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Create an Apache Hudi-based near-real-time transactional data lake using AWS DMS, Amazon Kinesis, AWS Glue streaming ETL, and data visualization using Amazon QuickSight

AWS Big Data

With the rapid growth of technology, more and more data volume is coming in many different formats—structured, semi-structured, and unstructured. Data analytics on operational data at near-real time is becoming a common need. Then we can query the data with Amazon Athena visualize it in Amazon QuickSight.

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Access Amazon Athena in your applications using the WebSocket API

AWS Big Data

Many organizations are building data lakes to store and analyze large volumes of structured, semi-structured, and unstructured data. In addition, many teams are moving towards a data mesh architecture, which requires them to expose their data sets as easily consumable data products.

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Implement alerts in Amazon OpenSearch Service with PagerDuty

AWS Big Data

For instructions, refer to Creating and managing Amazon OpenSearch Service domains. For Host , enter events.PagerDuty.com. For more information about anomaly detection and alerts in OpenSearch Service, refer to Anomaly Detection in Amazon OpenSearch and Configuring Alerts in Amazon OpenSearch. For Method , choose POST.

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

AWS Big Data

Apache Kafka is an open-source distributed event streaming platform used by thousands of companies for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications. This solution uses Amazon Aurora MySQL hosting the example database salesdb.

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Introducing Amazon EMR on EKS job submission with Spark Operator and spark-submit

AWS Big Data

For instructions, refer to Installing or updating the latest version of the AWS CLI. For instructions, refer to Configuration and credential file settings. Also, note that we are using data-team-a as a namespace and emr-job-execution-sa as a service account, which we created in the previous step.

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Build a pseudonymization service on AWS to protect sensitive data: Part 2

AWS Big Data

The account on the right hosts the pseudonymization service, which you can deploy using the instructions provided in the Part 1 of this series. For an overview of how to build an ACID compliant data lake using Iceberg, refer to Build a high-performance, ACID compliant, evolving data lake using Apache Iceberg on Amazon EMR.

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Join a streaming data source with CDC data for real-time serverless data analytics using AWS Glue, AWS DMS, and Amazon DynamoDB

AWS Big Data

Customers have been using data warehousing solutions to perform their traditional analytics tasks. Traditional batch ingestion and processing pipelines that involve operations such as data cleaning and joining with reference data are straightforward to create and cost-efficient to maintain.