SQL-on-Hadoop : Is SQL the next big step for Hadoop?

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Aug 012013

Since early days the Hadoop community has made several attempts to stretch Hadoop beyond its role as a distributed programming framework. The key strength that Hadoop brings to the table is its ability to scale linearly. Can we combine this advantage of Hadoop with the efficiency of databases? What does it take to run SQL over Hadoop?

Running SQL-on-Hadoop implies accessing data from “within” Hadoop using SQL as the interface. Accomplishing this demands a significant re-architecture of the storage and compute infrastructures.

SQL-on-Hadoop also shifts Hadoop’s role from being a technology, viewed so far as complementary to databases into something that could compete with them. Its perhaps the single most significant feature that will help Hadoop find its way into more enterprises.

This will be highlighting some conceptual ideas of the different ways that SQL processors can be implemented atop Hadoop. I’ll be taking some examples of OSS and Research-ware products.


Srihari SQL HadoopSrihari currently heads the technology organization for ThoughtWorks India. He’s been a developer and architect for several enterprise applications with focus on building large scale systems based on service oriented architectures, domain specific languages etc. He is passionate about distributed systems and databases.

Using Graph Databases For Insights Into Connected Data

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Jul 162013

Graph databases address one of the great macroscopic business trends of today: leveraging complex and dynamic relationships in highly connected data to generate insight and competitive advantage. Whether we want to understand relationships between customers, elements in a telephone or data center network, entertainment producers and consumers, or genes and proteins, the ability to understand and analyze vast graphs of highly connected data will be key in determining which companies outperform their competitors over the coming decade. In this session, I am going to cover graph database concepts mainly w.r.t Neo4j.

  1. High level view of Graph Space
  2. Power of Graph Databases
  3. Data Modeling with Graphs
  4. Cypher : Graph Query language
  5. Building a Graph Database Application
  6. Graphs in Real World / Common Use cases
  7. Predictive Analysis with Graph Theory

Gagan Agarwal is a Sr. Consultant at Xebia IT Architects. I have around 8 years of experience in Software industry and have worked in domains like e-Governance, Document and Content Management, Customer Communication Management, Media Buy Management etc. I have mainly worked on Java/J2EE and related technologies. I have been speaker at Indic threads conference and an active blogger. In my free time I love to explore new technologies and keep my self updated with latest trend.