Tag: AWS Competency Partners

Financial services customers face new industry regulations that require additional compute capacity up to 10 times to meet their regulatory demands. Learn about new service integrations and functionality that make TIBCO DataSynapse GridServer on AWS a great match to run grid computing workloads. These integrations help customers scale their grid workloads to take advantage of the compute elasticity and flexibility of AWS and GridServer without up-front capital expenditure.

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.

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.

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.

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.

In the data and analytics world, many customers have learned how expert AWS Partners can help them rethink and modernize their data architectures, or migrate and manage data warehouses, data-driven applications, and governed data lakes on AWS. To set your data and analytics business apart, it’s important to leverage the AWS Partner Network (APN) and our various differentiation programs. Whether you are just beginning to build or expand your AWS-based business, we offer programs to help you succeed.

With the proliferation of different, constantly changing applications, it’s become a challenge to innovate quickly while having a strategy in place to prevent ballooning storage costs. Legacy archival and analytic tools often restrict developers and diminish the benefits of NoSQL’s handling of modern, rich data sources. Learn how MongoDB is committed to making it easier for customers to scale and transform their data management solutions to make developers’ lives easier.

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.

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.