Category: Analytics

Apache Spark and its parallel processing framework, along with the ease of scaling up in public clouds, have pushed out the limits for data analytics. Learn how Bigstream addresses growing Spark needs, with software that optimizes existing CPU infrastructure and can also seamlessly incorporate advanced programmable hardware. With the same number of servers, Bigstream can accelerate Spark clusters 3x with software alone and 10x when introducing FPGAs.

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.

When it comes to evaluating tools and technologies that focus on customer data, it can be difficult to understand how one tool differentiates from the next. Tealium was one of the first entrants in the Customer Data Platform (CDP) category and helped shape the industry. As such, Tealium is uniquely positioned to help organizations make the most of their data and offer more ways to deliver high-quality customer data to AWS in real-time.

Organizations that prioritize data search and discovery are more productive and innovative. Deploying an intelligent search and discovery system requires organizations to change the way they integrate and curate data using semantic graphs (or knowledge graphs) to build rich search and discovery experiences. MarkLogic Data Hub Service has built-in semantic search capabilities, allowing you to quickly build knowledge graph-based applications.

Today’s enterprises are dealing with more than structured data that’s being exponentially generated. Customers are looking for effective ways to capture, store, curate, and analyze structured data, as well as semi-structured and unstructured data. Learn how Datavard Glue’s extraction capabilities help organizations enable a data-driven culture. Datavard Glue provides integration technology to build a highly scalable, available, secure, and flexible data lake powered by AWS.

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.

With increasing data variety and volumes, it’s become increasingly necessary to ensure all of an organization’s workloads run in the most efficient manner to reduce overall turn-around time and TCO. Get an overview of the data and analytics platform ZS built to streamline and improve contracting analytics for a top life sciences company. Then, dive deep into the data architecture and learn how ZS evolved its data technology stack to get maximum performance.

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.