Category: Analytics

Feature engineering is a critical process in which data, produced by data engineers, are consumed and transformed by data scientists to train models and improve their performance. Learn how to accelerate data processing tasks and improve collaboration between data science and data engineering teams by applying MLOps best practices from Data Reply and leveraging tools from AWS. Data Reply is focused on helping clients deliver business value and differentiation through advanced analytics and AI/ML on AWS.

Organizations realize the value of data and analytics for their businesses, but not all of them have been successful in defining a mature analytics vision and strategy. By cross-leveraging experience in process management, safeguarding data, and rich analytics practices, Genpact has developed an analytics maturity assessment framework known as the Analytics Maturity Meter (AMM). Learn how this solution evaluates a company’s current capabilities in data, process, technology, talent, and enterprise leadership.

To curb spend and unlock scale, Precisely collaborates with AWS to provide the means to synchronize mainframe data in the cloud at speed and power modern applications. Learn how Precisely Connect integrates data seamlessly from legacy systems into next-generation cloud and data platforms. Using Precisely Connect, data from sequential files, VSAM datasets, or databases such as IMS and Db2 can be transferred to Amazon RDS.

Many companies use online databases for talent sourcing, while others prefer more traditional means such as referrals or networking. Quantiphi’s cloud-native data platform facilitates the convergence of talent and recruiter performance data to provide crucial insights into the talent acquisition process. Explore the critical aspects of Quantiphi’s serverless, fully-managed ETL pipeline along with the benefits of the centralized lake house solution built on AWS in helping talent acquisition companies.

Getting SAP data into AWS in real-time enables insights for better business decisions, realizes competitive advantages, enhances sharing and collaboration, and improves operational performance. It also provides the opportunity to integrate data from SAP and non-SAP sources. Learn how to extract and integrate SAP data on AWS for use cases like analytics, reporting, AI/ML, and IoT in real-time, using the BryteFlow SAP Data Lake Builder on AWS.

SAP Business Technology Platform (SAP BTP) is a unified, business-centric, and open platform for the entire SAP ecosystem. It’s available on AWS and enables users to integrate and create value from data and extend their SAP and third-party solution landscapes to meet evolving business needs. Learn why you should run SAP BTP on AWS and how it benefits you as an SAP customer to get the lowest total cost of ownership and fastest time to value.

Learn how to leverage Apache Iceberg capabilities with Dremio and Amazon EMR Serverless to scale your business by keeping up with various changes to your data and analytics portfolio. Iceberg is a high-performance, open table format for huge analytical tables specifically designed to mitigate the challenges introduced by unforeseen changes observed by enterprises. Dremio is a data lake engine that delivers fast query speed and a self-service semantic layer operating directly against Amazon S3 data.

Graph databases are useful for solving problems related to connected data, and represent data as nodes and enable organizations to uncover relationships between data that’s not possible with other approaches. Experts from AWS and Neo4j explore four types of databases and the most common applications for each: relational, document, in-memory, and graph. We’ll cover how different industries use graph databases and how they work as part of an AWS architecture.

Infor OS is the foundational enterprise application platform which connects Infor’s various software products and third-party solutions into a complete digital business platform. It enables ongoing innovation with support for AI/ML, integration, hyperautomation, application development, data management, and analytics. The platform delivers everything you need to tackle innovation use cases—from integration to automation and extensibility to data and insights.

Decentralized clinical trials (DCTs) put the patient at the center of the trial experience and incorporate digital technologies like AI/ML to address the challenges associated with traditional clinical trials. DCTs can reshape workflows across the clinical lifecycle—from trial design and patient recruitment to evidence generation. Explore key challenges addressed by DCTs and how SourceFuse is leveraging AWS to build the right solutions for its clients to transform clinical research.