AWS Glue: What You Need to Know

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What is AWS glue?

AWS Glue is a serverless data integration service that streamlines discovering, preparing, and combining data for analytics, machine learning, and application development. Think of it as a powerful, automated toolkit for your data pipeline, handling the heavy lifting of extract, transform, and load (ETL) tasks.

At its core, AWS Glue has several key components:

• Data Catalog: A central metadata repository (compatible with Apache Hive Metastore) that catalogs your data assets, regardless of their location. It stores schemas, locations, and other information, making it easy to search multiple AWS datasets.
• ETL Engine: AWS Glue automatically generates ETL code (Python or Scala) to extract data from data sources, transform data, and load it into data stores. You can also write custom ETL scripts. The engine is based on Apache Spark for scalable processing.
• Glue Studio: A visual interface (drag-and-drop editor) for creating, running, and monitoring AWS Glue ETL jobs. This empowers data engineers, analysts, and business users to work with Glue without extensive coding.
• Glue DataBrew: A visual data preparation tool to clean and normalize data – ideal for filtering anomalies, standardizing formats, and correcting invalid values – all without writing code.
• Glue Data Quality evaluates and monitors data quality in your data lake and pipelines using defined data quality rules.

AWS Glue simplifies moving data from multiple sources (S3, Amazon Relational Database Service (RDS), Amazon Redshift, etc.), transforming it for analysis, and loading it into your data lake or data warehouses. It’s the foundation of serverless data integration on AWS.

When Would You Want to Use AWS Glue?

AWS Glue is a versatile solution for various data challenges. If you’re building a data lake on S3, Glue is essential for cataloging, discovering, and preparing data. For data warehousing, it expertly extracts, transforms, and loads data from disparate data sources into a unified schema in your warehouse (e.g., Amazon Redshift).

Glue’s transformation and schema conversion capabilities streamline data migration between databases or data stores. It also handles real-time data processing, integrating with streaming sources like Amazon Kinesis to enable immediate analytics. You can configure AWS Glue to trigger ETL jobs when new data arrives.

Data preparation for machine learning is another strong use case, with Glue DataBrew providing a visual, no-code interface for cleaning and transforming data. Glue automates repetitive data preparation tasks, freeing up valuable time. Thanks to its crawlers, it also allows you to discover data, capturing information that goes directly to the Glue Data Catalog. The integration with AWS Lake Formation allows reasonable security control.

For example, a retailer collecting data from online and physical stores and a CRM system could use AWS Glue to extract, clean, and transform this data (standardizing formats, resolving inconsistencies), load it into a data warehouse, and create a searchable data catalog for analysts. AWS Glue is a strong contender if your projects involve integrating, transforming, or preparing data from multiple sources.

Source: AWS Glue expands connectivity to 16 native connectors for applications

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What are the benefits of using AWS glue?

AWS Glue offers a compelling combination of benefits. Its serverless nature eliminates the need to manage servers or clusters, reducing operational overhead. You pay only for resources consumed during AWS Glue jobs – a cost-effective, pay-as-you-go model.

AWS Glue automatically generates much of the needed ETL code, saving you from writing boilerplate code and accelerating development. AWS Glue Studio provides a drag-and-drop editor for building ETL pipelines, while AWS Glue DataBrew offers a no-code environment for data cleaning. These visual tools broaden accessibility beyond just data engineers.

Powered by Apache Spark, AWS Glue handles large datasets and complex transformations. Its seamless integration with the AWS ecosystem (S3, RDS, Redshift, Amazon Athena, Amazon EMR, AWS Lake Formation) creates a cohesive data processing environment.

The AWS Glue Data Catalog is a central metadata repository that simplifies data discovery and access across multiple AWS datasets. AWS Glue Data Quality helps you analyze and improve data quality. In short, Glue streamlines your data integration workflows with serverless convenience, automation, visual tools, scalability, and AWS integration.

What are the Limitations of Using AWS Glue?

While powerful, AWS Glue has limitations. As a proprietary AWS service, it creates vendor lock-in, and migrating away from Glue later could require significant effort.

Even with Glue Studio and DataBrew, there is a learning curve. Advanced use cases often require understanding ETL concepts and potentially Apache Spark. Debugging complex AWS Glue ETL jobs can be challenging.

While convenient, the serverless model means costs can accumulate if AWS Glue jobs aren’t optimized. Monitoring resource usage and optimizing ETL scripts are crucial.

DataBrew, while excellent for many tasks, has limitations compared to custom code for highly specialized transformations.

Finally, AWS Glue offers many connectors but doesn’t support every data source. Verify connector availability for your specific needs, and know that you could also use Change Data Capture to minimize this limitation.

Source: What is AWS Glue?

What are the differences between AWS glue and EMR?

Both AWS Glue and Amazon EMR process data but have different strengths:

Choose AWS Glue for simpler, serverless ETL. Choose EMR for greater control and a broader range of big data tools. They often work together: Glue crawlers can populate the Data Catalog used by EMR.

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How to measure AWS glue costs

To measure AWS Glue costs, you need to monitor and analyze your usage using AWS tools actively:

AWS Cost Explorer: 

This is your primary tool for visualizing and understanding your AWS costs, including Glue. You can:

• Filter by Service: Select “Glue” to see all Glue-related costs.
• Group by Usage Type: Break down costs by Data Catalog operations, crawler runtime, ETL job runtime (DPU-hours), DataBrew sessions, etc. This is critical for understanding what is driving your costs.
• View Daily/Monthly Trends: Identify cost spikes or trends over time.
• Create Budgets: Set up budgets to receive alerts when costs exceed thresholds.

AWS CloudWatch: 

CloudWatch provides detailed metrics on your AWS Glue jobs, crawlers, and Data Catalog:

• Job Metrics: Track DPU usage, execution time, data read/written, and error rates for each job.
• Crawler Metrics: Monitor crawler runtime and the number of tables created/updated.
• Data Catalog Metrics: Track API requests to the Data Catalog.

AWS Glue Job Logs (CloudWatch Logs):

Examine the logs generated by your AWS Glue jobs for detailed information about execution, including resource utilization and any errors.

AWS Budgets: 

Set up budget alerts.

By combining Cost Explorer (for overall cost analysis), CloudWatch (for detailed metrics), and CloudWatch Logs (for debugging and optimization), you can gain a clear understanding of your AWS Glue costs and identify areas for optimization. This is measuring the costs. Then, you can proceed to optimize them.

AWS Glue, a powerful serverless data integration service, simplifies preparing data for analytics and machine learning. Its Data Catalog, automated code generation, and visual tools offer broad accessibility. 

At Ceiba, we have deep expertise in implementing and optimizing AWS Glue ETL jobs and ETL pipelines. Contact us to discuss your data integration needs and unlock your data’s full potential.