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How Gupshup built their multi-tenant messaging analytics platform on Amazon Redshift

AWS Big Data

It makes it fast, simple, and cost-effective to analyze all your data using standard SQL and your existing business intelligence (BI) tools. Additionally, extract, load, and transform (ELT) data processing is sped up and made easier. This compiled data is then imported into Aurora PostgreSQL Serverless for operational reporting.

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Configure monitoring, limits, and alarms in Amazon Redshift Serverless to keep costs predictable

AWS Big Data

In the Metric filters section, expand Additional filtering options. In the Metric filters section, expand Additional filtering options. Method 2: Monitor metrics in CloudWatch Redshift Serverless publishes serverless endpoint performance metrics to CloudWatch. Choose Workgroup to view workgroup-related metrics.

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Exploring real-time streaming for generative AI Applications

AWS Big Data

Stream processing, however, can enable the chatbot to access real-time data and adapt to changes in availability and price, providing the best guidance to the customer and enhancing the customer experience. When the model finds an anomaly or abnormal metric value, it should immediately produce an alert and notify the operator.

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

AWS Big Data

Amazon Redshift is a popular cloud data warehouse, offering a fully managed cloud-based service that seamlessly integrates with an organization’s Amazon Simple Storage Service (Amazon S3) data lake, real-time streams, machine learning (ML) workflows, transactional workflows, and much more—all while providing up to 7.9x

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Build incremental data pipelines to load transactional data changes using AWS DMS, Delta 2.0, and Amazon EMR Serverless

AWS Big Data

Building data lakes from continuously changing transactional data of databases and keeping data lakes up to date is a complex task and can be an operational challenge. You can then apply transformations and store data in Delta format for managing inserts, updates, and deletes.

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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. All these architecture patterns are integrated with Amazon Kinesis Data Streams. The raw data can be streamed to Amazon S3 for archiving.

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How Tricentis unlocks insights across the software development lifecycle at speed and scale using Amazon Redshift

AWS Big Data

It has been well published since the State of DevOps 2019 DORA Metrics were published that with DevOps, companies can deploy software 208 times more often and 106 times faster, recover from incidents 2,604 times faster, and release 7 times fewer defects. The main idea of this architecture is to be event-driven with eventual consistency.