Extract, transform, and load (ETL) tasks are probably the most common in any application that works with data. Building ETL pipelines is a significant portion of a data engineer and DataOps developer’s responsibilities. But the creation of efficient ETL processes requires a lot of skills and efforts. AWS Glue is a managed cloud service that provides features for easy data extraction, transformation, and loading, as well as automatization of ETL processes.
AWS Glue is a fully managed ETL service. This service makes it simple and cost-effective to categorize your data, clean it, enrich it, and move it swiftly and reliably between various data stores.
It comprises of components such as a central metadata repository known as the AWS Glue Data Catalog, an ETL engine that automatically generates Python or Scala code, and a flexible scheduler that handles dependency resolution, job monitoring, and retries.
AWS Glue will automatically generate Scala or Python code for the streaming ETL jobs that users can further customize using tools they are used to. Furthermore, AWS Glue is serverless – therefore, there are no compute resources to configure and manage.
The Architecture of AWS Glue:
The architecture of Amazon Glue AWS comprises of below mentioned that are needed to take note of:
⦁ AWS Glue provides jobs to effectuate the work that is required to extract, transform, and load data from a data source to a data target.
⦁ AWS Glue Data Catalog is populated with metadata with the help of Crawlers. Crawlers act as a relationship between data stores and Data Catalog.
⦁ Data Catalog also contains other metadata, which is required to create an ETL job script.
⦁ ETL script generated by AWS Glue is used to transform your data.
⦁ Jobs can be run on-demand or can be scheduled at specified time intervals.
⦁ When the job runs, ETL script extracts data from sources, transforms the data, and load it into the data target.
⦁ Script runs in Apache Spark environment present in AWS Glue.
AWS Glue Components:
Following are certain terminologies to be kept in mind while operating Amazon Glue AWS:
1. AWS Glue Data Catalog
It acts as a central metadata repository. It holds the data references of data sources and data targets used in ETL jobs. By cataloging this data, you can create Data Warehouse or Data Lake. It contains the index to location and schema of your data present in data stores. Metadata tables are stored in the data catalog, which is written to a container of tables in the Data Catalog.
It tells the schema of the data, which means a description of the data. AWS Glue provide classifiers for different file types: CSV, XML, JSON etc.
Properties are needed to connect the Data Catalog to a specific data store. If you are using the S3 data store as your data source and data target, then there is no need to create a connection.
It is used to populate the AWS Glue Data Catalog with metadata. It populates the table definitions in the data catalog by pointing the crawler at a data store. Crawlers can also be used to convert semi-structured data into a relational schema.
It contains tables that define data from many different data stores. It is also responsible for arranging tables into separate categories.
6. Data Store, Data Source and Data Target:
Storing up the data is done in a repository called Data Store. Data Source is the same as Data Store, but it is used as input data for transformations. The transformation of data is written to a Data Target, which is the Data Store.
7. Dynamic Frame:
It is similar to Data Frames in Apache Spark, but Data Frames have the limitation of ETL operations. To overcome this limitation, Dynamic Frames are widely used in Glue. Each record is self-describing and has a wide variety of advanced transformation operations for data cleaning and ETL. Data Frames can be converted to Dynamic Frame and vice versa.
Scripts like Pyspark or Scala are written and thereby used to extract the data from the data sources, transform it, and then insert the transformed data into the data target data store.
It works with ETL (Extract, Transform, Load) scripts. Jobs can be run only on demand or at scheduled time intervals.
It contains metadata definition which represents your data. Data can be of any type like S3 files, amazon RD service, etc.
Advantage of AWS Glue:
⦁ Serverless – As a serverless data integration service, Amazon Glue saves you the trouble of building and maintaining infrastructure. It is Amazon that provides and manages the servers.
⦁ Automatic ETL code – AWS Glue is capable of automatically generating ETL pipeline code in Scala or Python — based on your data sources and destination. This not only streamlines the data integration operations but also gives you the privilege of parallelizing heavy workloads.
⦁ Increased data visibility – By acting as the metadata repository for information on your data sources and stores, the Amazon Glue Data Catalog helps you keep tabs on all your data assets.
⦁ Developer endpoints – For users who prefer to manually create and test their own custom ETL scripts, AWS Glue facilitates the whole development process through what it calls “developer endpoints.”
⦁ Job scheduling – AWS Glue provides easy-to-use tools for creating and following up job tasks based on schedule and event triggers, or perhaps on-demand.
⦁ Pay-as-you-go – The service doesn’t force you to commit to long-term subscription plans. Instead, you can minimize your usage costs by paying only when you need to use it.
Disadvantages of AWS Glue:
Requires technical knowledge – Some aspects of Amazon Glue are not very friendly to non-technical beginners. For instance, since all the tasks run in Apache Spark, you need to be well-versed in Spark to tweak the generated ETL jobs. What’s more, the ETL code itself can only be worked on by developers who understand Python or Scala.
Only two languages – When it comes to customizing ETL codes, Amazon Glue only supports two programming languages, Python and Scala.
Limited integrations – Amazon Glue is only built to work with other AWS services. That means you won’t be able to integrate it with platforms outside the Amazon ecosystem.
In this post, we explored AWS Glue – a powerful cloud-based tool for working with ETL pipelines. The whole process of user interaction consists of just three main steps. First, you build the data catalog with the help of data crawlers. Then you generate the ETL code required for the data pipeline. Lastly, you create the schedule for ETL jobs. AWS Glue simplifies data extraction, transformations, and loading for Spark-fluent users.