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In-place version upgrades for applications on Amazon Managed Service for Apache Flink now supported

AWS Big Data

Apache Flink is an open source distributed processing engine, offering powerful programming interfaces for both stream and batch processing, with first-class support for stateful processing and event time semantics. Some things to keep in mind: Stateful downgrades are not compatible and will not be accepted due to snapshot incompatibility.

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How to achieve Kubernetes observability: Principles and best practices

IBM Big Data Hub

Observability comprises a range of processes and metrics that help teams gain actionable insights into a system’s internal state by examining system outputs. In this blog, we discuss how Kubernetes observability works, and how organizations can use it to optimize cloud-native IT architectures. How does observability work?

Metrics 69
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Architectural patterns for real-time analytics using Amazon Kinesis Data Streams, part 1

AWS Big Data

In this post, we will review the common architectural patterns of two use cases: Time Series Data Analysis and Event Driven Microservices. The streaming records are read in the order they are produced, allowing for real-time analytics, building event-driven applications or streaming ETL (extract, transform, and load).

Analytics 117
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Exploring real-time streaming for generative AI Applications

AWS Big Data

Another example is an AI-driven observability and monitoring solution where FMs monitor real-time internal metrics of a system and produces alerts. When the model finds an anomaly or abnormal metric value, it should immediately produce an alert and notify the operator. Streaming storage provides reliable storage for streaming data.

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Power enterprise-grade Data Vaults with Amazon Redshift – Part 2

AWS Big Data

Amazon Redshift delivers on that needed performance through a number of mechanisms such as caching, automated data model optimization, and automated query rewrites. String-optimized compression The Data Vault 2.0 You can use this mechanism to optimize merge operations while still making the data accessible from within Amazon Redshift.

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Achieve near real time operational analytics using Amazon Aurora PostgreSQL zero-ETL integration with Amazon Redshift

AWS Big Data

Customers across industries are becoming more data driven and looking to increase revenue, reduce cost, and optimize their business operations by implementing near real time analytics on transactional data, thereby enhancing agility. In the Instance configuration section , select Memory optimized classes.

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Introducing Amazon MWAA support for Apache Airflow version 2.7.2 and deferrable operators

AWS Big Data

You can see the time each task spends idling while waiting for the Redshift cluster to be created, snapshotted, and paused. The trigger runs in a parent process called a triggerer , a service that runs an asyncio event loop. The Cluster Activity page gathers useful data to monitor your cluster’s live and historical metrics.

Metrics 108