SPARK & KAFKA ONLINE TRAINING | PROJECT SUPPPORT

    SPARK & KAFKA ONLINE TRAINING.

SPARK

Spark was introduced by Apache Software Foundation for speeding up the Hadoop computational computing software process.
Spark is designed to cover a wide range of workloads such as batch applications, iterative algorithms, interactive queries and streaming. Apart from supporting all these workload in a respective system, it reduces the management burden of maintaining separate tools.
Spark is a general-purpose distributed data processing engine that is suitable for use in a wide range of circumstances. On top of the Spark core data processing engine, there are libraries for SQL, machine learning, graph computation, and stream processing, which can be used together in an application.

MAJOR USES OF SPARK:

• PROCESSING STREAMING DATA.
• MACHINE LEARNING.
• FOG COMPUTING.

                                          KAFKA

Kafka is designed for distributed high throughput systems. Kafka tends to work very well as a replacement for a more traditional message broker. In comparison to other messaging systems, Kafka has better throughput, built-in partitioning, replication and inherent fault tolerance, which makes it a good fit for large-scale message processing applications.

USES OF KAFKA :

• kafka messaging
• Website activity tracking
• kafka metrics
• kafka log aggregations.
• stream processing
• kafka event sourcing
• commit log

APPLICATIONS OF KAFKA :

• Twitter
• LinkedIn
• Netflix
• Mozilla.
• Oracle.