Big Data Platform for the Cloud Presented by Qubole This presentation was given at a Big Data Warehousing Meetup with Caserta Concepts, MediaMath and Qubole. You can learn more about the event here: http://www.meetup.com/Big-Data-Warehousing/events/228372516/ Event description: At Caserta Concepts, we are firm believers in big data thriving on the cloud. The instant-on, nearly unlimited storage and computing capabilities of AWS has made it the defacto solution for a full spectrum of organizations needing to process large amounts of data. What's more, an ecosystem of value-added platforms has emerged to further ease and democratize the implementation of cloud based solutions. Qubole has developed a great platform for easily deploying and managing ephemeral and long-lived Hadoop and Spark clusters on AWS. Moving Past Infrastructure Limitations: Data Warehousing at MediaMath Over the past year and a half, MediaMath has undertaken a “data liberation” effort in an attempt to leave their bigbox, monolithic data warehouse behind. In this talk, Rory Sawyer, Software Engineer at MediaMath, will describe how this effort transformed MediaMath’s legacy architecture and legacy mindset, which imposed harsh inefficiencies on data sharing and utilization. The current mindset removes these inefficiencies and allows them to say “yes” to more projects and ideas. Rory will also demo how MediaMath uses Amazon Web Services and Qubole so that infrastructure is no longer a limiting factor on what and how users query. This combination allows them to scale their resources up and down as needed while bridging different data sources and execution engines. Using and extending MediaMath’s data warehousing is no longer a privileged activity but an ability that every employee and client has.

1. Big Data Platform
For the Cloud
Presented by Qubole
FEB 16, 2016

2. Big Data Platform for the Cloud
February 16, 2016

3. Qubole provides Big Data as a Service
Accelerate time to value and reduce
Opex/Capex
– Deploy in the cloud, in days not months
while paying only for what you use.
Scale without Life Cycle Management
headaches
– Advanced auto-scaling manages clusters
based on policies and user workload
Maintain ownership of your data
– Data persists in the customer’s Cloud
Storage and doesn't need to move
Let Analysts and Data Scientists reach
results quicker using their tool of choice
– Notebook and Workbench UIs
– Cloud-optimized data processing engines
(Spark, Hive, Presto MapReduce, Hbase,
etc.) as well as SDK integration for
customer’s applications
Reduced governance overhead
– Policy- based administration to deploy self-
service analytics with no ongoing IT
assistance
We help customers get value and insight from data stored in AWS, GCP & Azure

4. Jove Kuang - Solutions Architect - jove@qubole.com
Demo
From building a data warehouse to running analytics

5. • ~6GB of CSV data on AWS S3.
• Build Hive external table on CSV data in place.
• Run data analytics with charts in Notebook with Spark.
Demo Setup
6G of CSV
Files on S3
Hive on Qubole
Spark on Qubole

6. Thank You!