The MapR Persistent Application Client Container (PACC) is a Docker-based container image that includes a container-optimized MapR client. The PACC provides secure access to MapR Converged Data Platform services, including MapR-FS, MapR-DB, and MapR Streams. The PACC makes it fast and easy to run containerized applications that access data in MapR.
In this blog, we describe how to use the Kafka REST Proxy to publish and consume messages to/from MapR Streams. The REST Proxy is a great addition to the MapR Converged Data Platform, allowing any programming language to use MapR Streams. The Kafka REST Proxy, provided with the MapR Streams tools, can be used with MapR Streams (default) as well as Apache Kafka (in a hybrid mode). In this article, we will focus on MapR Streams.
Apache Spark can use various cluster managers to execute applications (such as Standalone, YARN, and Apache Mesos). When you install Apache Spark on MapR, you can submit an application in a Stand Alone mode or by using YARN.
This article focuses on YARN and dynamic allocation, a feature that lets Spark add or remove executors dynamically based on the workload.
Druid is a high-performance, column-oriented, distributed data store. Druid supports streaming data ingestion and offers insights on events immediately after they occur. Druid can ingest data from multiple data sources, including Apache Kafka.
This article will guide you into the steps to use Apache Flink with MapR Streams. MapR Streams is a distributed messaging system for streaming event data at scale, and it’s integrated into the MapR Converged Data Platform, based on the Apache Kafka API (0.9.0)
Get an introduction to streaming analytics, which allows you real-time insight from captured events and big data. There are applications across industries, from finance to wine making, though there are two primary challenges to be addressed.
MapR Streams is a new distributed messaging system for streaming event data at scale, and it’s integrated into the MapR converged platform. MapR Streams uses the Apache Kafka API, so if you’re already familiar with Kafka, you’ll find it particularly easy to get started with MapR Streams.
We live in a world where the combination of Moore’s Law and Metcalfe’s Law heralds a data revolution. The billions of smartphone and broadband users today already generate massive quantities of data.
Streaming data is of growing interest to many organizations, and most applications need to use a producer-consumer model to ingest and process data in real time. Many messaging solutions exist today on the market, but few of them have been built to handle the challenges of modern deployment related to IoT, large web based applications and related big data projects.
In this week's whiteboard walkthrough, Tugdual Grall, technical evangelist at MapR, explains the advantages of a publish-subscribe model for real-time data streams.
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