AWS Partner Network (APN) Blog

Tag: Amazon S3

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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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Enabling Digital Automation in Intelligent Document Processing (IDP) for Public Sector Partners and Customers Using AWS AI

Learn about the AWS AI services stack for government agencies and partners to develop intelligent automation solutions to extract information from digitalized paper documents. Intelligent Document Processing (IDP) is a solution that enables extraction and processing of specific data elements from documents using AI and machine learning techniques. AWS services that add AI/ML intelligence to IDP solutions include Amazon Textract, Amazon Comprehend, Amazon Augmented AI, and Amazon Kendra.

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Enabling Security and Compliance in an AWS-Based Big Data Analytics Platform Using Cattle Server Automation and IaC

This post describes a solution created by IBM during the migration of a big data and analytics platform for one of the top 10 banks worldwide. The primary drivers were cost efficiency, business agility, and performance. The “pet to cattle” concept was applied to this solution to transform the legacy high availability disaster recovery solution to a more robust and cost-effective cattle-based solution through the use of AWS-native services.

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How an AWS Lambda Function Can Be Integrated with Box Webhooks

There are multiple use cases where applications running on AWS may require a Lambda function to process the file stored in Box storage before it’s sent to the next stage for further processing. This post explains how a Lambda function can be invoked on the upload of a file in a Box folder using a webhook. With the help of a Box webhook and Amazon API Gateway, users can invoke a Lambda function on file upload operation and download the file to Amazon S3.

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How Verisk Argus Migrated Petabytes of SQL Server Data to AWS

Verisk provides data analytic insights to customers in insurance, energy and specialized markets, and financial services. Learn how a joint team from AWS and Verisk Argus migrated petabytes of SQL Server data from on-premises to Amazon S3 and Amazon S3 Glacier using AWS Snowball Edge and the Amazon EMR custom Spark ingestion framework. Through this migration framework, Verisk Argus is positioned to save millions of dollars by moving to AWS from their data center.

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Solving the Challenge of Customer Verification and Security with Digital Onboarding

Customer onboarding remains a challenging and time-consuming process for most banks. Both digital and traditional processes are often overly complex, resulting in lower conversion rates and higher cost of acquisition. To overcome these challenges, numerous financial institutions have started customer onboarding online. In this post, walk through the use case of one of the largest financial institutes of Europe for whom Infostretch provided a substantial breakthrough to onboard the customers digitally.

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From Data Chaos to Data Intelligence: How an Internal Data Marketplace Transforms Your Data Landscape

The concept of an Internal Data Marketplace (IDM) is increasingly resonating with data organizations. An IDM is a secure, centralized, simplified, and standardized data shopping experience for data consumers. Explore how the IDM framework includes data governance and data catalogs, role-based access controls, data profiling, and powerful contextual search to easily identify the most relevant data. The end result is a seamless data consumption experience for end users.

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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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Leveraging Amazon Rekognition and Amazon Comprehend on Dataiku Data Science Platform

Dataiku orchestrates the entire machine learning lifecycle and makes it accessible to data scientists and analysts alike. With deep integration with AWS AI tools, Dataiku enables users to augment their analytics workflow with pretrained NLP and computer vision models. Learn how you can use Amazon Comprehend and Amazon Rekognition plugins on Dataiku Data Science Studio (DSS) to build a simple workflow of NLP and computer vision use cases, respectively.

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How to Simplify Machine Learning with Amazon Redshift

Building effective machine learning models requires storing and managing historical data, but conventional databases can quickly become a nightmare to regulate. Queries start taking too long, for example, slowing down business decisions. Learn how to use Amazon Redshift ML and Query Editor V2 to create, train, and apply ML models to predict diabetes cases for a sample diabetes dataset. You can follow a similar approach to address other use cases such as customer churn prediction and fraud detection.

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