parquet or ORC). Redshift ingests or externalizes data but typically works best when data is loaded into its own optimized columnar storage.

• SQL Compatibility: Both use ANSI SQL dialects but have differences in function support, syntax, and extensions.

• Performance Optimization: Redshift provides clustered indexes (sort keys), distribution styles for data placement across nodes — none of which apply in Athena.

• Cost Model: Athena charges per amount of scanned data per query; Redshift uses a cluster pricing model with options for reserved nodes.

Keeping these differences in mind frames every migration decision—from schema redesigns to query rewrites.

Step 2: Analyze Your Existing Athena Queries

Inventory your most critical Athena queries. Assess:

• Query complexity: Are you using complex window functions or advanced joins?

• Data sources: Which S3 buckets/tables does each query access?

• Format & Compression: Are your S3 objects stored as Parquet/ORC? These columnar formats speed up both Athena scans and easiest ingestion into Redshift Spectrum or tables.

A practical way is exporting query history with AWS CloudTrail or using tooling like AWS Glue Data Catalog metadata scans.

Step 3: Prepare Your Data for Redshift

You have two ways to access data from Redshift:

1. Load Data into Redshift Tables: Best for repeated queries on hot datasets where performance needs are strict.
2. Use Redshift Spectrum: Query data directly on S3 from within Redshift without loading it—somewhat similar to the Athena model but integrated inside Redshift.

If you want full performance benefits, consider ETL pipelines (e.g., AWS Glue, Apache Airflow) that transform and load Parquet/CSV files into native Redshift tables with optimized sort keys and distribution styles.

Step 4: Translate Your Queries

Athena supports Presto SQL while Redshift uses a PostgreSQL-based engine with some customizations. Here are some key points:

• Replace unsupported functions with supported alternatives (e.g., string functions).

• Modify syntax differences — e.g., LIMIT clauses behave similarly but offset handling can differ.

• Review date/time functions carefully as implementations vary.

Example:

Athena Query:

SELECT user_id, session_id, count(*) AS event_count
FROM events_table
WHERE event_date = date('2024-05-10')
GROUP BY user_id, session_id
ORDER BY event_count DESC
LIMIT 10;

Redshift Equivalent:

SELECT user_id, session_id, count(*) AS event_count
FROM events_table
WHERE event_date = '2024-05-10' -- no need for date() function
GROUP BY user_id, session_id
ORDER BY event_count DESC
LIMIT 10;

Most simple queries translate easily—focus more on complex ones that leverage Presto-specific extensions or UDFs.

Step 5: Optimize Table Design in Redshift

To maximize throughput:

• Choose appropriate distribution key (DISTKEY) to collocate join keys on the same node.
• Define efficient sort keys (SORTKEY) for frequent filter columns.

For example:

CREATE TABLE events_table (
    user_id bigint,
    session_id varchar(64),
    event_date date,
    event_type varchar(32)
)
DISTKEY(user_id)
SORTKEY(event_date);

Such design drastically improves join performance and reduces shuffle costs versus simply loading flat data from S3 endlessly queried by Spectrum or external tables.

Step 6: Setup ETL Pipelines

A typical pattern looks like this:

1. Raw logs land in S3.
2. Glue Crawlers catalog metadata.
3. Glue ETL transforms raw JSON/CSV into columnar Parquet format.
4. Copy commands load Parquet files into optimized Redshift tables via COPY statements:

   COPY events_table FROM 's3://mybucket/events/parquet/'
   IAM_ROLE 'arn:aws:iam::xxxxxx:role/MyRedshiftRole'
   FORMAT AS PARQUET;
   

This loads compressed binary files much faster than CSV imports—speeds up queries significantly compared to querying raw objects in Athena.

Step 7: Test & Validate Consistency

Before switching dashboards or BI tools over fully:

• Run key reports side-by-side on both systems.
• Compare result counts and aggregates.
• Benchmark execution times.

This iterative validation ensures migration didn’t introduce discrepancies or performance degradation surprises.

Step 8 (Optional): Gradually Migrate User Workloads

Don’t do a big-bang cutover unless necessary. You can:

• Redirect specific dashboards over time.
• Use unified BI tools like Looker/QuickSight connected both to Athena and Redshift.

This helps train end users gradually on the new system’s quirks while maintaining business continuity.

Final Thoughts

Migrating from Athena to Amazon Redshift is not just about porting SQL queries—it’s about evolving your entire data ecosystem towards greater scale and efficiency. With thoughtful planning around schema design, ETL pipelines, query translation, and gradual rollout strategies, you can unlock much higher performance at predictable costs—all while ensuring analysts continue getting insights without interruption.

If you’re starting this journey soon:

• Audit all critical queries first.
• Invest time learning Redshift’s distribution/sort key concepts early.
• Automate ETL workflows where possible using Glue or Apache Airflow integrations.

Embrace this shift as a natural progression from exploration (Athena) to enterprise-grade analytics power (Redshift). Your future self—and your CFO—will thank you!

Have you migrated workloads from Athena to Redshift? Drop your tips or questions in the comments—I’m keen to hear about your experiences!