Big Data Hadoop Certification Training
- 160k Enrolled Learners
- Live Class
Contents of the Webinar
1. Low Latency
2. Streaming support
3. Machine Learning and Graph
4. Data Frame API Introduction
5. Spark Integration with Hadoop
Similar to Hadoop, Spark is a framework as well. In the image below, Spark core is a processing engine which is the core spark API, that is internally written in Scala.
Spark cuts down read/write I/O to Disk
Spark stores its data in the form of RDDs and they’re nothing but in memory collection of the data which are distributed across the machines, however, there are limitations. The unique feature of spark is it stores data depending on the kind of infrastructure.
Used for processing real-time streaming data.
It uses the D-stream: A series of RDDs, to process the real-time data support.
Cyclic Data flows
1. All jobs in Spark comprise a series of operators and run on a set of data.
2. All the operators in a job are used to construct a DAG.
3. The DAG is optimized by rearranging and combining operators where its possible.
Data frame features
Ability to scale from KBS to PBS.
Support for a wide array of data formats and storage systems.
Seemless integration with all big data tooling and infrastructure via spark.
Mesos Vs YARN
Mesos and YARN are resource managers. YARN is popular because of Hadoop, mesos is not, although its functionality is the same.
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