AWS Glue Guide 2026: Serverless ETL & Zero-ETL • Mr. Data Engineer

Summary#

AWS Glue is a serverless data integration service that automates discovery (Crawlers), cataloging (Metadata), and transformation (ETL). In 2026, it features Zero-ETL for real-time syncing and Glue Flex for 35% cost savings

Introduction#

Data integration is the process of discovering, preparing, moving, and combining data from many different sources into a single, unified view. AWS Glue is a serverless data integration service.

Data Integration is a complex process and requires multiple components to work together to achieve its goals. AWS Glue is aptly named as it glues together multiple AWS services to achieve serverless data integration.

Cost#

As AWS service pricing is bound to be updated, I would suggest you to check the official pricing page ↗. Generally, with Glue, you only pay for the time that your AWS Glue ETL jobs takes to run. There are no resources to manage, and no upfront costs, and you are not charged for startup or shutdown time. AWS charges an hourly rate based on the number of Data Processing Units (DPUs) used to run your AWS Glue jobs. A single DPU provides 4 vCPU and 16 GB of memory.

Glue Components#

To build an efficient, scalable data lake, you must master the four pillars of the AWS Glue ecosystem. Each component plays a distinct role in moving data from raw ingestion to actionable intelligence.

Glue Data Catalog#

The Data Catalog serves as your organization’s central metadata repository. It does not store actual data; rather, it maintains a persistent index of table definitions, schema structures, and connection settings. By acting as a universal translator, it allows decoupled services, such as Amazon Athena for SQL queries and Amazon Redshift, to instantly recognize and interact with your S3 data.

Crawlers and Classifiers#

Crawlers eliminate the manual overhead of data profiling. These specialized programs connect to various data stores (S3, RDS, or JDBC-compliant databases), scan a sample of the data, and automatically infer/classify the schema. Once the crawling is complete, the Crawler populates or updates the Data Catalog with metadata table definitions. This process is essential for Schema Evolution, ensuring that your pipelines adapt automatically when source fields change.

ETL Jobs#

ETL Jobs represent the business logic of your integration layer. Leveraging the power of Apache Spark ↗, these jobs extract data from one or more sources, apply complex transformations (such as data cleaning, joining, or masking), and load the refined output into a target destination. Glue provides flexibility here; developers can write custom PySpark or Scala scripts for granular control or utilize AWS Glue Studio for a visual, no-code approach to pipeline construction.

Triggers#

Triggers provide the heartbeat of your automation strategy. Rather than manual execution, Triggers initiate job runs based on three primary criteria: Schedules (cron-based intervals), On-Demand (manual calls), or Events. In a modern event-driven architecture, a trigger might launch a transformation job the moment a new file lands in an S3 bucket, ensuring your data lake is updated in near real-time.

Glue Features#

Data Discovery & Metadata Management#

AWS Glue Data Catalog: A centralized, Hive-compatible metadata repository that acts as a universal index for data across S3, RDS, and on-premises sources.
Automated Crawlers: Scouts that scan data stores to automatically infer schemas and update the Catalog.
S3 Event-Based Crawling reduces discovery time by only scanning modified subfolders rather than entire buckets.
Schema Registry: Centrally manages schemas for streaming data (Kinesis/Kafka) to prevent data corruption between producers and consumers.

Serverless Transformation Engines#

Apache Spark (v5.1): The flagship engine for high-performance, distributed ETL. Includes support for Python 3.11 and Scala 2.12.
AWS Glue for Ray: A specialized engine for scaling pure Python workloads (using Ray.io). Ideal for machine learning preprocessing and non-Spark Python libraries.
Dynamic Frames: An extension of Spark DataFrames that allows for flexible, schema-on-read processing; critical for handling nested JSON data.
Interactive Sessions: Serverless development environments that allow you to test and debug Spark/Ray code in real-time from an IDE or a Jupyter Notebook.

No-Code & Visual Development#

AWS Glue Studio: A visual, drag-and-drop interface for building complex ETL pipelines without writing code.
Custom Visual Transforms: Allows data engineers to wrap custom Spark logic into reusable visual nodes for less technical team members.
AWS Glue DataBrew: A visual “data prep” tool with over 250 pre-built transformations for cleaning and normalizing data in an Excel-like interface.

Intelligent Data Quality & Security#

ML-Powered Data Quality: Automatically recommends and enforces rules (e.g., Column "Total" > 0) and detects anomalies in data patterns over time.
Sensitive Data Detection: Uses machine learning to identify, mask, or redact PII (Personally Identifiable Information) like SSNs and credit card numbers during the ETL process.
Fine-Grained Access Control: Integration with AWS Lake Formation for row-level and cell-level security, now extended to both read and write operations in 2026.

Modern Data Lake & SaaS Integration#

Open Table Format Support: Native, optimized support for Apache Iceberg (v3.0), Hudi, and Delta Lake, enabling ACID transactions on S3.
Native SaaS Connectivity: Over 100+ built-in connectors for applications like Salesforce, SAP, Google Ads, and ServiceNow.
Native REST API Connector: Allows universal connectivity to any REST-enabled source without requiring custom Java/JAR libraries.
Reverse ETL: The capability to push processed insights from your data lake back into SaaS operational tools (e.g., syncing lead scores to Salesforce).

Orchestration & Optimization#

Glue Workflows: A visual tool to build and monitor multi-step pipelines (Crawler Job Trigger).
Job Bookmarks: Tracks state between runs to ensure only new data is processed, preventing redundant compute costs.
Glue Flex: A flexible execution class for non-urgent jobs that offers up to a 35% discount on DPU costs.
GenAI Job Upgrades: AI-powered assistants that automatically analyze legacy Spark code and generate upgrade plans for newer Glue versions.

Concepts#

Connecting to Data Sources and Destinations#

An AWS Glue connection is a Data Catalog object that stores login credentials, URI strings, virtual private cloud (VPC) information, and more for a particular data store. Connections are used for both sources and targets. If your data source or destination connector is not supported natively, you can either subscribe to a connector from the AWS Marketplace or develop your own. It is most likely your data store is supported with official connectors as Glue is always adding new connectors.

Glue Streaming#

From the official docs, Read for In-depth Coverage ↗

Using the Apache Spark Streaming framework, AWS Glue Streaming provides a serverless service that can handle streaming data at scale. AWS Glue provides various optimizations on top of Apache Spark such as serverless infrastructure, auto-scaling, visual job development, instant-on notebooks for streaming jobs and other performance improvements.

Supported Source
- Amazon Kinesis
- Amazon MSK (Managed Streaming for Apache Kafka)
- Self-managed Apache Kafka
Supported Destinations
- Data targets supported by AWS Glue Data Catalog
- Amazon S3
- Amazon Redshift
- MySQL
- PostgreSQL
- Oracle
- Microsoft SQL Server
- Snowflake
- Any database that can be connected using JDBC
- Apache Iceberg, Delta and Apache Hudi
- AWS Glue Marketplace connectors

Glue Zero-ETL Integrations#

AWS Glue Zero-ETL is a fully managed integration that enables near real-time data movement from operational sources—such as Amazon Aurora, DynamoDB, and enterprise SaaS apps (e.g., Salesforce, SAP), directly into Amazon Redshift or Amazon S3 Tables without manual pipeline development.

The core philosophy is managed ingestion: it utilizes built-in Change Data Capture (CDC) to synchronize data automatically, handling complex tasks like schema discovery, mapping, and evolution. By eliminating the “T” (Transformation) during the initial move, it drastically reduces operational overhead and data latency, allowing analysts to query live transactional data seconds after it is created. So Zero-ETL shits to Extract Load & Transform paradigm i.e Transform at the Destination.

Official Docs ↗

Official Documentation#

Glue Documentation ↗

Wrap-up#

With the rise of Zero-ETL and Generative AI for script generation in 2026, AWS Glue is becoming more accessible than ever. Whether you are using the visual designer or writing deep PySpark logic, the goal remains the same: Reliable, scalable, and serverless data integration.