The Internet which expected 21 billion devices to be marketed on online before 2020, people are very much involved in knowing about the Value of Event streaming which helps in making real time decision making. Event streaming introduces lots of new components and concepts and also provides a clear architecture of streaming options in each stage. Hadoop Training in Chennai provides detail about the Streaming architecture.
Streaming Architecture – 3 Components
The three major components involved in the streaming architecture are
- The producers
- Streaming system
- The consumers
Big data hadoop training helps in knowing about the streaming architecture and its techniques. Hadoop course in chennai provides complete information about the open data source of producers and consumers.
The producers – It is the system which is based on software that has been connected with the data source. After collecting data from the data source, the event data will be published to the streaming system by the producers, it will be transformed to the desired format by enriching and filtering.
Streaming System – The producers who published the data will be taken which they will persist and delivers to the consumers in a reliable manner.
The consumers – Here the consumers are called as stream processing engines which converts streams to the data and analyse and manipulate the data and insights. We have more options for the data analysis.
Why Hadoop for big data is the important question by most of the consumers. This will be explained by the Streaming system.
Producers : Data sources are collected from the data producers, and it will be converted into the desired format. This data will be published into the streaming platforms known as MapR streams or Apache kafka. For Kafka, The producer is Apache Flume. Data collector streamsets is an up-and-coming data which is worthy.
Apache Flume is one of the best system which is mainly used for aggregating and collecting the enormous amount of data and it has a architecture. From the data sources the event data will be collected by the Flume source. The event data will be put it in the flume sink which is an external repository
Streamsets Data collector is the software which is open source which helps in the data flows operations and development.
STAGE 2 :
STREAMING SYSTEM : MapR streams and apache Kafka are the two important transport systems which will deliver events in billions per second.This will differentiate the Kafka and MapR. Both are the publisher model in which producers are the publisher and consumers are the subscriber.
STAGE 3 :
THE CONSUMERS : Kafka and MapR streams delivering a data in a wide range of sources. Here main concept is processing the engines which helps in increasing the efficiency of source data. The important engines are Apache Flink, Apache Apex, Apache storm, Apache storm streaming.
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