AWS Glue Spark ETL Job to Load Data from Amazon S3 to AWS Glue Data Catalog | PySpark ETL скачать в хорошем качестве

AWS Glue Spark ETL Job to Load Data from Amazon S3 to AWS Glue Data Catalog | PySpark ETL

=================================================================== 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 : https://www.buymeacoffee.com/cloudqui... =================================================================== Welcome to our tutorial on leveraging AWS Glue, Apache Spark, and PySpark for efficient ETL (Extract, Transform, Load) tasks in the AWS cloud environment. In this video, we'll guide you through the process of setting up an ETL job to extract data from Amazon S3, transform it using PySpark, and load it into the AWS Glue Data Catalog. Introduction to AWS Glue: We'll start by providing an overview of AWS Glue, highlighting its key features and benefits for data integration and transformation tasks. You'll learn how AWS Glue simplifies the process of building and managing ETL pipelines in the cloud. Setting up AWS Glue: Next, we'll walk you through the steps to set up AWS Glue, including creating a Glue Data Catalog to store metadata about your data sources, configuring IAM roles for access permissions, and defining connections to your Amazon S3 buckets. Creating an AWS Glue ETL Job: We'll demonstrate how to create a new ETL job in AWS Glue using the console interface. You'll see how to specify the source data location in Amazon S3, define transformation logic using PySpark scripts, and configure the target location in the Glue Data Catalog. Writing PySpark Code: This section will focus on writing PySpark code to implement the necessary transformations on the source data. We'll cover common data cleaning and enrichment tasks using PySpark DataFrame APIs, showcasing how to manipulate and reshape your data to fit your analytical needs. Executing the ETL Job: Once the ETL job is configured and the PySpark code is written, we'll demonstrate how to execute the job within AWS Glue. You'll observe the job progress, monitor resource utilization, and track any errors or warnings that may occur during execution. Monitoring and Debugging: We'll discuss best practices for monitoring and debugging AWS Glue ETL jobs, including how to use CloudWatch logs and metrics to identify performance bottlenecks and troubleshoot issues effectively. Viewing Results: Finally, we'll verify the successful completion of the ETL job and demonstrate how to access the transformed data in the AWS Glue Data Catalog. You'll learn how to query the catalog using standard SQL queries or integrate it with other AWS services for further analysis. By the end of this tutorial, you'll have a comprehensive understanding of how to use AWS Glue, Apache Spark, and PySpark to build scalable and efficient ETL pipelines for your data processing needs in the AWS cloud environment. Whether you're a data engineer, analyst, or scientist, this video will equip you with the knowledge and tools to unlock the full potential of your data assets on AWS. Repo Link : https://github.com/RekhuGopal/PythonH... #cloudquicklabs #tutorial #dataengineering #aws #glue #spark #etl #pyspark #s3 #dataloading #datacatalog #awscloud #bigdata #dataintegration #analytics #awsdata #cloudcomputing #datawarehouse #python #data #awsarchitecture