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Publish and enrich real-time financial data feeds using Amazon MSK and Amazon Managed Service for Apache Flink

AWS Big Data

An enriched data feed can combine data from multiple sources, including financial news feeds, to add information such as stock splits, corporate mergers, volume alerts, and moving average crossovers to a basic feed. To run the application, choose Run , select Run with latest snapshot , and choose Run.

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Simplify data integration with AWS Glue and zero-ETL to Amazon SageMaker Lakehouse

AWS Big Data

The status and statistics of the SEED load are published into CloudWatch and the data ingested by zero-ETL integration can be accessed in AWS using a set of services such Amazon Sagemaker Unified Studio , Amazon QuickSight , and others. The status and statistics of the CDC load are published into CloudWatch.

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Optimize checkpointing in your Amazon Managed Service for Apache Flink applications with buffer debloating and unaligned checkpoints – Part 1

AWS Big Data

Amazon Managed Service for Apache Flink , formerly known as Amazon Kinesis Data Analytics, is the AWS service offering fully managed Apache Flink. Each of the distributed components of an application asynchronously snapshots its state to an external persistent datastore. This is a two-phase operation.

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How Klarna Bank AB built real-time decision-making with Amazon Kinesis Data Analytics for Apache Flink

AWS Big Data

This post presents a reference architecture for real-time queries and decision-making on AWS using Amazon Kinesis Data Analytics for Apache Flink. In addition, we explain why the Klarna Decision Tooling team selected Kinesis Data Analytics for Apache Flink for their first real-time decision query service.

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Modernize a legacy real-time analytics application with Amazon Managed Service for Apache Flink

AWS Big Data

In this post, we discuss ways to modernize your legacy, on-premises, real-time analytics architecture to build serverless data analytics solutions on AWS using Amazon Managed Service for Apache Flink. In this traditional architecture, a relational database is used to store data from streaming data sources.

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Reliable Data Exchange with the Outbox Pattern and Cloudera DiM

Cloudera

The Outbox Pattern The general idea behind this pattern is to have an “outbox” table in the service’s data store. When the service receives a request, it not only persists the new entity, but also a record representing the message that will be published to the event bus. It is implemented in Java using the Spring framework.

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

AWS Big Data

In this example, we use Amazon EMR Serverless in combination with the open source library Pydeequ to act as an external system for data quality. If the asset has AWS Glue Data Quality enabled, you can now quickly visualize the data quality score directly in the catalog search pane.