AWS Big Data Blog

Getting the most out of your analytics stack with Amazon Redshift

Analytics environments today have seen an exponential growth in the volume of data being stored. In addition, analytics use cases have expanded, and data users want access to all their data as soon as possible. The challenge for IT organizations is how to scale your infrastructure, manage performance, and optimize for cost while meeting these […]

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Working with timestamp with time zone in your Amazon S3-based data lake

With a data lake built on Amazon Simple Storage Service (Amazon S3), you can use the purpose-built analytics services for a range of use cases, from analyzing petabyte-scale datasets to querying the metadata of a single object. AWS analytics services support open file formats such as Parquet, ORC, JSON, Avro, CSV, and more, so it’s […]

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Using pipes to explore, discover and find data in Amazon ES with Piped Processing Language

System developers, DevOps engineers, support engineers, site reliability engineers (SREs), and IT managers make sure that the underlying infrastructure powering the applications and systems within an organization is available, reliable, secure, and scalable. To achieve these goals, you need to perform a fast and deep analysis on the underlying logs, monitoring, and observability data. Amazon […]

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Harness the power of your data with AWS Analytics

2020 has reminded us of the need to be agile in the face of constant and sudden change. Every customer I’ve spoken to this year has had to do things differently because of the pandemic. Some are focusing on driving greater efficiency in their operations and others are experiencing a massive amount of growth. Across […]

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Introducing Amazon Redshift RA3.xlplus nodes with managed storage

Since we launched Amazon Redshift as a cloud data warehouse service more than seven years ago, tens of thousands of customers have built analytics workloads using it. We’re always listening to your feedback and, in December 2019, we announced our third-generation RA3 node type to provide you the ability to scale and pay for compute […]

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Amazon EMR Studio (Preview): A new notebook-first IDE experience with Amazon EMR

We’re happy to announce Amazon EMR Studio (Preview), an integrated development environment (IDE) that makes it easy for data scientists and data engineers to develop, visualize, and debug applications written in R, Python, Scala, and PySpark. EMR Studio provides fully managed Jupyter notebooks and tools like Spark UI and YARN Timeline Service to simplify debugging. […]

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Optimizing tables in Amazon Redshift using Automatic Table Optimization

Amazon Redshift is the most popular and fastest cloud data warehouse that lets you easily gain insights from all your data using standard SQL and your existing business intelligence (BI) tools. Amazon Redshift automates common maintenance tasks and is self-learning, self-optimizing, and constantly adapting to your actual workload to deliver the best possible performance. Amazon […]

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Announcing Amazon Redshift data sharing (preview)

Amazon Redshift is a fast, scalable, secure, and fully managed cloud data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL. Amazon Redshift offers up to 3x better price performance than any other cloud data warehouse. Tens of thousands of customers use Amazon Redshift to process exabytes of data […]

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Get up to 3x better price performance with Amazon Redshift than other cloud data warehouses

Since we announced Amazon Redshift in 2012, tens of thousands of customers have trusted us to deliver the performance and scale they need to gain business insights from their data. Amazon Redshift customers span all industries and sizes, from startups to Fortune 500 companies, and we work to deliver the best price performance for any use case. Earlier […]

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Bringing machine learning to more builders through databases and analytics services

Machine learning (ML) is becoming more mainstream, but even with the increasing adoption, it’s still in its infancy. For ML to have the broad impact that we think it can have, it has to get easier to do and easier to apply. We launched Amazon SageMaker in 2017 to remove the challenges from each stage […]

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