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.
Apache Drill has a hidden gem: an easy to use REST interface. This API can be used to Query, Profile and Configure Drill engine.
Today, MapR has announced the developer preview of MapR-DB with native support of JSON, and the new library OJAI (Open JSON Application Interface), pronounced "OH-hy."
A very common use case when working with Hadoop is to store and query simple files (such as CSV or TSV), and then to convert these files into a more efficient format such as Apache Parquet in order to achieve better performance and efficient storage.
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