AWS Big Data Blog

ScopeofSolution

Accelerate self-service analytics with Amazon Redshift Query Editor V2

Amazon Redshift is a fast, fully managed cloud data warehouse. Tens of thousands of customers use Amazon Redshift as their analytics platform. Users such as data analysts, database developers, and data scientists use SQL to analyze their data in Amazon Redshift data warehouses. Amazon Redshift provides a web-based query editor in addition to supporting connectivity […]

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Introducing Amazon S3 shuffle in AWS Glue

AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, machine learning (ML), and application development. In AWS Glue, you can use Apache Spark, which is an open-source, distributed processing system for your data integration tasks and big data workloads. Apache Spark utilizes in-memory caching and […]

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Integrate AWS Glue DataBrew and Amazon PinPoint to launch marketing campaigns

Marketing teams often rely on data engineers to provide a consumer dataset that they can use to launch marketing campaigns. This can sometimes cause delays in launching campaigns and consume data engineers’ bandwidth. The campaigns are often launched using complex solutions that are either code heavy or using licensed tools. The processes of both extract, […]

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TrueBlue uses Amazon QuickSight to deliver more accurate pricing and grow business

This is a guest post by TrueBlue. In their own words, “Founded in 1989, TrueBlue provides specialized workforce solutions, including staffing, talent management, and recruitment process outsourcing (RPO). In 2020, the company connected approximately 490,000 people with work.” At TrueBlue, we offer solutions that help employers connect with workers worldwide. Every day, sales teams at […]

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Query hierarchical data models within Amazon Redshift

In a hierarchical database model, information is stored in a tree-like structure or parent-child structure, where each record can have a single parent but many children. Hierarchical databases are useful when you need to represent data in a tree-like hierarchy. The perfect example of a hierarchical data model is the navigation file and folders or […]

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Now Available: Updated guidance on the Data Analytics Lens for AWS Well-Architected Framework

Nearly all businesses today require some form of data analytics processing, from auditing user access to generating sales reports. For all your analytics needs, the Data Analytics Lens for AWS Well-Architected Framework provides prescriptive guidance to help you assess your workloads and identify best practices aligned to the AWS Well-Architected Pillars: Operational Excellence, Security, Reliability, […]

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Cybersecurity Awareness Month: Learn about the job zero of securing your data using Amazon Redshift

Amazon Redshift is the most widely used cloud data warehouse. It allows you to run complex analytic queries against terabytes to petabytes of structured and semi-structured data, using sophisticated query optimization, columnar on high-performance storage, and massively parallel query execution. At AWS, we embrace the culture that security is job zero, by which we mean […]

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Copy large datasets from Google Cloud Storage to Amazon S3 using Amazon EMR

Many organizations have data sitting in various data sources in a variety of formats. Even though data is a critical component of decision-making, for many organizations this data is spread across multiple public clouds. Organizations are looking for tools that make it easy and cost-effective to copy large datasets across cloud vendors. With Amazon EMR […]

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Automate building an integrated analytics solution with AWS Analytics Automation Toolkit

Amazon Redshift is a fast, fully managed, widely popular cloud data warehouse that powers the modern data architecture enabling fast and deep insights or machine learning (ML) predictions using SQL across your data warehouse, data lake, and operational databases. A key differentiating factor of Amazon Redshift is its native integration with other AWS services, which […]

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Accelerate large-scale data migration validation using PyDeequ

Many enterprises are migrating their on-premises data stores to the AWS Cloud. During data migration, a key requirement is to validate all the data that has been moved from on premises to the cloud. This data validation is a critical step and if not done correctly, may result in the failure of the entire project. […]

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