Big Data and NoSQL

Abstract
The combination between Big Data and NoSQL is one made of inevitability. As data grows larger and larger, the weaknesses in the relational data model are exacerbated. NoSQL technologies grew out of the need for fast query speed and real-time analytics from data sources too large for traditional SQL.
Introduction
A web site running with a large number of users/members will experience the dreaded Big Data Performance Inconsistency. When you need the web site to respond more quickly to a successful, it slackens.
Sites like Facebook, Twitter, and others have struggled with this problem for years as they’ve grown from thousands to millions and now hundreds of millions of users. Inundated by huge amounts of user data, they took advantage of data store technologies like Memcached and Redis to make their sites run fast. But for sites without the engineering resources of companies like Facebook, adopting these technologies has been challenging.
Big data and NoSQL
Big Data company for example Garantia Data addresses above issue. Garantia Data’s cloud-based, in-memory NoSQL solutions make web site run faster. That’s why a number of companies are beta testing Garantia Data’s offering.
NoSQL is often used for storing Big Data. This is a new type of database which is becoming more and more popular among web companies today. Proponents of NoSQL solutions state that they provide simpler scalability and improved performance relative to traditional relational databases. These products excel at storing “unstructured data,” and the category includes open source products such as Cassandra, MongoDB, and Redis.
In-memory means data is stored in computer memory to make access to it faster. Garantia Data deals with some of the fastest data stores available today – Redis and Memcached, both NoSQL databases, entirely served from memory. These products excel at storing “unstructured data,” and the category includes open source products just like in NoSQL above
Cloud computing means computing resources that are delivered as a service, typically over the Internet. The in memory NoSQL database space provided just that challenge. Such Big Data systems handle hundreds of thousands of transactions per second at sub-millisecond latencies.
The Open Source Challenge
Both Memcached, which accelerates existing relational databases like MySQL, and Redis, which is a complete, stand-alone database and acceleration solution, are open source technologies.
While these technologies provide immense capabilities, they also suffer from some key challenges in the areas of reliability, scalability, and manageability. The same limitations apply to commercial solutions such as the native Amazon ElastiCache service for Memcached.
Garantia Data’s solutions, called “Redis Cloud” and “Memcached Cloud” overcome these limitations, by adding to standard Redis and Memcached a breakthrough technological layer called “dynamic clustering.” The technology fully automates all operational processes associated with managing these ultra-fast databases, while enabling infinite scalability with zero data loss reliability. What further distinguishes the solution is the company’s approach to real-time compression for unstructured data. This approach allows Garantia Data to support a price point comparable to
Amazon’s ElastiCache, while maintaining strong margins.
Put simply, you can continue to serve more customers without compromising on speed or having to re-architect. Once you adopt Garantia Data’s solution, there’s nothing more you need to do; it scales automatically.
Accelerating Big Data
Given Bengal and Shoolman’s years of experience with application acceleration and high-speed networking, they have a unique set of insights into what you should look for when evaluating inmemory
NoSQL solutions:
 Automated scalability. The solution should be infinitely scalable in a fully-automated manner. This means you should not deal with nodes, clusters and scaling operations. All that should be automatically done for you.
 No data loss. Memory nodes often crash and when this happens you lose all data stored on them. Your solution must include persistent storage, auto-failover and backup capabilities. Moreover, these processes must be fully automated and guarantee data continuity on failure and zero data loss.
 Uncompromising performance. In some solutions performance is poor when your dataset is small. Make sure your solution uses the strongest servers and preferably processes your data using multiple servers (cores).
 Zero management. Besides creating your database, all other operations tasks (software upgrades, clustering, and failure recovery) should be fully-automated and not require any action on your part.
 Pay-as you go model. Look for a solution that charges according to the actual GB/h used by your dataset. Many solutions charge by the number of cloud servers, which means you end up paying for more than you need.
Conclusion
There are a few in-memory NoSQL solutions out there. Garantia Data’s Redis Cloud and Memcached Cloud offerings, which address many of the key insights above, combined with its unique approach to real-time compression, make the company one to watch.
Reference
http://www.garantiadata.com/ http://www.pragprog.com/titles/rwdata http://bigdatalandscape.com/
http://www.thebigdatagroup.com