Hadoop as a service presented by Ajay Jha at Houston Hadoop Meetup

1. Altiscale
Big Data-as-a-Service
Paul Tibaldi RSD & Ajay Jha SA

2. • Market Background
• Who is Altiscale?
• Why are we different/better?
• Hadoop Admin
• Apache Hadoop Stack
• Platform/Access/Demo
• Q/A
2
Big Data As A Service

3. Market Background

4. 4
Interest in Big Data is growing fast

5. 5
Big Data in The Cloud is Accelerating
On-
Premises
32%
Cloud
Only
23%
Cloud
Plus On-
Premises
29%
Source: “Hadoop Expansion Boosts Cloud and Unsupported On-Premises Deployments,” Merv Adrian, Nick Huedecker, 3 September 2015

6. But the journey has dangers
Gartner:
70% of independent
Big Data implementations
will fail to meet revenue
and cost objectives,
through 2018.

7. Who is Altiscale?

8. Altiscale Data Cloud GA in 2014
Financed by top-tier technology investors
Recognized innovator in Hadoop-as-a-Service
About Altiscale

9. About Altiscale
Led by experienced, renowned Hadoop team from Yahoo!
• Raymie Stata, CEO. Former Yahoo! CTO,
well-known advocate of Apache Software Foundation
• David Chaiken, CTO. Former Yahoo! Chief Architect
Built and managed by veterans of Big Data, SaaS, and
enterprise software
• From Google, Netflix, LinkedIn, VMware, Oracle, and Yahoo!
40,000 nodes
500 PB
1,000 users
$ billions at stake
Raymie Stata, CEO David Chaiken, CTO Ricardo Jenez
VP of Engineering
Charles Wimmer
Head of Operations

10. Big data built for speed
Fast time to value—days not months
Easier, faster scalability—with elastic scaling
Operations support—so your jobs get done
Lower TCO—for fast investment payback

11. 11
Unmatched Security
Altiscale is the only provider
that delivers integrated security
encompassing its Big Data platform offering

12. Complete best of breed

13. Big Data is complex.
It gets more complicated as you scale.

14. Big Data-as-a-Service

15. The Altiscale Data Cloud Core

16. Altiscale Data Cloud is 100% based on Apache open source.
Our current Altiscale Data Cloud 4.0 release is composed of the following Apache components and
versions:
• Apache Hadoop 2.7.1
• Apache Spark 1.5*
• Apache Hive (& HCatalog) 1.2
• Apache Tez 0.7.0
• Apache Pig 0.15.1
• Apache Oozie 4.2.0
• Apache Flume 1.5.2
• Avro 1.7.4
• JDK/JRE 7 (Sun/Oracle version)
• HttpFS
In addition to the above, we also support the three latest versions of Spark to our customers. That
allows our customers the options of a conservative approach as well as a the option to work with
the “bleeding edge” fast moving Spark community.
Concurrency with Apache Versioning

17. Hire an expert to take care of the cluster
• Hardware setup and Cluster installation
• Address hardware failure
• Upgrade Hadoop stack
• Tuning config parameters
• yarn-site.xml  ex : yarn.nodemanager.resource.memory-mb
• mapred-site.xml  ex : mapreduce.task.io.sort.mb
• hdfs-site.xml  ex : dfs.blocksize
Hadoop Administration

18. Accessing the cloud

19.  Spark example
• Build Spark code laptop using maven
• Build the jar and copy over Altiscale’s workbench (Gateway) node.
• Launch Spark job on YARN.
• Monitor using Resource Manager
Quick Spark Demo

20. 20
Thank You!