Over the summer, we introduced the MapR Ecosystem Pack (MEP) which is a natural evolution of our existing software update program that decouples open source ecosystem updates from core platform updates. MEP gives our customers quick access to the latest open source innovations while also ensuring cross-project compatibility in any given MEP version.
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.
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.
MapR 5.2: Getting More Value from the MapR Converged Community Edition
Thank you for using the MapR Converged Community Edition. We hope you have enjoyed great success with your big data projects with the MapR Platform.
Want even more? We recently released version 5.2 of the MapR Converged Data Platform with even more new features. You are welcome to deploy the free Community Edition of the MapR Converged Data Platform in a production environment and take advantage of the free community support. (Paid commercial support is also available.)
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.
NoSQL Application Development with JSON and MapR-DB
NoSQL databases are being used everywhere by startups and Global 2000 companies alike for data environments that require cost-effective scaling. These environments also typically need to represent data in a more flexible way than is practical with relational databases.
Spark’s machine learning (ML) library goal is to make practical machine learning scalable and easy. Decision trees are widely used for the machine learning tasks of classification and regression.
In this Free Code Friday post, I’ll give an overview of machine learning with Apache Spark’s MLlib, and I'll show you how to use decision trees to predict flight delays.
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.
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.
Hadoop is a powerful and cost-effective framework for processing large data sets — but how do you get self-service, real-time access to your data? Leveraging an SQL-on-Hadoop engine and BI tools gives the everyday user access to valuable data without relying on having in-depth knowledge of technical application programming interfaces. Apache Drill is a new project which makes SQL on modern data structures in Hadoop, NoSQL, and more easier than ever.