AWS Glue

The ETL process is crucial in data integration, enabling organizations to consolidate, cleanse, and harmonize data from disparate sources into a unified and consistent format.

Introduction to AWS Glue

AWS Glue is a serverless data integration service that consolidates the data integration capabilities, including discovery, ETL, cleansing, transforming, and cataloging, into a single serverless service catering to various workloads and user types. It provides productivity tools for authoring, running jobs, and implementing business workflows.

AWS Glue connects to data sources, extracts the data, and manages it centrally in a catalog. Additionally, it offers seamless querying of cataloged data through services like Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum. In this lesson, we will learn about the components of AWS Glue and how they work.

AWS Glue offers a comprehensive solution for managing ETL workloads through both console and API operations. Users can interact with AWS Glue programmatically using language-specific SDKs and the AWS Command Line Interface (CLI). The service utilizes the AWS Glue Data Catalog to store metadata pertaining to data sources, transformations, and targets, acting as a seamless alternative to the Apache Hive Metastore.

AWS Glue components

AWS Glue Jobs define the business logic for executing ETL workflows to process and move data between various data sources and targets. AWS Glue Jobs system provides a managed infrastructure for defining, scheduling, and executing ETL operations on data sources.

AWS Glue Data Catalog

The AWS Glue Data Catalog is the backbone of organized data management within the AWS Glue ecosystem. Imagine it as a maintained library for all our data assets in the cloud. Unlike scattered data definitions across various systems, the Data Catalog acts as a unified registry for our data. It stores metadata about our structured and semi-structured data stored in AWS services like RDS, S3, or external sources.

Here’s a breakdown of its key features and the added value it brings:

• Structured organization: The Data Catalog organizes the data assets using a familiar database and table structure. This simplifies navigating and understanding data, similar to how a well-organized library aids book discovery.

• Breaking down data silos: Data silos occur when data is isolated within specific systems, hindering overall visibility and analysis. The Data Catalog bridges this gap by providing a central location for data definitions, allowing different systems to interact and understand the data consistently.

• Enhanced security and access control: The Data Catalog empowers us to leverage IAM policies and Lake Formation to implement granular access controls. This ensures sensitive data remains protected while allowing authorized users across the organization to access and utilize relevant data securely. Imagine a library with designated access sections and librarian-controlled permissions.

• Improved data governance with audit trails: AWS Glue Data Catalog, in conjunction with CloudTrail and Lake Formation, offers comprehensive audit and governance features. It tracks schema changes and enforces data access controls, similar to how libraries maintain book borrowing and alterations records. This ensures data integrity and minimizes the risk of unauthorized modifications or accidental data exposure.

• Streamlined data integration: With a centralized understanding of data structure through the Data Catalog, integrating data from various sources for analytics or machine learning tasks becomes significantly smoother. This allows us to leverage the power of data more efficiently.

AWS Glue crawler and classifier

AWS Glue offers crawlers to automate data discovery and organization within the AWS Glue Data Catalog. They act as automated scouts, scanning various data repositories like S3 buckets, RDS databases, or custom locations. Imagine a team of librarians who automatically scan bookshelves, categorize them by genre, and create detailed entries in a library catalog.

Glue crawler runs custom or built-in classifiers to infer the data schema. Glue offers built-in classifiers for common file types such as JSON, CSV, XML, and more. These intelligent tools analyze the data’s structure and format and act like data whisperers, deciphering the language of the data to determine its type. After that, it connects to the database and writes down the metadata.

AWS Glue ETL

AWS Glue takes data integration one step further by offering automated ETL script generation. Imagine having a helpful assistant who writes the initial code for data processing tasks! Here’s how it works:

• Leveraging the data catalog: Crawlers and Classifiers discover the data in various sources and automatically populate the Data Catalog with metadata. This metadata includes details about the data’s format, location, and schema and acts as a blueprint for the data.

• Automatic script generation: Based on the information in the Data Catalog, AWS Glue can automatically generate code snippets in Scala or PySpark (Python API for Apache Spark) with AWS Glue extensions. These scripts involve extracting data from various sources, transforming it to a desired format, and loading it into a target location like a data warehouse (Redshift) or data lake (S3).

• Customizable code: The generated scripts are not set in stone. AWS Glue extensions within the code allow the data processing steps to be tailored to specific needs. We can clean, transform, and manipulate the data as required before loading it into its destination.

• ETL jobs system: We can create jobs within Glue that automate the scripts for extracting, transforming, and moving data between locations. These jobs can run on a schedule, be chained together for complex workflows, or even trigger automatically when new data arrives.

To understand the multiple aspects of Glue ETL, consider the example below. Suppose a retail company has its Point of Sales data stored in an RDS instance. Meanwhile, the customer feedback is pushed into the S3 bucket as JSON objects. The company wants to analyze this data to gain insights into sales trends, customer behavior, and product performance.

Since the data is spread across multiple storage devices, they decide to utilize AWS Glue. The AWS Glue crawler crawls through both databases and creates tables in the Glue Data Catalog to store the data. This data is then ingested by a Glue ETL job to enrich the sales data by integrating additional information such as product categories, customer demographics, and geographic regions. Furthermore, it aggregates sales data to generate key performance indicators (KPIs) such as total revenue, average order value, sales by product category, and sales by location.

The ETL job is scheduled to run every morning to load the data in a Redshift cluster. The users can perform SQL queries directly on the Redshift cluster to get specific information or integrate QuickSight, a dashboarding service provided by AWS, to create interactive dashboards and visualizations to explore and share insights from your data.

Overall, AWS Glue simplifies the process of getting metadata from the data stores and managing ETL pipelines for data integration and analytics, enabling organizations to derive insights from their data more quickly and efficiently.