Tag: Amazon Athena

Running virtual machines with databases or datastores on VMware Cloud on AWS lets you use the same management tools and VMs as on your on-premises VMware vSphere environment. You can easily extend these workloads to the cloud and take advantage of AWS on-demand delivery, global footprint, elasticity, and scalability. Learn how VMware Cloud on AWS brings these datasets closer to AWS Analytics Services, making it easier to use services to draw meaningful insights from business data.

Achieving “speed of thought” or instant analytics on large data sets is a key challenge for business intelligence platforms. Traditionally, data engineers would design and deliver an optimized, aggregated subset of the data to a data warehouse to drive the visualization. This can often take weeks of development and testing or incur significant infrastructure costs. Learn how Indexima uses machine learning and hyper indexes to automate this process and accelerate analytics by up to 1000x across a full data set on Amazon S3.

Amazon Managed Streaming for Apache Kafka (Amazon MSK) is a fully managed, highly available, and secure Apache Kafka service that makes it easy to build and run applications that use Kafka to process steaming data. Learn how to use the new open source Kafka Connect Connector (StreamReactor) from Lenses.io to query, transform, optimize, and archive data from Amazon MSK to Amazon S3. We’ll also demonstrate how to use Amazon Athena to query the partitioned parquet data directly from S3.

Environmental, social, and governance (ESG) factors are increasingly important for financial institutions as they look to assess portfolio risk, meet investment mandates, align with customer values, and report on the sustainability of their portfolios. Working closely with CSRHub, a data provider on AWS Data Exchange, learn how AWS has produced a demonstration to illustrate how customers can analyze company-level ESG scoring data with Amazon QuickSight.

Given the breadth of use cases, data lakes need to be a complete analytical environment with a variety of analytical tools, engines, and languages supporting a variety of workloads. These include traditional analytics, business intelligence, streaming event and Internet of Things (IoT) processing, advanced machine learning, and artificial intelligence processing. Learn how Cazena builds and deploys a production ready data lake in minutes for customers.

Organizations need a comprehensive data management solution that includes data quality, cleansing, de-duplication, and curation capabilities. After consolidating trusted golden records, they need to enforce governance requirements and track changes over time. Semarchy’s xDM platform is an innovation in multi-vector Main Data Management (MDM) that leverages smart algorithms and material design to simplify data stewardship, governance, and integration.

Developing and maintaining an on-premises data lake is a complex undertaking. To maximize the value of data and use it as the basis for critical decisions, the data platform must be flexible and cost-effective. Learn how to build a hybrid data lake with Alluxio to leverage analytics and AI on AWS alongside a multi-petabyte on-premises data lake. Alluxio’s solution is called “zero-copy” hybrid cloud, indicating a cloud migration approach without first copying data to Amazon S3.

AWS customers are increasingly searching for new ways to manage access in a scalable way that maintains the benefits of an agile DevOps delivery model. However, the traditional and highly-manual processes for assessing and certifying access quickly demonstrates they cannot keep up with the speed of DevOps changes. Learn how PwC designs and implements baseline IAM roles for customers while leveraging usage-based analytics to identify overprivileged roles.

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.