Tag: Data Warehouse
Most enterprise customers are trying to migrate their on-premises data warehouse from Oracle or other on-premises solutions to Amazon Redshift. Learn how TEKsystems strives to put forth a series of tools, technologies, and methodologies to meet customers in their current AWS cloud journey path. With a phased migration approach, TEKsystems’s customer realized immediate savings moving off an on-premises data warehouse while running its current applications against Amazon Redshift through partner solutions.
Many SAP enterprise customers have deployed data lakes to optimize manufacturing outcomes, track business performance, improve forecast, and accelerate product lifecycle management. The initial process of data extraction involves bringing data from commercial off-the-shelf (COTS) applications and non-COTS applications. Learn how the Atos AWS Data Lake Accelerator for SAP helps customers build a scalable data lake for SAP systems such as SAP S4/HANA or SAP ECC.
One of the biggest advantages of VMware Cloud on AWS is that it can readily integrate with other AWS services. That gives you countless ways to elevate your workloads. If you’re amassing data in your databases over time and are looking for novel ways to glean fresh insights out of it, using Amazon Redshift and Amazon QuickSight is an easy and accessible way to achieve it. This post describes how to get more out of existing data residing inside your databases running in VMware Cloud on AWS.
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.
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.
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.
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.
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.
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.
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.