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Orchestrate an end-to-end ETL pipeline using Amazon S3, AWS Glue, and Amazon Redshift Serverless with Amazon MWAA

AWS Big Data

Cross-account access has been set up between S3 buckets in Account A with resources in Account B to be able to load and unload data. In the second account, Amazon MWAA is hosted in one VPC and Redshift Serverless in a different VPC, which are connected through VPC peering. For more information, see Accessing an Amazon MWAA environment.

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Use your corporate identities for analytics with Amazon EMR and AWS IAM Identity Center

AWS Big Data

Use Lake Formation to grant permissions to users to access data. Test the solution by accessing data with a corporate identity. Audit user data access. About the Authors Pradeep Misra is a Principal Analytics Solutions Architect at AWS. Create an IAM Identity Center enabled security configuration for EMR clusters.

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Use Snowflake with Amazon MWAA to orchestrate data pipelines

AWS Big Data

If you’re testing on a different Amazon MWAA version, update the requirements file accordingly. For testing purposes, you can choose Add permissions and add the managed AmazonS3FullAccess policy to the user instead of providing restricted access. To create the connection string, the Snowflake host and account name is required.

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Build efficient ETL pipelines with AWS Step Functions distributed map and redrive feature

AWS Big Data

There are multiple tables related to customers and order data in the RDS database. Amazon S3 hosts the metadata of all the tables as a.csv file. Over the years, he has helped multiple customers on data platform transformations across industry verticals. The following diagram illustrates the Step Functions workflow.

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Break data silos and stream your CDC data with Amazon Redshift streaming and Amazon MSK

AWS Big Data

This solution uses Amazon Aurora MySQL hosting the example database salesdb. Prerequisites This post assumes you have a running Amazon MSK Connect stack in your environment with the following components: Aurora MySQL hosting a database. He works with AWS customers to design and build real time data processing systems.

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Gain insights from historical location data using Amazon Location Service and AWS analytics services

AWS Big Data

Data analytics – Business analysts gather operational insights from multiple data sources, including the location data collected from the vehicles. This solution includes a Lambda function that continuously updates the Amazon Location tracker with simulated location data from fictitious journeys.

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Run Spark SQL on Amazon Athena Spark

AWS Big Data

Then to perform more complex data analysis such as regression tests and time series forecasting, you can use Apache Spark with Python, which allows you to take advantage of a rich ecosystem of libraries, including data visualization in Matplot, Seaborn, and Plotly. Analytics Architect on Amazon Athena.

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