Amazon Web Services (AWS) offers a wide array of services for collecting, storing, processing, and analyzing data at scale. As data volumes continue to grow exponentially, organizations need robust and flexible extract, transform, load (ETL) solutions to integrate data from disparate sources and prepare it for business intelligence and analytics.
AWS Glue is a fully managed ETL service that makes it simple and cost-effective to catalog your data, clean it, enrich it, and move it reliably between various data stores and data streams.
Understanding AWS Glue
AWS Glue is a serverless data integration service that allows you to easily discover, prepare, and combine data for analytics, machine learning, and application development.
With AWS Glue, you don‘t need to manage any infrastructure or servers. It automatically provisions the environment needed to transform your data and scales compute resources as needed to match the workload.
The key capabilities of AWS Glue include:
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Data Catalog: Maintains metadata and structural information about your data assets and how they relate to each other.
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ETL Engine: Transforms and moves data between data stores using scalable Apache Spark jobs.
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Automatic Code Generation: Generates PySpark or Scala code for ETL jobs to extract data, transform it, and load it to target destinations.
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Scheduling and Monitoring: Enables running ETL jobs on demand or on a schedule. Allows tracking status and performance.
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Integration with AWS: Natively integrates with a wide range AWS data services including S3, Redshift, RDS, DynamoDB etc.
Using AWS Glue instead of managing your own ETL platform provides several key benefits:
- Fully managed service: No infrastructure to setup or manage
- Pay as you go pricing: Pay only for the resources used
- Rapid development: Get started quickly without investing months of effort
- Flexible scaling: Automatically scales to match data volumes
- Data lineage tracking: Track data from source to target and audit changes
- Compatible with AWS: Integrate natively with other AWS services
Common Use Cases for AWS Glue
Here are some of the most popular use cases where organizations use AWS Glue:
Building Data Warehouses
AWS Glue simplifies moving data from S3, RDS, Redshift or other data stores into a centralized data warehouse built on Amazon Redshift or Amazon EMR. The data pipeline keeps the warehouse updated by batch loading incremental changes.
Creating Data Lakes
AWS Glue crawls data sources like S3, classifies data via machine learning algorithms, catalogs metadata in the Glue Data Catalog, and makes the data available for analytics applications.
Real-time Data Processing
Glue can ingest streaming data from sources like Amazon Kinesis, then run cleaning and enrichment transformations before loading to data lakes or warehouses. The serverless architecture provides flexible scaling for event-driven ETL workloads.
Self-Service Analytics
Through integration with Amazon QuickSight, AWS Glue enables users to find, understand and prepare data for analysis without needing engineering resources.
Migration to the Cloud
For organizations moving on-premise data platforms to the cloud, AWS Glue offers an agile way to transform and load data into cloud data stores.
Key Components and Features
AWS Glue consists of a set of tightly integrated capabilities that enable end-to-end data integration:
Visual Job Editor
AWS Glue provides an easy-to-use visual interface for building, debugging and monitoring ETL jobs without programming. It auto-generates PySpark or Scala code that can be customized as needed.
Crawlers
Glue Crawlers can connect to data sources like RDS, Redshift, S3 etc, crawl the metadata of data objects, extract schemas, and store the metadata in the Glue Data Catalog.
Classifiers
AWS Glue Classifiers use machine learning algorithms to categorize, index, and tag data sources so you can easily search and access datasets for business users and applications.
Data Catalog
The Glue Data Catalog is a central metadata store that contains structural and descriptive metadata covering data sources, ETL job mappings, data lineages etc. This allows you to easily find datasets.
ETL Engine
This distributed processing engine runs Spark ETL jobs that extract data from sources, transform it, and load it into targets like S3, Redshift, Elasticsearch etc. It auto-scales resources.
Scheduling & Monitoring
AWS Glue allows scheduling workflows based on events or timers. You can track status of jobs through completion metrics and logs to resolve failures and performance issues.
Integration with Services
Natively integrates with a wide range AWS databases (DynamoDB, RDS), data warehouses (Redshift), data lakes (S3), and analytics services (EMR, QuickSight).
How Does AWS Glue Work?
AWS Glue provides a flexible workflow to integrate, transform and load your data:
Discover Data
Crawlers connect to data sources via JDBC or other access methods and listing tables, columns etc automatically into the Glue Data Catalog. Optionally apply classifiers to categorize data.
Develop ETL Jobs
Using job wizards or editors, you visually map sources to targets, designing data transformations either automatically or by writing code. AWS Glue generates PySpark/Scala code.
Generate Code and Execute
AWS Glue converts directed acyclic graph (DAG) workflows into code, scales Apache Spark dynamically, and runs ETL jobs to transform data and move it between sources/targets.
Schedule and Monitor
Schedule ETL jobs using triggers based on time or events. Track logs and metrics around runtime, failures, retries etc. Get alerts for job status.
This fully managed workflow allows you to focus on the business logic rather than managing infrastructure or data engineering tasks. Glue accelerates the cycle of discovery, analytics and application development.
Key Benefits Compared to Traditional ETL Tools
Using AWS Glue as your enterprise ETL platform provides multiple advantages over legacy, on-premise ETL technologies:
Lower Costs
As a fully managed service, Glue eliminates managing servers and infrastructure. You pay only for the compute resources used to run ETL jobs. Jobs scale down to zero when not active.
Faster Deployment
Get started in minutes since there is no need to install, configure and tune a complex ETL platform. AWS Glue integrates out-of-the-box with AWS data services.
Flexibility
Serverless architecture allows Spark ETL jobs to handle unpredictable workloads and scale instantly to adapt to data spikes without capacity planning.
Agility
Visually create, debug, update ETL flows quickly without programming expertise. Automated mapping and code generation accelerates development.
Operational Simplicity
All infrastructure software and security patching is handled automatically. Integration with AWS monitoring and security services.
Compatible with AWS
Natively integrates with Amazon S3, Redshift, RDS, EMR and other data, analytics and ML services seamlessly.
AWS Glue Use Cases and Examples
Here we look at a few examples of how AWS Glue is used within organizations for various data integration scenarios:
Build a Data Warehouse on Amazon Redshift
- Use Glue crawlers to catalog datasets from Amazon S3 buckets
- Classify data objects like lists, tables, partitions via machine learning
- Design ETL jobs with source nodes pointing to S3 data and target node as Redshift tables
- Execute Spark ETL processes that extract, transform and load data into Redshift
- Schedule batch jobs to keep Redshift updated with latest changes
Stream and Process Data in Real-time
- Stream data events, clicks, and logs via Amazon Kinesis
- Consume streams in Glue ETL jobs using the Kinesis source connector
- Look up reference data from RDS databases to enrich events
- Run cleansing, aggregations, filters or ML-based processing logic
- Load transformed, analytics-ready data into S3, Redshift etc
Create a Data Lake on Amazon S3
- Crawl and catalog bucket folders, partitions, formats like JSON/Parquet
- Profile and tag data with business metadata and ML classifiers
- Map S3 sources to target data lake folders, leveraging partitioning
- Use FindMatches to de-duplicate similar records
- Automate via event triggers or schedules
Self-Service Data Preparation and Analytics
- Data consumers browse the Data Catalog to discover datasets
- Use AWS Glue DataBrew for visual data preparation without coding
- Enrich data quality by cleaning, shaping at scale via Glue ETL
- Directly query the processed S3 data lake via Amazon Athena
- Build visualizations and dashboards on prepared data using Amazon QuickSight
As evident, AWS Glue provides a versatile platform to power diverse data transformations across your cloud, on-premise, and hybrid environments.
AWS Glue DataBrew Capabilities
AWS Glue DataBrew is a visual data preparation tool that makes it easy for data analysts and data scientists to clean, normalize and combine data to prepare it for analytics and machine learning.
With DataBrew, you don‘t need to write any code. The interactive, point-and-click visual interface makes it intuitive to profile datasets, identify issues, and take suggested actions to fix problems and transform data at scale.
Key features include:
Visual Data Profiling
Get quick insights into datasets. Profile overview shows potential data quality issues, missing values distribution, outliers etc.
Over 250 Pre-Built Transforms
Apply transformations like filtering, casting, validation, sampling, joins etc without coding. Fixes missing data, anomalies.
Machine Learning Powered
Identifying column data types and relationships between columns is automated via ML algorithms. Quickly harmonize schemas.
Scalable Data Preparation
Handle large datasets up to petabytes in size for production workflows. Integrated with AWS Glue ETL engine.
Collaboration Tools
DataBrew allows teams to build recipes, share best practices, and track data lineage end-to-end accurately.
By handling many repetitive, manual tasks involved in preparing data, DataBrew enables faster time to analytics and application development.
Alternatives and Competitors to AWS Glue
While AWS Glue is a robust platform for cloud-based data integration, there are a few alternatives to evaluate:
Azure Data Factory
Microsoft‘s iPaaS solution that allows creating ETL and ELT data orchestration using GUI or code. Integrates well across Azure data services. More limited in terms of open source and multi-cloud support.
Informatica Cloud
Leading proprietary ETL tool focused on very large enterprise data workloads. Requires significant consulting services to customize and deploy. Much more complex and expensive compared to AWS Glue.
Matillion ETL
Specializes in ETL to cloud data warehouses like Snowflake, Redshift, BigQuery. Limited to analytics use cases but very fast and easy to use through intuitive visual interface.
Talend Data Fabric
Provides extensive data integration capabilities encompassing ETL, metadata management, data quality etc. Supports multi-cloud environments. Steeper learning curve than AWS Glue.
Fivetran
Focused exclusively on ETL pipelines from SaaS/on-premise data sources to cloud warehouses and lakes. Faster to setup but less extensible than AWS Glue.
Conclusion
AWS Glue solves some the most complex challenges enterprises face in discovering, accessing, transforming, enriching and digesting constantly growing volumes of data across siloed sources.
It significantly accelerates the pace of traditional ETL processes in a serverless environment, while simplifying data engineering needs for both technical and non-technical users.
If you are looking to migrate, modernize and consolidate your analytics data workloads on the cloud, AWS Glue deserves strong evaluation.
