Spark is an open source cluster computing system that provides primitives for fast in-memory processing of big data. It runs on top of Apache Mesos for efficient resource sharing. Shark is Hive built on Spark, allowing SQL queries over big data with low latency by running Hive queries using Spark's in-memory primitives. The presentation will demonstrate how Spark and Shark can provide low latency access to big data through in-memory processing and compare the performance of Shark to Hive.

1. Low Latency Access of Big data using Spark and Shark
by Pradeep Kumar G.S

2. Objective
This presentation will cover the technology which enables
us to access Big data with low latency access.
Combines In-memory datastore and Massive data crunch
together.

3. Spark
Spark is an open source cluster computing system that
aims to make data analytics fast — both fast to run and
fast to write.
To run programs faster, Spark provides primitives for in-
memory cluster computing: your job can load data into
memory and query it repeatedly much more quickly than
with disk-based systems like Hadoop MapReduce.
Its build on top of Apache Mesos which is a cluster manager
that provides efficient resource isolation and sharing across
distributed applications

4. Shark
Shark - Hive on Spark.
Shark has majority of features what hive has like:
● SERDE
● UDF
we will see the comparison of Shark Vs Hive Vs Impala

5. Performance Comparison of Hive Vs
Shark.

6. Use Cases

7. More on Session