Category: Amazon Redshift

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.

ThoughtSpot has developed a way for business people to easily answer their own data questions. Search-driven analytics is based on the concept that finding answers to business questions should be as easy as a basic internet search. With ThoughtSpot, there’s no need for SQL expertise or lengthy training sessions—rather, simple searches are translated into database queries and answers are calculated on-the-fly.

Built as a cloud-native service, Confluent Cloud offers developers a serverless experience with elastic scaling and pricing that charges only for what they stream. Confluent’s Kafka Connect Amazon Redshift Sink Connector exports Avro, JSON Schema, or Protobuf data from Apache Kafka topics to Amazon Redshift. The connector polls data from Kafka and writes this data to an Amazon Redshift database. Polling data is based on subscribed topics.

Google Analytics and Google Ads are popular platforms for customers who need to make data-driven decisions about the performance of their web assets. For prediction, testing, and optimization scenarios, however, customers need a broader and more complete set of analytics. Matillion is an ideal tool to combine the power and convenience of Amazon Redshift and Amazon QuickSight, providing cloud-native data integration tools that make loading and transforming data fast, easy, and affordable.

Fully managed cloud services enable global enterprises to focus on strategic differentiators versus maintaining infrastructure. They do this by creating data lakes and performing big data processing in the cloud. SnapLogic eXtreme allows citizen integrators, those who can’t code, and data integrators to efficiently support and augment data-integration use cases by performing complex transformations on large volumes of data. Learn how to set up SnapLogic eXtreme and use Amazon EMR to do Amazon Redshift ETL.

To help everyone visualize COVID-19 data confidently and responsibly, we brought together APN Partners Salesforce, Tableau, and MuleSoft to create a centralized repository of trusted data from open source COVID-19 data providers. Anyone can work with the public data, blend it with their own data, or subscribe to the source datasets directly through AWS Data Exchange, and then use Amazon Redshift together with Tableau to better understand the impact on their organization.

Mactores used a five-step approach to migrate, with zero downtime, a large manufacturing company from an Oracle on-premises data warehouse to Amazon Redshift. The result was lower total cost of ownership and triple the performance for dependent business processes and reports. The migration tripled the customer’s performance of reports, dashboards, and business processes, and lowered TCO by 30 percent. Data refresh rates dropped from 48 hours to three hours.

Software-as-a-Service (SaaS) presents developers and architects with a unique set of challenges. One essential decision you’ll have to make is how to partition data for each tenant of your system. Learn how to harness Amazon Redshift to build a scalable, multi-tenant SaaS solution on AWS. This post explores trategies that are commonly used to partition and isolate tenant data in a SaaS environment, and how to apply them in Amazon Redshift.

Attribution models allow companies to guide marketing, sales, and support efforts using data, and then custom tailor every customer’s experience for maximum effect. Combined together, cloud-based data pipeline tools like Fivetran and data warehouses like Amazon Redshift form the infrastructure for integrating and centralizing data from across a company’s operations and activities, enabling business intelligence and analytics activities.

Organizations these days are inundated with data. Learn how engineers and analysts can handle the critical challenges of gaining insights from large and complex data sources while also democratizing data for improved adoption across the organization. The Sisense platform simplifies end-to-end data and analytics, reducing time-to-insights by empowering data and IT teams to build advanced data models and perform advanced analysis for their needs.