AWS Glue for Beginners: Key Components and How It All Works

Introduction

In today's data-driven world, businesses rely heavily on effective data integration and preparation to make informed decisions. AWS Glue, a fully managed extract, transform, and load (ETL) service, simplifies these processes. With its ability to connect, transform, and manage data from various sources, AWS Glue empowers organizations to streamline workflows and harness the full potential of their data.

This blog will guide you through what AWS Glue is, how it works, and why it is an essential tool for modern data engineering. Whether you’re new to ETL processes or exploring ways to optimize your data pipelines, this post will equip you with the insights needed to understand and effectively leverage AWS Glue.

What Is ETL?

ETL stands for Extract, Transform, and Load, a process used to prepare data for analysis or use in applications.

• Extract: Data is collected from various sources such as databases, APIs, or flat files.
• Transform: The data is cleaned, formatted, or enriched to meet specific requirements.
• Load: The transformed data is then stored in a destination like a data warehouse or a data lake for further use.

For instance, imagine a retail company extracting sales data from its online store, transforming it by organizing it by region and time period, and then loading it into a database for creating sales performance reports.

AWS Glue simplifies this process by providing tools to automate and manage each step, making it easier to work with data from over 70 sources. Its centralized data catalog enables users to organize and query data efficiently using AWS services like Amazon Athena and Redshift Spectrum.

With features like data discovery, cleansing, transformation, and cataloging, AWS Glue is designed to handle data workflows of any size. It seamlessly integrates with AWS analytics services and Amazon S3 data lakes, offering tools suited for both technical and non-technical users. Additionally, its pay-as-you-go model ensures flexibility and cost-effectiveness.

What Is AWS Glue ?

AWS Glue is a serverless data integration service that simplifies discovering, preparing, and integrating data from multiple sources. It supports use cases like analytics, machine learning, and application development by providing tools to build and monitor ETL (extract, transform, load) pipelines, all without managing infrastructure.

Key Features of AWS Glue

AWS Glue offers a wide range of features across three main areas: data discovery, transformation, and pipeline management.

Discover and Organize Data

• Unify and Search Across Data Sources: Catalog and index data from multiple on-premises and AWS sources for unified access.
• Automatic Data Discovery: Use crawlers to infer schemas and integrate them into the AWS Glue Data Catalog.
• Schema and Permission Management: Validate schemas and control data access securely.
• Wide Connectivity: Seamlessly connect to diverse data sources to build robust data lakes.

Transform, Prepare, and Clean Data

• Visual Data Transformation: Use a drag-and-drop job canvas to define ETL processes and auto-generate code.
• Advanced ETL Scheduling: Schedule or trigger ETL jobs based on events or demand.
• Streamlined Streaming Data Handling: Clean and transform data in transit for real-time analysis.
• Smart Deduplication: Use built-in machine learning with FindMatches to clean and deduplicate data easily.
• Interactive Development Tools: Explore, debug, and test data interactively with notebooks and IDEs.
• Sensitive Data Management: Detect, classify, and handle sensitive data in pipelines and data lakes.

Build and Monitor Data Pipelines

• Dynamic Scaling: Automatically scale resources up or down based on workload demands.
• Event-Driven Automation: Automate jobs and workflows with event-based triggers and dependencies.
• Comprehensive Monitoring: Track jobs using Spark or Ray engines with detailed insights and Apache Spark UI.
• Workflow Orchestration: Define and manage complex workflows with multiple jobs, triggers, and crawlers.

AWS Glue Components

AWS Glue provides a set of tools to help you manage, prepare, and process data easily. Each feature works together to simplify tasks like organizing metadata, discovering data, and building ETL pipelines.

Here’s an overview of the key features, starting with the Data Catalog, which organizes your data, and moving to Crawlers, ETL tools, Triggers and more.

AWS Glue Data Catalog

As one of the key components of AWS Glue, the Data Catalog plays a pivotal role in managing and organizing metadata for your data assets. It acts as a centralized repository, making it easier to discover, understand, and utilize data across your organization. Here's an overview of its features and capabilities:

The AWS Glue Data Catalog stores metadata about your datasets, serving as an index for their location, schema, and runtime metrics. Each metadata table in the catalog represents a single data store. This metadata simplifies data discovery and integration across various AWS services.

You can populate the Data Catalog in two ways:

• Automated Crawling: AWS Glue Crawlers scan your data sources (both AWS-based and external) to automatically infer schemas and create metadata.
• Manual Definition: Define tables manually by specifying the structure, schema, and partitions based on your specific needs.

Key Features of the AWS Glue Data Catalog

Metadata Repository

Acts as a centralized metadata hub, storing information about data locations, schemas, and properties. The structure is organized into databases and tables, similar to a relational database catalog.

Automatic Data Discoverability

AWS Glue Crawlers can automatically detect and catalog new or updated data sources. This minimizes manual effort and ensures your catalog remains current. Supported sources include Amazon S3, RDS, Redshift, Hive, and more.

Schema Management

Automatically captures and manages data schemas, handling schema inference, evolution, and versioning. You can update schemas and partitions directly through AWS Glue ETL jobs.

Table Optimization

Enhances performance by managing compaction for Iceberg tables, reducing small Amazon S3 objects into larger, optimized objects. This boosts query and job performance in services like Athena and EMR.

Column Statistics

Provides insights into column-level statistics such as min/max values, null counts, and distinct values for formats like Parquet, ORC, JSON, CSV, and more. This helps optimize queries and understand data profiles.

Data Lineage

Tracks the transformations and operations applied to data, enabling visibility into data lineage. This is valuable for compliance, auditing, and understanding data provenance.

Integration with AWS Services

The Data Catalog integrates with services like Athena, Lake Formation, Redshift Spectrum, and EMR, enabling seamless querying and analysis of data across multiple stores.

Security and Access Control

1. Fine-Grained Permissions: Integrates with AWS Lake Formation for granular access control.
2. Encryption: Uses AWS Key Management Service (KMS) to encrypt metadata for enhanced security.

The Data Catalog enhances ETL workflows by providing a single source of truth for your metadata, which you can use to define and monitor ETL jobs. Whether it’s querying data with Amazon Athena or processing it with EMR, the Data Catalog ensures your metadata is consistent and accessible.

AWS Glue Crawlers

AWS Glue Crawlers are the primary method used by most AWS Glue users to keep their Data Catalog updated and organized.

A crawler can scan multiple data stores in a single run, automatically detecting the structure and schema of your data. Once the crawl is complete, the crawler creates or updates one or more tables in the Data Catalog. These tables act as the foundation for your ETL workflows, serving as sources and targets for AWS Glue jobs. The ETL job then reads data from and writes data to the data stores specified in these catalog tables.

By automating metadata discovery and schema management, AWS Glue Crawlers save significant time and effort, ensuring that your Data Catalog remains accurate and up-to-date. This makes it easier to manage large-scale data lakes or complex data ecosystems efficiently.

The following diagram illustrates how AWS Glue Crawlers interact with data stores and other elements to populate the Data Catalog:

1. A crawler runs any custom classifiers that you choose to infer the format and schema of your data. You provide the code for custom classifiers, and they run in the order that you specify.The first custom classifier to successfully recognize the structure of your data is used to create a schema. Custom classifiers lower in the list are skipped.
2. If no custom classifier matches your data's schema, built-in classifiers try to recognize your data's schema. An example of a built-in classifier is one that recognizes JSON.
3. The crawler connects to the data store. Some data stores require connection properties for crawler access.
4. The inferred schema is created for your data.
5. The crawler writes metadata to the Data Catalog. A table definition contains metadata about the data in your data store. The table is written to a database, which is a container of tables in the Data Catalog. Attributes of a table include classification, which is a label created by the classifier that inferred the table schema.

AWS Glue ETL

AWS Glue ETL enables you to extract data from various sources, transform it based on your requirements, and load it into your target destination. It uses the Apache Spark engine for distributed processing, allowing efficient handling of large-scale datasets with in-memory processing.

Supported Data Sources

AWS Glue ETL supports multiple data sources, including:

• Amazon S3
• Amazon DynamoDB
• Amazon RDS
• Amazon Kinesis Data Streams
• Apache Kafka and Amazon MSK (Managed Streaming for Apache Kafka)

Authoring ETL Jobs

AWS Glue provides several methods for creating ETL jobs:

1. Python Shell Jobs:
   • For small to medium datasets.
   • Runs basic ETL scripts on a single machine.
2. Apache Spark Jobs:
   • For large datasets and complex transformations.
   • Written in Python or Scala and scaled across multiple worker nodes.
3. Streaming ETL:
   • Processes streaming data using the Apache Spark Structured Streaming engine.
   • Ingests data streams from Amazon Kinesis, Apache Kafka, and Amazon MSK.
   • Cleans and transforms streaming data and loads it into Amazon S3 or JDBC data stores.
   • Can handle event data like IoT streams, clickstreams, and network logs.

AWS Glue Triggers

Triggers in AWS Glue automate ETL workflows by starting jobs or crawlers based on specific conditions. They allow you to define flexible schedules or event-based workflows, reducing the need for manual intervention in data pipelines. Here’s an overview of how triggers work and the types available:

How Triggers Work

• A trigger can start specified jobs and crawlers either on-demand, on a schedule, or based on certain conditions.
• Each trigger can activate up to two crawlers at a time. For crawling multiple data stores, it’s recommended to configure one crawler for multiple sources instead of running multiple crawlers simultaneously.
• Triggers can exist in the following states:
  • CREATED: Ready to fire but not yet activated.
  • ACTIVATED: Actively monitoring or waiting for conditions to fire.
  • DEACTIVATED: Temporarily paused from firing.
  • Transitional states such as ACTIVATING occur during state changes.

Triggers can be deactivated to temporarily stop them from firing and reactivated when needed.

Types of Triggers in AWS Glue

1. Scheduled Triggers
Scheduled triggers are time-based and run jobs or crawlers at specific intervals. You can define the schedule using cron-based expressions, allowing customization of frequency, days of the week, and execution times.

2. Conditional Triggers
Conditional triggers activate based on the completion status of other jobs or crawlers. They monitor specific states, such as:

• Job States: SUCCEEDED, FAILED, TIMEOUT, STOPPED.
• Crawler States: SUCCEEDED, FAILED, CANCELLED.

These triggers allow complex dependencies between jobs and crawlers. For example, you can set a trigger to start Job J3 only after both J1 and J2 succeed or start J4 if either J1 or J2 fails.

3. On-Demand Triggers
On-demand triggers activate manually. They are always in the CREATED state and do not rely on schedules or conditions, making them ideal for ad-hoc job execution.

Passing Parameters with Triggers

Triggers can pass parameters to the jobs they start, such as:

• Job Arguments: Custom settings like timeout values or security configurations.
• Key-Value Pairs: Override default job arguments or add additional ones.

For example, you can use triggers to set specific configurations for each job they execute, ensuring consistency and flexibility in your ETL workflows.

AWS Glue Workflow

Since we’ve explored the core components, let’s take a look at how a typical workflow in AWS Glue comes together:

1. Define Data Sources and Targets: Start by registering your data sources and destinations in the AWS Glue Data Catalog.
2. Populate the Data Catalog: Use Crawlers to scan data sources, infer schemas, and create table metadata automatically in the catalog.
3. Define ETL Jobs: Write or auto-generate transformation scripts to extract, transform, and load data between sources and targets.
4. Run Jobs: Execute ETL jobs on-demand or automate them with schedule- or event-based triggers.
5. Monitor Job Performance: Use built-in dashboards to track job runs, optimize performance, and troubleshoot as needed.

The following diagram shows the architecture of an AWS Glue environment:

You define jobs in AWS Glue to accomplish the work that's required to extract, transform, and load (ETL) data from a data source to a data target. You typically perform the following actions:

• For data store sources, you define a crawler to populate your AWS Glue Data Catalog with metadata table definitions. You point your crawler at a data store, and the crawler creates table definitions in the Data Catalog. For streaming sources, you manually define Data Catalog tables and specify data stream properties.
• In addition to table definitions, the AWS Glue Data Catalog contains other metadata that is required to define ETL jobs. You use this metadata when you define a job to transform your data.
• AWS Glue can generate a script to transform your data. Or, you can provide the script in the AWS Glue console or API.
• You can run your job on demand, or you can set it up to start when a specified trigger occurs. The trigger can be a time-based schedule or an event.
• When your job runs, a script extracts data from your data source, transforms the data, and loads it to your data target. The script runs in an Apache Spark environment in AWS Glue.

Additional Components

AWS Glue DataBrew

AWS Glue DataBrew is a no-code, visual data preparation tool designed to make cleaning and normalizing data faster and more accessible. With over 250 pre-built transformations, DataBrew allows you to automate tasks like filtering anomalies, standardizing data formats, and correcting invalid values

The following image illustrates how DataBrew works at a high level:

To start with AWS Glue DataBrew, you begin by creating a project and connecting it to your data. In the project workspace, your data is displayed in a grid-like visual interface, where you can explore its structure and profile. This includes viewing value distributions, identifying patterns, and analyzing charts to understand your data at a deeper level.

Data preparation is intuitive and code-free, thanks to a library of over 250 point-and-click transformations. These transformations let you:

• Remove nulls or replace missing values.
• Fix schema inconsistencies or create new columns using custom functions.
• Apply advanced techniques like natural language processing (NLP) to split sentences into phrases.

As you apply transformations, DataBrew provides immediate previews of your data, showing changes before committing them to the entire dataset. This allows you to refine your steps and ensure accuracy before finalizing your recipe—a saved sequence of transformations that can be reused or updated later.

Once the recipe is run, the transformed dataset is stored in Amazon S3, making it readily available for downstream systems.

AWS Glue Data Quality

AWS Glue Data Quality is a tool designed to help you measure and monitor the quality of your data. Built on the open-source DeeQu framework, it offers a managed, serverless experience and uses the Data Quality Definition Language (DQDL) to define and enforce data quality rules.

Key Features and Benefits

Serverless Operation:

No installation, patching, or maintenance is required, allowing you to focus solely on improving data quality.

Quick Start:

Get started in just two clicks with the “Create Data Quality Rules → Recommend rules” workflow. AWS Glue Data Quality can analyze your data and suggest initial rules automatically.

Comprehensive Quality Checks:

Enforce data quality checks for both data at rest (in the Data Catalog) and data in transit (during AWS Glue ETL jobs).

ML-Powered Anomaly Detection:

Machine learning helps identify anomalies and subtle data quality issues that might otherwise go unnoticed.

Customizable Rules:

Start with over 25 pre-built rules or create your own using DQDL to fit your specific requirements.

Data Quality Score:

Evaluate your rules and receive a score that reflects the overall health of your data. Use this score to guide your decisions confidently.

Pinpoint and Fix Issues:

Identify exact records that fail quality checks, making it easier to quarantine and resolve bad data.

Pay-As-You-Go Pricing:

Avoid annual licenses and only pay for what you use.

Open Language for Rules:

Built on DeeQu, AWS Glue Data Quality ensures flexibility and portability. Rules authored in DQDL can be consistently managed, version-controlled, and deployed.

AWS Glue Schema Registry

The AWS Glue Schema Registry is a tool for centrally managing, discovering, and evolving data stream schemas. A schema defines the structure and format of a data record, acting as a contract between producers and consumers of data. By using the Schema Registry, you can enforce schemas in your streaming applications and ensure data quality across systems.

Key Features of AWS Glue Schema Registry

• Schema Management for Streaming Applications:
• Supports integrations with popular streaming platforms like Apache Kafka, Amazon MSK (Managed Streaming for Apache Kafka), Amazon Kinesis Data Streams, Amazon Managed Service for Apache Flink, and AWS Lambda.
• Supported Formats and Compatibility:
  • AVRO (v1.10.2)
  • JSON Schema (Draft-04, Draft-06, Draft-07) with validation using the Everit library.
  • Protocol Buffers (Protobuf) for proto2 and proto3, without extensions or groups.
  • Java Language Support, with additional formats and languages planned.
• Convenient Features:
  • Compatibility checks and schema evolution.
  • Schema sourcing via metadata.
  • Auto-registration of schemas for ease of use.
  • Optional ZLIB compression to reduce storage and transfer costs.
  • Integration with IAM for access control.
• Serverless and Free to Use:
  • Like other AWS Glue services, the Schema Registry is serverless, requiring no infrastructure setup, and is free to use.

Benefits of Using AWS Glue Schema Registry

• Improved Data Governance:
• Enforcing schemas ensures that data records follow consistent formats, improving quality and reliability.
• Resilience to Schema Changes:
• The registry enables downstream systems to adapt to compatible changes, reducing the impact of updates.
• Seamless Serialization and Deserialization:
• Producers and consumers of data can rely on the Schema Registry to handle serialization and deserialization, simplifying the process in systems like Amazon MSK and Apache Kafka.

AWS Glue vs Amazon EMR

AWS Glue and Amazon EMR are both powerful tools for data processing on AWS. Their overlapping capabilities—like enabling ETL workflows and supporting Apache Spark—often lead to confusion. However, they are designed for different purposes and cater to distinct use cases.

Key Differences Between AWS Glue and Amazon EMR

FeatureAWS GlueAmazon EMRTypeServerless data integration serviceManaged big data platformSetupMinimal setup; auto-handles infrastructureRequires configuration of EC2 instancesUse Case FocusSimplified ETL workloadsBig data processing, real-time analyticsSupported FrameworksApache SparkHadoop, Spark, Hive, Presto, TensorFlowCost ModelPay-as-you-go serverless pricingLower infrastructure costs, but more effort

When to Use AWS Glue

• Quick ETL Workflows: Ideal for small to medium jobs, especially when infrastructure setup needs to be avoided.
• Testing New Data Pipelines: Perfect for sandbox environments or ad-hoc jobs with minimal risk of wasted spending.
• Legacy ETL Migrations: Great for transitioning from platforms like Informatica or Talend.

When to Use Amazon EMR

• Big Data Processing: Suited for handling large-scale, distributed workloads securely and reliably.
• Machine Learning and AI: Supports TensorFlow and other tools for deep learning.
• Advanced Analytics: Ideal for teams needing flexibility with Hadoop ecosystem components like Hive and Presto.

Considerations for Performance and Cost

• AWS Glue: Easy to use but limited by its serverless architecture. For example, it caps memory for worker nodes at 32GB, which may lead to performance issues with large files.
• Amazon EMR: Offers flexibility with instance types, scaling up to 24 Tebibytes (TiB) of RAM, making it better for high-performance workloads. However, it requires manual setup, which can increase operational effort.

Summary: AWS Glue or Amazon EMR?

• Choose AWS Glue for ad-hoc ETL jobs, quick setups, or when simplicity is key.
• Choose Amazon EMR for large-scale, long-term data processing or real-time analytics that demand flexibility and advanced features.

AWS Glue Pricing

AWS Glue offers a flexible, pay-as-you-go pricing model tailored to various data integration needs. Here's a concise breakdown:

1. AWS Glue Data Catalog:

• Storage and Access: The first million objects stored and the first million accesses per month are free. Beyond these thresholds, charges apply based on usage.

2. Crawlers and ETL Jobs:

• Billing: Charged per second, with rates based on the number of Data Processing Units (DPUs) utilized.
• Minimum Duration: For AWS Glue versions 2.0 and later, there's a 1-minute minimum billing duration for jobs.

3. Development Endpoints:

• Purpose: Facilitate interactive ETL code development.
• Billing: Hourly rates, billed per second, with a 10-minute minimum duration.

4. AWS Glue DataBrew:

• Interactive Sessions: Billed per session.
• DataBrew Jobs: Billed per minute.

5. AWS Glue Schema Registry:

• Cost: Offered at no additional charge.

Note: Pricing varies by AWS Region. For detailed and up-to-date information, refer to the AWS Glue Pricing page.

Conclusion

AWS Glue offers a versatile, serverless solution for data integration, making it easier for organizations to manage ETL workflows and process their data efficiently. Whether you’re looking to handle metadata management, streamline ETL tasks, prepare data, or enforce data quality checks, AWS Glue delivers a comprehensive set of tools to meet diverse business needs.

With AWS Glue, businesses can shift their focus from managing infrastructure to uncovering valuable insights, enabling faster, smarter decision-making. As the importance of data continues to grow, AWS Glue stands out as an essential tool for staying competitive in today’s data-driven landscape.