AWS Partner Network (APN) Blog

Tag: Data Warehouse

How to Create a Continually Refreshed Amazon S3 Data Lake in Just One Day

Data management architectures have evolved drastically from the traditional data warehousing model, to today’s more flexible systems that use pay-as-you-go cloud computing models for big data workloads. Learn how AWS services like Amazon EMR can be used with Bryte Systems to deploy an Amazon S3 data lake in one day. We’ll also detail how AWS and the BryteFlow solution can automate modern data architecture to significantly accelerate delivery and business insights at scale.

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Driving Hybrid Cloud Analytics with Amazon Redshift and Denodo Data Virtualization

A data integration architecture that can virtually connect multiple data platforms provides business users with immediate access to data, with far less IT friction than traditional methods, so you can make faster, more data-driven decisions. The Denodo Platform for AWS can aid organizations in managing their data by providing an alternative data integration method. With Denodo, data is presented in real-time and without the need to replicate data to a new consolidated repository.

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Datacoral_AWS Solutions

Building Serverless Data Pipelines on Amazon Redshift By Writing SQL with Datacoral

Amazon Redshift is a powerful yet affordable data warehouse, and while getting data out of Redshift is easy, getting data into and around Redshift can pose problems as the warehouse grows. Datacoral is a serverless data platform that manages metadata changes, data transformations, and orchestrating pipelines for data consumers. In this post, learn how to write Redshift SQL to represent data flow, and how serverless data pipelines get automatically generated for that data flow.

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Heimdall Data_AWS Solutions

Using the Heimdall Proxy to Split Reads and Writes for Amazon Aurora and Amazon RDS

Horizontally scaling a SQL database involves separating the write-master from read-only servers. This allows the write server to perform dedicated write operations rather than processing redundant read queries. However, writing to one node and reading from another can result in inconsistent data due to synchronization delays. Heimdall Data offers a database proxy to help developers and architects achieve optimal scale from their Amazon RDS and Amazon Aurora environment without any application changes.

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Cognizant_AWS Solutions

Accelerating Data Warehouse Migration to Amazon Redshift Using Cognizant Intelligent Data Works

Many organizations are looking to migrate existing, on-premises enterprise data warehouse systems to cloud-based data warehouse systems such as Amazon Redshift. Here, we discuss how Cognizant’s Intelligent Migration Workbench (IMW) can be used to accelerate the data warehouse migrations while converting Oracle PL/SQL and Tetradata BTEQ scripts. IMW makes it easy to move mission critical proprietary code to AWS, giving customers competitive edge through faster time to market.

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Heimdall Data_AWS Solutions

Improving Application Performance with No Code Changes Using Heimdall’s Database Proxy for Amazon Redshift

Rewriting an application code for performance optimization generally requires a significant amount of effort. Also, IT and development groups using third-party applications like Tableau may not have access to the application code. Heimdall’s database proxy solution offers a flexible and cost-effective alternative to rewriting your application for performance and scale. Heimdall transparently provides SQL control and visibility to the application owner without (re)writing a single line of code.

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