MapR & Cisco Make IT Better

Cisco UCS S-Series and the MapR CDP Architectural Fit

You’re not the only one still loading your data into data warehouses and building marts or cubes out of it. But today’s data requires a much more accessible environment that delivers real-time results. Prepare for this transformation because your data platform and storage choices are about to undergo a re-platforming that happens once in 30 years.

MapR 5.2: Getting More Value from the MapR Converged Data Platform

Thank you for using the MapR Converged Data Platform. We hope you have enjoyed great success with your big data projects with the MapR Platform.

Are your ready to take big data to the next level? We recently released version 5.2 of the MapR Converged Data Platform with even more new features. As we announce end of maintenance for 4.x version of MapR, seize this opportunity to upgrade to the latest version and explore the latest developments in the world of big data.

The Role of Spark in Developing Converged Data Applications

How Spark is Enabling the New Wave of Converged Applications

Apache Spark has become the de-facto compute engine of choice for data engineers, developers, and data scientists because of its ability to run multiple analytic workloads with a single compute engine. Spark is speeding up data pipeline development, enabling richer predictive analytics, and bringing a new class of applications to market.

How Xactly Built a Converged Data Platform with Hadoop, Spark, Solr and More

How to Build a Successful Big Data Infrastructure by Leveraging the Hadoop Ecosystem

Big data presents both enormous challenges and incredible opportunities for companies in today’s competitive environment. To deal with the rapid growth of global data, companies have turned to Hadoop to help them with performing real-time search, obtaining fast and efficient analytics, and predicting behaviors and trends. In this session, we’ll demonstrate how we successfully leveraged Hadoop and its ecosystem components to build a big data infrastructure to meet these needs.

When is the Right Time for Real-Time? Architectural Best Practices for Hadoop

Real-time processing is an important part of your Hadoop architecture, but is it always the best approach to data-driven applications? The reality is that there are a host of situations where real-time not only costs your business unnecessary time and effort, but can also produce erroneous results. Join the experts from MapR and ThinkBig as they delve into the decision making process around Hadoop real-time and batch processes. You will learn the ins and outs of low-latency design for analytics, as well as see how these designs get implemented in the real world.

Best Practices to Deploy a Governed Data Lake

As companies seek to achieve business value from big data via exploratory analytics, the business wants agility to access and explore the data. But agility can turn into chaos if the need for self-service to data isn't balanced with the data security and data governance. It’s not enough to simply load data in Hadoop and put self-service tools in the hands of users – you must consider your data governance and security policies as well. Access permissions and regulatory compliance demand confidentiality safeguards on sensitive data such as personally identifiable information.


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