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Enable Multi-AZ deployments for your Amazon Redshift data warehouse

AWS Big Data

Amazon Redshift is a fully managed, petabyte scale cloud data warehouse that enables you to analyze large datasets using standard SQL. Data warehouse workloads are increasingly being used with mission-critical analytics applications that require the highest levels of resilience and availability.

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

AWS Big Data

When data is used to improve customer experiences and drive innovation, it can lead to business growth,” – Swami Sivasubramanian , VP of Database, Analytics, and Machine Learning at AWS in With a zero-ETL approach, AWS is helping builders realize near-real-time analytics.

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Migrate Microsoft Azure Synapse Analytics to Amazon Redshift using AWS SCT

AWS Big Data

Amazon Redshift is a fast, fully managed, petabyte-scale data warehouse that provides the flexibility to use provisioned or serverless compute for your analytical workloads. Modern analytics is much wider than SQL-based data warehousing. Fault tolerance is built in. Any hardware failures are automatically replaced.

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Implement data warehousing solution using dbt on Amazon Redshift

AWS Big Data

Amazon Redshift is a cloud data warehousing service that provides high-performance analytical processing based on a massively parallel processing (MPP) architecture. Building and maintaining data pipelines is a common challenge for all enterprises. For more information, refer SQL models.

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

AWS Big Data

This is the first post to a blog series that offers common architectural patterns in building real-time data streaming infrastructures using Kinesis Data Streams for a wide range of use cases. In this post, we will review the common architectural patterns of two use cases: Time Series Data Analysis and Event Driven Microservices.

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Getting started guide for near-real time operational analytics using Amazon Aurora zero-ETL integration with Amazon Redshift

AWS Big Data

In this post, we provide step-by-step guidance on how to get started with near-real time operational analytics using this feature. There are two broad approaches to analyzing operational data for these use cases: Analyze the data in-place in the operational database (e.g.

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Use Amazon Athena with Spark SQL for your open-source transactional table formats

AWS Big Data

AWS-powered data lakes, supported by the unmatched availability of Amazon Simple Storage Service (Amazon S3), can handle the scale, agility, and flexibility required to combine different data and analytics approaches. It will never remove files that are still required by a non-expired snapshot.

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