AWS Partner Network (APN) Blog

Category: Amazon Redshift

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How to Collaborate Across Your AWS Data Stack with Atlan

More people are using data today than ever before, but it’s getting harder and harder for everyone to collaborate on the same data. Atlan has pioneered a collaborative workspace helping modern data teams work together better. Atlan is a collaboration and orchestration layer—the glue that brings together your team, the tools you love, and the data you need. Learn how companies use Atlan and AWS to democratize their data, collaborate more effectively, and unify knowledge and context in one place.

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Finding Value in Your Digital Analytics Data Using Analytics Shift with Softcrylic and AWS

Although the business case for digital analytics is well-articulated, many organizations are looking for ways to build stronger cases around transformations by consolidating data generated across the enterprise with customer behavioral data. Learn how Softcrylic developed the Analytics Shift solution which helps businesses bring Adobe Analytics data into Amazon Redshift to drive deeper insights and data integration.

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Manage, Visualize, and Analyze Spatial Data with Amazon Redshift and CARTO

Spatial data is a vital ingredient in today’s applications, enabling capabilities ranging from asset tracking and location-based marketing to monitoring deforestation and more. Learn about the most important areas that organizations need when working with spatial data in Amazon Redshift and how CARTO helps them unleash unique spatial visualization, analysis, and app development features right inside AWS’s cloud data warehouse.

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Gaining Operational Insights of the Australian Census with AWS

In early August, millions of people took part in the 2021 Census across Australia, providing a comprehensive picture of the country’s economic, social, and cultural makeup. The Australian Bureau of Statistics (ABS) ran the 2021 Census on AWS using an operational insights platform built in partnership with AWS Professional Services, Shine Solutions, ARQ Group, and the ABS. Learn how this tool provided near real-time insights into a very complex logistical activity.

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How to Simplify Machine Learning with Amazon Redshift

Building effective machine learning models requires storing and managing historical data, but conventional databases can quickly become a nightmare to regulate. Queries start taking too long, for example, slowing down business decisions. Learn how to use Amazon Redshift ML and Query Editor V2 to create, train, and apply ML models to predict diabetes cases for a sample diabetes dataset. You can follow a similar approach to address other use cases such as customer churn prediction and fraud detection.

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Using AtScale and Amazon Redshift to Build a Modern Analytics Program with a Lake House

There has been a lot of buzz about a new data architecture design pattern called a Lake House. A Lake House approach integrates a data lake with the data warehouse and all of the purpose-built stores so customers no longer have to take a one-size-fits-all approach and are able to select the storage that best suits their needs. Learn how to couple Amazon Redshift with a semantic layer from AtScale to deliver fast, agile, and analysis-ready data to business analysts and data scientists.

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Migrating Netezza Workloads to AWS Using Amazon EMR and Amazon Redshift

Data warehouse modernization has been a key aspect of many customers’ broader cloud transformation stories. Legacy data warehouse systems, however, present many challenges when dealing with today’s enterprise data needs. Learn how AWS and Infosys collaborated to transform a legacy Netezza platform on AWS for a large retail customer. With Infosys tools, processes, and industry knowledge, the collaboration between AWS and Infosys enables customers to transform their analytics platforms.

Helping a Pharmaceutical Company Drive Business Insights Using ZS Accelerators on Amazon Redshift

With increasing data variety and volumes, it’s become increasingly necessary to ensure all of an organization’s workloads run in the most efficient manner to reduce overall turn-around time and TCO. Get an overview of the data and analytics platform ZS built to streamline and improve contracting analytics for a top life sciences company. Then, dive deep into the data architecture and learn how ZS evolved its data technology stack to get maximum performance.

Data Tokenization with Amazon Redshift and Protegrity

Many companies are using Amazon Redshift to analyze and transform their data. As data continues to grow and become even more important, they are looking for more ways to extract valuable insights. One use case we’re especially excited to support is that of data tokenization and masking. Amazon Redshift has collaborated with Protegrity, an AWS Advanced Technology Partner, to enable organizations with strict security requirements to protect their data while being able to obtain the powerful insights.

How Etleap Integrates with Amazon Redshift Data Sharing to Provide Isolation of ETL and BI Workloads

Tens of thousands of customers use Amazon Redshift to process exabytes of data per day and power analytics workloads such as business intelligence, predictive analytics, and real-time streaming analytics. Learn how Etleap integrates with new data sharing and cross-database querying capabilities in Amazon Redshift to enable workload isolation for diverse analytics use cases in customer environments. Etleap integrates with any data source and runs either as a hosted solution (SaaS) or inside your VPC.