Video

A Better Way to Build a Fraud Detector: Streaming Data and Microservices Architecture | Whiteboard Walkthrough

In this Whiteboard Walkthrough Ted Dunning, Chief Application Architect at MapR, provides some pointers for building better machine learning models, including the advantages of data streams and microservices style design in the example of a credit card fraud detector, the need for metrics, and how reconstruction of data from an auto-encoder can serve as a figure of merit that helps identify good models.

Kafka Connect on MapR | Whiteboard Walkthrough

In this Whiteboard Walkthrough, Ankur Desai, Senior Product Marketing Manager at MapR, describes how Apache Kafka Connect and a REST API simplify and improve agility in working with streaming data from a variety of data sources including legacy database or data warehouse. He also explains the differences in this architecture when you use MapR Streams versus Kafka for data transport. 

Additional resources:

Open Source Innovations in the MapR Ecosystem Pack 2.0

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.

Watch this webinar to learn more about the new open source project enhancements in MEP 2.0. We cover:

How Spark is Enabling the New Wave of Converged Cloud 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, general-purpose compute engine.

But is Spark alone sufficient for developing cloud-based big data applications? What are the other required components for supporting big data cloud processing? How can you accelerate the development of applications which extend across Spark and other frameworks such as Kafka, Hadoop, NoSQL databases, and more?

Watch this webinar to:

Streaming with MapR

MapR Streams is a global publish-subscribe event streaming system for big data. It connects data producers and consumers worldwide in real-time, with unlimited scale. Publishers (data producers) write data to one or more topics in MapR Streams. Subscribers (data consumers) to the topic can read the data instantaneously, anywhere across the globe.

MapR: Converged Advantages in the Cloud | Whiteboard Walkthrough

In this Whiteboard Walkthrough, Ted Dunning, Chief Application Architect at MapR, describes advantages of MapR Converged Data Platform and how they work in the cloud. With files, tables and streams engineered into the same technology, MapR has particular advantages for multi-tenancy in the cloud including common pathnames and common security.

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MapR: Big Data in the Cloud | Whiteboard Walkthrough

In this Whiteboard Walkthrough, MapR Chief Application Architect, Ted Dunning, explains how special capabilities such as mirroring, bi-directional stream and table replication and control of data locality make MapR particularly effective in cloud computing, whether you use cloud-to-cloud clusters or a hybrid of cloud and on-premise. Ted also explains how cloud bursting is a useful strategy for elastic work loads.

MapR Ecosystem Packs for Updating Ecosystem Components | Whiteboard Walkthrough

In this week’s Whiteboard Walkthrough, Rachel Silver, Ecosystem Product Manager at MapR, talks about MapR Ecosystem Packs or MEPs that give you a convenient way to upgrade open source ecosystem components without having to upgrade the core MapR platform.  The open source components in MEPs have been tested to be functionally interoperable within the MEP so that you can spend more time processing/analyzing data and less time troubleshooting your stack.

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