ETL | AWS Glue | Working with Apache Spark Using 3rd Party Library and AWS Data Catalog | PySpark

===================================================================
1. SUBSCRIBE FOR MORE LEARNING :
/ @cloudquicklabs
===================================================================
2. CLOUD QUICK LABS - CHANNEL MEMBERSHIP FOR MORE BENEFITS :
/ @cloudquicklabs
===================================================================
3. BUY ME A COFFEE AS A TOKEN OF APPRECIATION :
www.buymeacoffee.com/cloudqui...
===================================================================
Welcome to our comprehensive tutorial on using AWS Glue with Apache Spark for ETL (Extract, Transform, Load) processes! In this video, we will dive deep into how to leverage AWS Glue to streamline your data processing tasks, incorporating third-party libraries and utilizing the AWS Data Catalog for enhanced data management.
What You Will Learn:
Introduction to AWS Glue and ETL: We'll start with an overview of AWS Glue, a fully managed ETL service that makes it easy to prepare and load data for analytics. Learn about the key features and benefits of using AWS Glue for your data workflows.
Setting Up AWS Glue: Step-by-step instructions on setting up AWS Glue in your AWS environment, including creating an AWS Glue job, setting up IAM roles, and configuring the necessary permissions.
Introduction to Apache Spark and PySpark: Get to know Apache Spark, an open-source unified analytics engine for big data processing, and PySpark, the Python API for Spark. Understand how they integrate with AWS Glue.
Using Third-Party Libraries in AWS Glue: Learn how to incorporate third-party libraries into your AWS Glue job to extend its functionality. We'll show you how to add external libraries and packages to your Glue job script.
Working with AWS Data Catalog: Discover how to use the AWS Data Catalog to organize and manage your data assets. Learn how to create and configure a Data Catalog, and how to use it within your AWS Glue jobs for metadata management.
Building and Running an ETL Job: Follow along as we build a real-world ETL job using AWS Glue and PySpark. We'll cover data extraction, transformation, and loading processes, demonstrating how to handle complex data transformations efficiently.
Best Practices and Tips: Get valuable tips and best practices for optimizing your AWS Glue jobs, managing resources, and ensuring cost-effectiveness.
Troubleshooting Common Issues: Learn how to troubleshoot common issues you might encounter while working with AWS Glue and Apache Spark, ensuring a smooth and efficient ETL process.
By the end of this tutorial, you'll have a solid understanding of how to use AWS Glue with Apache Spark, incorporating third-party libraries and effectively utilizing the AWS Data Catalog. Whether you're a data engineer, data scientist, or anyone interested in data processing, this video will provide you with the knowledge and skills to harness the power of AWS Glue for your data workflows.
Chapters:
00:00 - Introduction
00:40 - Setting Up AWS Glue Spark ETL Job Using Notebook
01:20 - Introduction to Notebook imported
02:23 - Using Third-Party Libraries in AWS Glue Spark Job
09:00 - Working with AWS Data Catalog data in AWS Glue Spark Job
Resources: github.com/RekhuGopal/PythonH...
Don't forget to like, comment, and subscribe for more tutorials on AWS and data engineering!
#AWSGlue #ApacheSpark #ETL #DataEngineering #PySpark #DataCatalog #DataTransformation #BigData #AWS #DataProcessing #ThirdPartyLibraries Here are all the possible tags for the video, formatted with a # mark and in lowercase:
#awsglue #apachespark #etl #dataengineering #pyspark #datacatalog #datatransformation #bigdata #aws #dataprocessing #thirdpartylibraries #dataworkflow #datamanagement #metadata #cloudcomputing #clouddata #dataanalytics #cloudetl #awstutorial #sparkjobs #cloudservices #dataextraction #datatransform #dataload #datahandling #dataoptimization #awscloud #bigdataengineering #awsetl #gluetutorial #sparketl #datascience #datatech #cloudtechnology #dataautomation #gluejob #awsgluejobs #sparkpyspark #datahandling #awstools #datacatalogmanagement #cloudquicklabs

[@adityachaubey3965]

Amazing I have a question on Redshift... 1. How companies deal with redshift as a data warehouse because in order to perform ETL from S3 we need to keep the cluster up and running all the time for loading a new batch of data ...coming from S3 to the data catalog to Redshift tables so ... Do we need to keep running the redshift cluster in order to perform such ETL where data updates are done in the indefinite time period. Wouldn't this be a costly method to go for...and what companies do in such cases to avoid cost or they keep the redshift cluster up and running 24*7 ?? Can you or anyone help me give a company's perspective for this question? What companies do in such cases ?

[@cloudquicklabs]

Thank you for watching my videos. I believe it depends on multiple factors here. 1. You need to go for Redshift Datawarehouse when this workloads has good value to your business. 2. There should multiple Consumer of this workload like application, analytics, Reporting etc. 3. Here there are chances of cost optimization is via removing the cluster in required time ( while snapshot of cluster is taken) In total there should be good tested startegy to use this service. I shall cover these points in my new video soon.