Glue, Athena & Redshift
AWS's core analytics pipeline services — cataloging and transforming data with Glue, querying it directly in S3 with Athena, and loading it into a data warehouse with Redshift.
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- •Explain what AWS Glue's Data Catalog and ETL jobs each do
- •Explain how Athena queries data without loading it into a database first
- •Explain when to use Athena versus Redshift for analytical queries
What is it?
AWS Glue is a serverless data integration service for discovering, cataloging, and transforming (ETL — Extract, Transform, Load) data. Amazon Athena is a serverless query service that runs standard SQL directly against data stored in S3, with no need to load it into a database first. Amazon Redshift (introduced conceptually in the Database Selection topic) is AWS's managed data warehouse for large-scale analytical queries.
Why it exists
Raw data accumulating in S3 — logs, exports, event streams — isn't directly queryable with SQL until something understands its structure and location. Glue's Data Catalog exists to be that structural map (what tables/columns exist, where the underlying files are), and Glue's ETL jobs exist to transform raw, messy data into a clean, analysis-ready format. Athena exists to let you query data sitting in S3 directly via standard SQL, without provisioning or loading it into a separate database at all — genuinely useful when you want to analyze data without committing to a full data warehouse. Redshift exists for when query performance and volume genuinely exceed what querying S3 directly (via Athena) can efficiently handle.
Problem it solves
Glue solves the data-discovery and transformation problem (a serverless, managed catalog and ETL engine instead of building and operating your own). Athena solves the 'I just want to run SQL against files in S3 without setting up a database' problem, with true pay-per-query pricing. Redshift solves the large-scale, high-performance analytical query problem once volume and query complexity genuinely exceed what direct S3 querying handles well.
Intuition
Think of raw files in S3 as an unlabeled storage unit full of boxes. Glue's Data Catalog is like creating a detailed inventory listing exactly what's in each box and where it is. Athena is like being able to search and retrieve specific items directly from the storage unit using that inventory, without first moving everything into a proper warehouse. Redshift is the proper, organized warehouse you'd actually build once you're doing this kind of retrieval so often and at such volume that searching the original storage unit directly is no longer efficient enough.
Analogy
A large public library's uncataloged donation pile (raw S3 data) versus a librarian creating a card catalog describing what's in the pile and where (Glue Data Catalog), letting visitors search and retrieve specific items directly from the pile using that catalog (Athena) — versus eventually moving the most-requested, most-organized material into the library's actual main collection with proper shelving and fast retrieval systems (Redshift) once demand justifies that investment.
Technical explanation
A Glue Crawler infers schema from source data (CSV, JSON, Parquet, and other formats) and populates the Glue Data Catalog, a Hive-metastore-compatible metadata repository that both Athena and Redshift Spectrum (Redshift's ability to query S3 data directly, similar in spirit to Athena) can reference. Athena is truly serverless and pay-per-query (billed by data scanned per query), making it ideal for ad-hoc, infrequent, or exploratory analysis where provisioning a persistent cluster wouldn't be cost-effective; query performance and cost are heavily influenced by data format and partitioning — columnar formats like Parquet and well-chosen partition keys (e.g. by date) dramatically reduce the data scanned per query compared to querying raw, unpartitioned CSV. Redshift, by contrast, requires provisioning a cluster (or using Redshift Serverless for a more elastic, pay-per-use variant) and loading data into its own optimized columnar storage, trading the setup/loading overhead for significantly better performance on complex, high-volume, repeated analytical workloads than querying S3 directly would provide.
Architecture
A company's clickstream data lands continuously in S3 as raw JSON. A scheduled Glue ETL job transforms this into partitioned Parquet files (partitioned by date) and catalogs the result in the Glue Data Catalog. Data analysts run ad-hoc exploratory Athena queries directly against this cataloged S3 data for occasional investigations, while a nightly Glue job additionally loads aggregated summary data into Redshift, which powers the company's daily business intelligence dashboards requiring fast, complex, repeated queries that would be too slow and expensive to run against raw S3 data via Athena every time.
Workflow
- Land raw data in S3 in a reasonably organized structure (e.g. partitioned by date). 2) Run a Glue Crawler to infer schema and populate the Data Catalog, or define the schema manually if the data structure is well-known upfront. 3) For ad-hoc or infrequent analytical needs, query directly via Athena against the cataloged S3 data. 4) For data transformation needs (cleaning, aggregating, reformatting), use Glue ETL jobs. 5) Once query volume, complexity, or performance requirements genuinely exceed what Athena-on-S3 handles well, load the relevant (typically aggregated/curated) data into Redshift for faster, more demanding analytical workloads.
Example
A marketing analytics team occasionally needs to answer ad-hoc questions about historical campaign data sitting in S3 — they use Athena to run a handful of exploratory SQL queries per week directly against that cataloged S3 data, paying only for the data actually scanned by each query, avoiding the cost and operational overhead of provisioning and maintaining a Redshift cluster for what's genuinely occasional, low-volume analytical need.
Real-world usage
Glue, Athena, and Redshift together form AWS's standard analytics pipeline reference architecture, extensively documented in AWS's own big-data and analytics whitepapers; Athena's serverless, pay-per-query model is commonly recommended as the starting point for analytical needs before committing to Redshift's higher setup investment, only moving to Redshift once actual query volume and performance requirements justify it.
Trade-offs
Athena's zero-provisioning, pay-per-query model is ideal for unpredictable, exploratory, or lower-volume analytical needs, but its per-query cost and relative performance become less favorable at very high, sustained query volume compared to a dedicated Redshift cluster's fixed cost handling that same volume more efficiently. The general guidance is to start with Athena for cost and simplicity, and graduate specific high-volume, performance-critical workloads to Redshift once the actual usage pattern justifies that additional investment.
Visual explanation
Picture raw data files landing in an S3 bucket. A Glue Crawler scans this data, inferring its schema (columns, types) and registering it as a table in the Glue Data Catalog — without moving or copying the underlying data at all. Athena then lets you run SQL queries directly against that cataloged S3 data, scanning the relevant files on demand per query. Separately, a Glue ETL job might transform and load a cleaned, aggregated version of this same data into Redshift for faster, more complex analytical queries at scale.
Advantages
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Athena requires zero infrastructure provisioning — query S3 data directly with standard SQL, paying only for what's actually scanned
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Glue's Data Catalog provides a single, shared metadata layer usable by multiple query engines (Athena, Redshift Spectrum, and others)
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Serverless Glue ETL jobs remove the operational burden of managing your own data transformation infrastructure
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This pipeline lets you start with the lowest-commitment option (Athena on S3) and only invest in Redshift once genuinely justified by actual scale/performance needs
Disadvantages
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Athena's pay-per-query cost can become expensive for very high query volume or poorly optimized (unpartitioned, non-columnar) data, where each query scans far more data than necessary
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Query performance on raw S3 data via Athena is generally slower than a properly tuned, purpose-built Redshift cluster for complex, high-volume analytical workloads
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Glue Crawlers can sometimes infer schema incorrectly for messy or inconsistent source data, requiring manual schema correction
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Maintaining data freshness across this pipeline (raw S3 → cataloged → transformed → loaded into Redshift) requires deliberate orchestration, not automatic
Common mistakes
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Querying large volumes of unpartitioned, row-oriented (e.g. raw CSV) data via Athena repeatedly, incurring unnecessarily high per-query scan costs that partitioning and a columnar format (Parquet) would have dramatically reduced
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Provisioning a Redshift cluster prematurely for a genuinely low-volume, occasional analytical need that Athena would have served more cost-effectively with zero infrastructure management
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Not validating a Glue Crawler's inferred schema against the actual source data, leading to incorrect query results downstream
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Treating the Glue Data Catalog as a one-time setup rather than something that needs to stay in sync as underlying data structure evolves over time
In the AWS Console
- 1
AWS Console → AWS Glue → Data Catalog → Crawlers → Create crawler
Point the crawler at your S3 data location, and it will infer schema and register the result as a table in the Glue Data Catalog.
Review the crawler's inferred schema afterward — automatic inference isn't always perfectly correct, especially for inconsistent or messy source data.
- 2
AWS Console → Athena → Query editor
Select the Glue Data Catalog database/table, and write a standard SQL query against it directly.
Check the 'Data scanned' amount shown after running a query — this directly drives Athena's per-query cost, and is a strong signal of whether your data needs better partitioning or a columnar format.
🎤 Interview questions
How does Athena let you query data without first loading it into a database? (Listen for: runs standard SQL directly against data in S3 using the Glue Data Catalog's schema metadata, scanning the relevant files on demand per query — no separate database or loading step needed.)
When would you choose Athena over Redshift for an analytical need? (Listen for: lower/unpredictable query volume, exploratory/ad-hoc analysis, wanting to avoid provisioning and managing a persistent cluster — Athena's pay-per-query model fits better there.)
Why does data format and partitioning matter so much for Athena cost and performance? (Listen for: Athena bills by data scanned per query; unpartitioned, row-oriented formats force scanning far more data than a partitioned, columnar (Parquet) format would require for the same logical query.)
What role does the Glue Data Catalog play in this analytics pipeline? (Listen for: a shared metadata layer describing schema and location of data in S3, usable by multiple query engines like Athena and Redshift Spectrum without duplicating that metadata definition.)