AWS Partner Network (APN) Blog

Tag: ETL

Matillion-AWS-Partners

How Matillion Multiplies the Performance of Complex ETL Jobs with Amazon Redshift Materialized Views

Amazon Redshift recently announced support for materialized views, which lead to significantly faster query performance on repeatable query workloads. That, in turn, reduces the time to deliver the datasets you need to produce your business insights. Matillion ETL for Amazon Redshift provides comprehensive enterprise-grade features to simplify and speed up building and maintaining these pipelines.

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Matillion-AWS-Partners

Quickly Visualize Marketing Analytics and Ads Data with Matillion, Amazon Redshift, and Amazon QuickSight

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.

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Cognizant-AWS-Partners

Migrating ETL Operations from SSIS Packages to AWS Lambda Functions

Many Windows solutions have used Microsoft SQL Server Integration Services (SSIS) as a method for performing ETL operations. Legacy SSIS packages that have been handed down to different developers over the years can be complex, cumbersome, and difficult to support. See an architecture and several techniques Cognizant uses to help clients with ETL operations based on legacy SSIS packaging move their applications to the AWS Cloud.

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SnapLogic-AWS-Partners

How SnapLogic eXtreme Helps Visualize Spark ETL Pipelines on Amazon EMR

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.

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How Kloia Helped GoDataFeed Modernize Monolithic .NET Applications with AWS Serverless

Many enterprises have .NET Framework legacy applications they need modernize and convert to cloud-native. One of the largest product feed automation and optimization platforms, GoDataFeed has a back-end platform that ingests millions of items of catalog data from multiple sources. Learn how Kloia addressed GoDataFeed’s challenges by transforming their legacy .NET Framework monolithic application into a .NET Core-based decoupled architecture.

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Accenture-AWS-Partners

Optimizing Supply Chains Through Intelligent Revenue and Supply Chain (IRAS) Management

Fragmented supply-chain management systems can impair an enterprise’s ability to make informed, timely decisions. Accenture’s Intelligent Revenue and Supply Chain (IRAS) platform integrates insights generated by machine learning models into an enterprise’s technical and business ecosystems. This post explains how Accenture’s IRAS solution is architected, how it can coexist with other ML forecasting models or statistical packages, and how you can consume its insights in an integrated way.

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Mactores-AWS-Partners

How Mactores Tripled Performance by Migrating from Oracle to Amazon Redshift with Zero Downtime

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.

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Running SQL on Amazon Athena to Analyze Big Data Quickly and Across Regions

Data is the lifeblood of a digital business and a key competitive advantage for many companies holding large amounts of data in multiple cloud regions. Imperva protects web applications and data assets, and in this post we examine how you can use SQL to analyze big data directly, or to pre-process the data for further analysis by machine learning. You’ll also learn about the benefits and limitations of using SQL, and see examples of clustering and data extraction.

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Machine Learning-4

How to Use Amazon SageMaker to Improve Machine Learning Models for Data Analysis

Amazon SageMaker provides all the components needed for machine learning in a single toolset. This allows ML models to get to production faster with much less effort and at lower cost. Learn about the data modeling process used by BizCloud Experts and the results they achieved for Neiman Marcus. Amazon SageMaker was employed to help develop and train ML algorithms for recommendation, personalization, and forecasting models that Neiman Marcus uses for data analysis and customer insights.

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Xplenty_AWS-Competency

How to Use Xplenty with AWS KMS to Provide Field-Level Encryption in ETL Data Processing

Enterprises often choose to mask, remove, or encrypt sensitive data in the ETL step to minimize the risk of sensitive data becoming stored, logged, accessible, or breached from their data lake or data warehouse. Xplenty’s ETL and ELT platform allows customers to quickly and easily prepare their data for analytics using a simple-to-use data integration cloud service. Xplenty’s global service uses AWS KMS to create and control the keys used to encrypt or digitally sign your data.

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