Tag: AWS Glue

Many organizations have started applying machine learning and artificial intelligence expertise to scale customer communications and accelerate research during the COVID-19 pandemic. Onica has been actively involved in these efforts, leveraging AWS technologies to help decision makers navigate this pandemic. In this post, dive into the technical details of two COVID-19-related solutions Onica has produced and learn about their results and impact.

Scaling doctors’ expertise through artificial intelligence (AI) and machine learning (ML) provides an affordable and accurate solution, giving millions of infants equal access to eye screening. Learn how Pr3vent, a medical AI company founded by ophthalmologists, teamed up with AWS Machine Learning Competency Partner Provectus to develop an advanced disease screening solution powered by deep learning that detects pathology and signs of possible abnormalities in the retinas of newborns.

Organizations often need business analysts and citizen data scientists to work with data scientists to create machine learning (ML) models, but they struggle to provide a common ground for collaboration. Newly enriched Dataiku Data Science Studio (DSS) and Amazon SageMaker capabilities answer this need, empowering a broader set of users by leveraging the managed infrastructure of Amazon SageMaker and combining it with Dataiku’s visual interface to develop models at scale.

Systems are growing more complex, cloud applications are growing in adoption, and cloud data lakes are being increasingly deployed. At the same time, organizations need to implement data cataloging solutions to provide data governance, data analytics, or pure metadata management. Informatica Enterprise Data Catalog (EDC) scans and catalogs an enterprise’s data assets, whether hosted on the cloud or stored on-premises.

Data lakes have evolved into the single store-platform for all enterprise data managed. On AWS, an integrated set of services are available to engineer and automate data lakes. A data lake on AWS is able to group all of the previously mentioned services of relational and non-relational data and allow you to query results faster and at a lower cost. Learn how nClouds used code automation via AWS CloudFormation to create a dynamic data lake stack to visualize and analyze the financial market data.

One of the biggest challenges in moving to the cloud for organizations that collect and process personally identifiable information (PII) is the fundamental change to the trust model. SecuPi minimizes changes to the trust model and reduces the risk associated with digital transformations. Learn how SecuPi can help you collect and process sensitive or regulated PII and reduce barriers to cloud adoption while satisfying the trust model requirements of even the most conservative and risk-averse companies.

Although many SQL engines allow tools to query Amazon S3 data, organizations face multiple challenges, including high latency and infrastructure costs. Learn how Dremio empowers analysts and data scientists to analyze data in S3 directly at interactive speed, without having to physically copy data into other systems or create extracts, cubes, and/or aggregation tables. Dremio’s unique architecture enables faster and more reliable query performance than traditional SQL engines.

A big challenge for enterprises is how to manage the growth of data in a cost-effective manner. CIOs are also looking for ways to get insights out of the data so their organizations can create actionable outcomes. Learn how the CloudArchive Direct feature of Cohesity’s DataPlatform with AWS analytics services to drive insights into customers’ NAS data. Cohesity is redefining data management to lower TCO while simplifying the way businesses manage and protect their data.

AWS introduced Amazon EventBridge partner event source integrations to showcase reference architectures and end-to-end use cases that help you get started quickly with integrating SaaS partners in your own applications. Fully open source, these solutions include code and AWS Serverless Application Model (SAM) templates that can be customized and extended to fit your application’s needs. The reference architectures include AWS Quick Starts and GitHub code to ease integration.

Change Data Capture (CDC) is the technique of systematically tracking incremental change in data at the source, and subsequently applying these changes at the target to maintain synchronization. You can implement CDC in diverse scenarios using a variety of tools and technologies. Here, Cognizant uses a hypothetical retailer with a customer loyalty program to demonstrate how CDC can synchronize incremental changes in customer activity with the main body of data already stored about a customer.