Hello readers, welcome to my new tutorial series!
Data Engineering is currently at the top of every IT company’s priority list. As you are aware, there are numerous developments such as LLMs, Chatbots, CVs, and NLP. But, before we start building projects in these domains, we need to understand data engineering. We have PBs of data and it is expanding every year.
There are a number of data processing frameworks available on the market, but Apache Spark is currently the most popular.
Want to learn Apache Spark follow this link: here
Many companies are providing managed services to make it easier to host a Spark cluster. As an example:
I have experience with all of them. In this blog, I am starting an AWS Glue tutorial series, covering everything from basics to advanced topics. If you’d like tutorials on Databricks, Azure, or Google Cloud too, let me know in the comments.
AWS Glue is an Amazon Web Services (AWS) completely managed extract, transform, and load (ETL) service. It enables you to prepare and load data in the AWS environment for analyses.
AWS Glue makes it simple to identify, catalog, and transform data from a variety of sources, such as databases, data lakes, and other storage systems, into a format that can be analyzed, queried, and visualized.
AWS Glue is made up of several essential components that collaborate to make the ETL (Extract, Transform, Load) process and data preparation easier. The following are the primary components of AWS Glue:
The Data Catalog is a centralized metadata repository that maintains metadata about diverse data sources, tables, and schemas. It allows for data discovery while still maintaining a uniform picture of the data.
Crawlers are used to discover the schema and metadata of data sources automatically. They scan the data stored in various repositories, such as Amazon S3, Amazon RDS, and others, and populate the Data Catalog with the metadata that is identified.
ETL Jobs are in charge of transforming and preparing data for analysis. ETL scripts can be created and defined using the visual ETL editor or Apache Spark-based code. These jobs perform transformations on the data and load it into the target data store.
Development Endpoints provide an environment for writing, testing, and debugging Apache Spark ETL scripts. They enable data engineers to test data and code before launching ETL tasks.
You can use triggers to automate the execution of ETL tasks. Jobs can be scheduled to run at defined intervals or triggered by events such as data arriving in a specific data source.
AWS Glue provides monitoring features to track the progress of ETL tasks, their duration, and any potential faults or issues. You can also use logs to troubleshoot and analyze job performance.
Because AWS Glue is a serverless service, no infrastructure is required. The service scales automatically to handle varying workloads.
AWS Glue integrates with AWS Identity and Access Management (IAM), allowing you to control access to data sources and AWS Glue resources based on IAM policies.
This is the first and last theoretical blog on aws glue in the next blogs, I will cover each and every component of AWS Glue practically.
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