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Contributed on May 24, 2022

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AWS Glue for Serverless Data Integration Services for Analytics and Machine Learning

Blog: Indium Software - Big Data

Businesses rely on data today for analytics, machine learning, and the development of applications. The data is drawn from multiple sources such as databases, data lake, or data warehouses, where it is loaded after undergoing a series of processes. These processes include discovery, extraction, enrichment, cleaning, normalizing, and combining.

All these processes together are called Data Integration, and it makes data discovery, preparation, and combination effortless.

AWS Glue is a serverless data integration service that cuts down the data integration time from months to minutes and does not require infrastructure to be set up or managed. Users pay only for the resources consumed in a pay-per-use model.

AWS Glue Features

This time- and cost-effective service provides visual and code-based interfaces. Its features include:

• ● AWS Glue Data Catalog: Data discovery is enabled for all data assets located anywhere using a persistent metadata store in the Data Catalog. It helps manage the AWS Glue environment by providing definitions for tables, jobs, and schemas. Automatic computation of statistics and registers partitions makes data queries efficient and cost-effective. A comprehensive schema version history helps trace all the changes to your data over time.
• It also facilitates automatic schema discovery using crawlers that connect source or target data store, passing through a prioritized list of classifiers that determine the schema. The metadata is created in the tables in the Data Catalog and used for authoring the ETL jobs. The crawlers can be scheduled to run as needed, on-demand, or triggered during an event, ensuring up-to-date metadata.
• ● AWS Glue Schema Registry: Registered Apache Avro schemas are used at no additional charge for the validation and control of the evolving streaming data. The Schema Registry leverages Apache-licensed serializers and deserializers to integrate with Java applications, which helps to improve data quality and perform compatibility checks that govern schema to protect in case of unexpected changes. The schemas stored within the registry can also be used for creating or updating AWS Glue tables and partitions.
• ● AWS Glue Studio: Authoring ETL jobs for distributed processing that are highly scalable is now possible even for those who are not experts in Apache Spark. A drag-and-drop job editor allows defining and automatically generating the code in Scala or Python for the ETL process. It also facilitates building complex ETL pipelines to be run at a predetermined time, on-demand, or triggered during an event. Multiple jobs can be run parallelly or based on specific dependencies across jobs for building complex ETL pipelines. Amazon CloudWatch records all logs and notifications, allowing monitoring and being alerted in case of a failure from a central service.
• ● AWS Glue Elastic Views: This allows you to view data stored in different types of AWS data stores in AWS in any target data store you choose. Queries written using PartiQL can be used to create materialized views, which can be shared with other users. AWS Glue Elastic Views automatically updates target data stores in case of any changes to source data stores.
• ● AWS Glue DataBrew: Data analysts and scientists do not need to write code to clean and normalize data but can use the point-and-click visual interface. Data can be visualized, cleaned, and normalized directly from the data warehouses, data lakes, and databases, such as Amazon S3, Amazon Aurora, Amazon Redshift, and Amazon RDS. It offers more than 250 built-in transformations for combining, pivoting, and transposing the data. Saved transformations can also be applied directly to new incoming data to automate data preparation tasks.

Benefits of AWS Glue

AWS Glue accelerates data integration through automation of tasks such as discovery by crawling through data sources to identify the different formats of data and suggest schemas for storing the data. Data transformation and loading processes can also be automated. It enables running and managing several ETL jobs and combining and replicating data across multiple sources using SQL. AWS Glue also facilitates collaboration on related tasks such as extraction, cleaning, combining, loading, normalization, and running scalable ETL workflows.

Being serverless, users do not have to set up or manage infrastructure. Resources are also automatically provisioned, configured, and scaled based on need, with costs calculated based on usage.

• ● AWS Glue can be used to build ETL pipelines based on need.
• ● By creating a unified catalog, it helps find data from different data stores.
• ● No coding is required for creating, running, and monitoring ETL jobs.
• ● It empowers business users to prepare visual data for exploring data.
• ● Materialized views can be built for combining and replicating data.

Indium Software for Data Integration with AWS Glue

Indium Software is an AWS partner offering expertise across the entire range of AWS solutions, including AWS Glue. Our team of experienced developers and data scientists work closely with our customers to enable transforming ideas into business value quickly.

Indium leverages its more than two decades of experience working across domains and technologies to provide innovative solutions to accelerate transformations. A cross-functional team assesses your needs, designs a bespoke solution, and implements it on time and within budget to ensure optimal performance.

The post AWS Glue for Serverless Data Integration Services for Analytics and Machine Learning appeared first on Indium Software.