AWS Partner Network (APN) Blog

Tag: Data Warehouse

Esri-AWS-Partners-1

Leveraging the Power of Esri’s ArcGIS Enterprise Through Amazon Redshift

Esri and AWS are extending their collaboration through an extensive integration of product suites and services. This includes Amazon QuickSight leveraging Esri basemap tiles through Amazon Location Service. Esri also supports ArcGIS Enterprise on Kubernetes running with Amazon EKS, and interoperability with Amazon Redshift. Esri‘s GIS solutions create, manage, analyze, and map various types of geospatial data. Their flagship GIS mapping software, ArcGIS, is a powerful mapping and spatial analytics technology.

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Deloitte-AWS-Partners

Managing the Evolution of an Amazon Redshift Data Warehouse Using a Declarative Deployment Pipeline

Enterprise data warehouses are complex and consist of database objects that need to be modified to reflect the changing needs of business, data analytics, and machine learning teams. In this post, learn about an approach to managing the evolution of enterprise-scale data warehouses based on the experience of Deloitte’s Data and AI global practice teams. The declarative tool developed by Deloitte that can automatically generate DDL statements to align Amazon Redshift’s state to an approved baseline configuration.

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AtScale-AWS-Partners

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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Infosys-AWS-Partners

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.

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How to Enable 360-Degree Analytics and Innovate Faster on AWS with Datavard Glue for SAP

Today’s enterprises are dealing with more than structured data that’s being exponentially generated. Customers are looking for effective ways to capture, store, curate, and analyze structured data, as well as semi-structured and unstructured data. Learn how Datavard Glue’s extraction capabilities help organizations enable a data-driven culture. Datavard Glue provides integration technology to build a highly scalable, available, secure, and flexible data lake powered by AWS.

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Heimdall-Data-AWS-Partners

Advanced Connection Pooling with the Heimdall Proxy

As databases are often a key component of internet infrastructure, IT departments may be challenged by poor connection management from the application. The Heimdall Proxy helps developers, database administrators, and architects horizontally scale out and optimize connections through connection pooling for Amazon Amazon RDS and Amazon Redshift without any application changes. As a result, you will reduce your database instance size and support higher user counts.

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Confluent-AWS-Partners

Accelerate Data Warehousing by Streaming Data with Confluent Cloud into Amazon Redshift

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.

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Best Practices from Onica for Optimizing Query Performance on Amazon Redshift

Effective and economical use of data is critical to your success. As data volumes increase exponentially, managing and extracting value from data becomes increasingly difficult. By adopting best practices that Onica has developed over years of using Amazon Redshift, you can improve the performance of your AWS data warehouse implementation. Onica has completed multiple projects ranging from assessing the current state of an Amazon Redshift cluster to helping tune, optimize, and deploy new clusters.

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Snowflake-AWS-Partners

Analyze Streaming Data from Amazon Managed Streaming for Apache Kafka Using Snowflake 

When streaming data comes in from a variety of sources, organizations should have the capability to ingest this data quickly and join it with other relevant business data to derive insights and provide positive experiences to customers. Learn how you can build and run a fully managed Apache Kafka-compatible Amazon MSK to ingest streaming data, and explore how to use a Kafka connect application to persist this data to Snowflake. This enables businesses to derive near real-time insights into end users’ experiences and feedback.

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Enabling Customer Attribution Models on AWS with Automated Data Integration

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.

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