Category: Analytics

Exasol provides a high performance in-memory analytics database to give organizations the power to transform how they work with data. Working with AWS SaaS Factory, Exasol launched their analytics database through a SaaS model and navigated technical and business decisions for launch and beyond. In this Q&A, Peter Jackson, Chief Data and Analytics Officer at Exasol, shares insights on their journey and advice for organizations who are in the process of building SaaS on AWS.

For a specialty pharmaceutical company, the launch of a new drug is of great importance for their commercial success. Thanks to AI capabilities to crunch massive amounts of data in a short period of time, drug development pipelines that would normally take over a decade are being compressed into a matter of months. Learn how Infosys Data & Analytics Practice and AWS have collaborated to develop the Infosys Life Science Commercial Insights Platform that addresses such needs.

The concept of DataOps was born with the goal of solving issues prevalent in old, complex, and monolithic architectures, and to optimize data pipeline architectures. To meet the demand, Cognizant and AWS jointly built the DataHyperloop solution which provides a real-time view of DataOps and demonstrates automation of continuous integration, delivery, testing, and monitoring of data assets moving across the data lifecycle on AWS.

Increasing penetration of analytics in the life sciences industry is expected to drive significant growth for businesses in the coming years. Learn about Accenture’s life sciences data and analytics accelerator which enables customers to respond to these challenges and use data for their competitive advantage. Particular focus is given to the commercial domain and use of analytics to increase customer engagement and optimize sales and marketing.

Customer experience is at its best when a customer perceives the experience offered is unique and aligns to their preferences. The need to engage, at a very personal level, becomes key. Learn how Capgemini’s data and analytics practice implements customer intelligence platforms on AWS to help companies build a unified data hub. This enables customer data to be converted into insights that can be used for reporting and building AI/ML predictive analytics capabilities.

In early August, millions of people took part in the 2021 Census across Australia, providing a comprehensive picture of the country’s economic, social, and cultural makeup. The Australian Bureau of Statistics (ABS) ran the 2021 Census on AWS using an operational insights platform built in partnership with AWS Professional Services, Shine Solutions, ARQ Group, and the ABS. Learn how this tool provided near real-time insights into a very complex logistical activity.

AWS and IBM recently announced that IBM Cloud Pak for Data, a unified platform for data and AI, has been made available on AWS Marketplace. You can easily test, subscribe to, and deploy the Cloud Pak for Data platform on AWS. By running on Red Hat OpenShift and being integrated with AWS services, the platform simplifies data access, automates data discovery and curation, and safeguards sensitive information by automating policy enforcement for all users in your organization.

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.

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.

A serverless architecture strategy reduces complexity and provides more flexibility in adopting the features and non-functional requirements needed to support market agility. In this post, walk through an example of an IoT use case and build a serverless scalable platform using Amazon Kinesis Data Firehose, Amazon Managed Service for Apache Flink, and MongoDB Realm. You’ll learn how easy it is to develop mobile and desktop applications on top of the data platform for different personas.