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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

Add this policy to the AWS Glue role and Amazon MWAA role: { "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Action": [ "s3:GetObject", "s3:PutObject", "s3:PutObjectAcl" ], "Resource": "arn:aws:s3:::sample-inp-bucket-etl- /*" } ] } In Account B, create the IAM policy policy_for_roleB specifying Account A as a trusted entity.

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Accelerate HiveQL with Oozie to Spark SQL migration on Amazon EMR

AWS Big Data

We split the solution into two primary components: generating Spark job metadata and running the SQL on Amazon EMR. The first component (metadata setup) consumes existing Hive job configurations and generates metadata such as number of parameters, number of actions (steps), and file formats. sql_path SQL file name.

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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. Athena is used to run geospatial queries on the location data stored in the S3 buckets. The ingestion approach is not in scope of this post. Choose Run.

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Amazon DataZone now integrates with AWS Glue Data Quality and external data quality solutions

AWS Big Data

If the asset has AWS Glue Data Quality enabled, you can now quickly visualize the data quality score directly in the catalog search pane. By selecting the corresponding asset, you can understand its content through the readme, glossary terms , and technical and business metadata.

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How SumUp made digital analytics more accessible using AWS Glue

AWS Big Data

Founded in 2012, SumUp is the financial partner for more than 4 million small merchants in over 35 markets worldwide, helping them start, run and grow their business. Data Catalog: We also wanted to automate a Glue Crawler to have metadata in a Data Catalog and be able to explore our files in S3 with Athena.

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

AWS Big Data

int '2' 'InstanceType': 'Ref': 'ClusterInstanceType' 'Market': 'ON_DEMAND' 'Name': 'Core' 'Outputs': 'ClusterId': 'Value': 'Ref': 'EmrCluster' 'Description': 'The ID of the EMR cluster' 'Metadata': 'AWS::CloudFormation::Designer': {} 'Rules': {} Trusted identity propagation is supported from Amazon EMR 6.15