Remove 2012 Remove Metadata Remove Optimization Remove Testing
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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

Additionally, it enables cost optimization by aligning resources with specific use cases, making sure that expenses are well controlled. The policies attached to the Amazon MWAA role have full access and must only be used for testing purposes in a secure test environment.

Metadata 101
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Accelerate HiveQL with Oozie to Spark SQL migration on Amazon EMR

AWS Big Data

Customers now want to migrate their Apache Hive workloads to Apache Spark in the cloud to get the benefits of optimized runtime, cost reduction through transient clusters, better scalability by decoupling the storage and compute, and flexibility. Generate Spark SQL metadata Our batch job consists of Hive steps scheduled to run sequentially.

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Build streaming data pipelines with Amazon MSK Serverless and IAM authentication

AWS Big Data

For testing, this post includes a sample AWS Cloud Development Kit (AWS CDK) application. The following sections take you through the steps to deploy, test, and observe the example application. With AWS X-Ray, you can trace the entire application, which is useful to identify bottlenecks when load testing.

Testing 93
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Best practices to implement near-real-time analytics using Amazon Redshift Streaming Ingestion with Amazon MSK

AWS Big Data

ORDERTOPIC" WHERE CAN_JSON_PARSE(kafka_value); The metadata column kafka_value that arrives from Amazon MSK is stored in VARBYTE format in Amazon Redshift. For this post, you use the JSON_PARSE function to convert kafka_value to a SUPER data type. This sorting step can increase the latency before the streaming data is available to query.

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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. AWS Glue gave us a cost-efficient option to migrate the data and we further optimized storage cost by pruning cold data.

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

AWS Big Data

This method uses GZIP compression to optimize storage consumption and query performance. The Data Catalog provides metadata that allows analytics applications using Athena to find, read, and process the location data stored in Amazon S3. You can test this solution yourself using the AWS Samples GitHub repository. Choose Run.

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Real-Real-World Programming with ChatGPT

O'Reilly on Data

To provide some coherence to the music, I decided to use Taylor Swift songs since her discography covers the time span of most papers that I typically read: Her main albums were released in 2006, 2008, 2010, 2012, 2014, 2017, 2019, 2020, and 2022. This choice also inspired me to call my project Swift Papers.