AWS Glue is a managed service extract, transform, and load (ETL) service that automatically performs the time-consuming process of preparing data for subsequent data analysis.
AWS Glue is nothing more than a serverless ETL tool. The term ETL consists of three processes. These processes are greatly needed for most data analytics or machine learning processes. These three procedures are as follows:
Extract data from a source, transform it for application use and then reload it into the data warehouse.
Glue Data Catalog detects and catalogs data automatically. It’s one of two AWS tools for transferring data from sources to analytics destinations. The other tool is AWS Data Pipeline, which focuses on data transfer.
But in this article, we will focus on AWS Glue Service. So, before moving forward, let’s have a quick look at the topics that we will be covering in this blog:
- Why should you use Glue?
- How does Glue work?
- Components of AWS Glue
- Use cases of AWS Glue
- Benefits of using Glue
- Drawbacks of using Glue
- Pricing of AWS Glue
Why should you use Glue?
Glue sought to resolve data setup and processing in a single location with little infrastructure setup. The Glue data catalog allows Glue jobs access to file-based and traditional data sources, including schema detection via crawlers. AWS Athena and Glue’s data catalog can share a Hive metastore, an excellent option for current Athena users.
One of the most valuable features of Glue is that its default timeout is two days, as opposed to Lambda’s maximum of 15 minutes. This means that you can use Glue jobs in the same way as a Lambda.
How does Glue work?
Glue extracts data from various AWS services and integrates it into data lakes and warehouses using ETL jobs. It employs APIs to convert the obtained data set for integration and assist users in job monitoring.
Users can schedule ETL jobs or select events that will trigger a job. When a job is triggered, Glue retrieves data, transforms it using code generated by Glue, and loads it into Amazon S3 or Redshift. The metadata from the job is then written into the Glue Data Catalog by Glue.
The data is then profiled in the service’s Glue Data Catalog. For Amazon Elastic MapReduce applications, a group can even use the Glue Data Catalog instead of the Apache Hive Metastore.
The service uses Glue crawlers to pull metadata into the Data Catalog, inspect raw data stores, and extract schema and other attributes.
Components of AWS Glue
There are various components of AWS Glue. Some of these components are:
Job: A job is a piece of business logic that executes an ETL task.
Table: In the database, create one or more tables that the source and target can use.
Data catalog: The data catalog stores the metadata and the data structure.
Crawler and classifier: A crawler retrieves data from a source using built-in or custom classifiers.
Development endpoint: It creates a development environment where the ETL job script can be evaluated, built, and tested for its functionality.
Database: This is used to generate or access the source and target databases.
Trigger: A trigger initiates the execution of an ETL job on demand or at a predetermined time.
Use cases of AWS Glue
Some of the use cases for Glue are:
- Execute queries on an Amazon S3 data lake. (To make your data accessible for analytics without moving it, you can use Glue.)
- Examine your data warehouse’s log data. (Create ETL scripts to modify, compress, and enhance data as it moves from source to destination)
- Build event-driven ETL pipelines. (As soon as new data is available in Amazon S3, you can start an ETL job by invoking Glue ETL jobs through an AWS Lambda function.)
- A unified view of your data from various data stores. (With Glue Data Catalog, users can quickly scan and discover their datasets while keeping all relevant metadata in one place.)
Benefits of using Glue
Some of the benefits of using AWS Glue are:
- Fault-tolerance: Failed jobs in Glue can be retrieved, and you can correct Glue logs.
- Maintenance and deployment: Since AWS handles the service, maintenance and deployment are simple.
- Support: Several non-native Java Database Connectivity data sources are supported.
- Filtering: Searches for insufficient or bad data.
Drawbacks of using Glue
Some of the drawbacks of using AWS Glue are:
- There is no incremental data sync: Since all data is first staged on S3, Glue is not the top pick for real-time ETL jobs.
- Limited compatibility: AWS Glue is only compatible with AWS-hosted services. If the sources are not AWS-based, organizations will have to use a third-party ETL service.
- Relational database queries: Glue only supports SQL queries for traditional relational database queries.
- Learning curve: Teams using Glue should be well-versed in Apache Spark.
Pricing of AWS Glue
Users must pay a monthly fee to AWS to store and manage metadata in the Glue Data Catalog. AWS Glue pricing also includes a per-second charge, with a minimum of ten minutes or 1 minute for ETL job and crawler execution. AWS also charges a fee per second for connecting to a development endpoint for interactive development.
Since there is no free trial of the AWS Glue service, you have to pay to use this service.
If you want to learn more about AWS resources or services, you can refer to the following articles:
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What are the benefits of using AWS Glue Schema Registry?
Some of the benefits of using the Glue Schema Registry are: Improve processing efficiency Save costs Improve data quality Validate schemas Safeguard schema evolution
Is AWS Glue Schema Registry a free and open-source project?
The AWS Glue Schema Registry storage is an AWS service, while the serializers and deserializers are open-source components licensed under the Apache license.
Does the AWS Glue Schema Registry include tools for managing user authorization?
Yes, both resource-level permissions and identity-based IAM policies are supported by the Schema Registry.
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