The document discusses the rise in popularity of Apache Spark. It highlights the key appealing aspect of Spark as its resilient distributed datasets (RDDs) which allow for fault-tolerant, parallel data structures that can be partitioned and manipulated via operators. Some key differences between Hadoop and Spark clusters are that Spark has its own cluster manager and supports a wider variety of applications by converging SQL, streaming, machine learning and graph analytics workloads. Applications that benefit from Spark include big data analytics and high-performance computing by decoupling from Hadoop.

2. The Rise in Popularity of Apache Spark
With Ian Lumb, Product Marketing Manager
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3. What is the single most appealing
aspect of Apache Spark?

4. 4
Single-most Appealing Aspect

5. 5
Single-most Appealing Aspect
https://spark.apache.org/

6. 6
 Abstraction for in-memory computing
 Fault-tolerant, parallel data structures
• Cluster-ready
 Optionally persistent
 Can be partitioned for optimal placement
 Manipulated via operators
Resilient Distributed Datasets (RDDs)
Zaharia et al., NSDI 2012
http://www.cs.berkeley.edu/~matei/papers/2012/nsdi_spark.pdf

7. What are the key differences between
Hadoop clusters and Spark clusters?

8. 8
Cluster Management

9. 9
Hadoop Distributions

10. 10
Well-managed Clusters
https://spark.apache.org/ http://aryannava.com/2014/02/19/apache-hadoop-ecosystem/hadoopecosystem/

11. 11
Applications

12. What applications benefit from utilizing
Hadoop vs. Spark?

13. 13
Hadoop Limitations
https://tomsitpro.com/

14. 14
Spark’s Converged Applications
http://www.informationweek.com/big-data/big-data-analytics/apache-spark-3-promising-use-cases/a/d-id/1319660

15. 15
 Big Data Analytics
• Combine SQL, streaming, machine learning and graph
analytics
 HPC
• Decouple from Hadoop to easily incorporate with existing
infrastructure
Spark’s converged application play
https://spark.apache.org/

17. www.brightcomputing.com/solutions-hadoop
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