With the information explosion and the mercurial growth of social media and content, the appetite for intelligence is growing at an alarming rate. Every organization is on a mission to make sense of the explosion of data through Web traffic, twitter tweets, mobile SMS, social network comments, geo-spatial data, as well as software and sensors that monitor shipments, air traffic, logistics, suppliers and customers — to guide decisions, trim costs and lift sales.
Conventional data processing systems are not sufficient or capable of processing such huge volumes of data in short time. The big data is really big, moves fast and often does not fit the database architectures. We need to think and act different to harness big data. New cost effective approaches have emerged to tame the data explosion and derive patterns, correlations, and hidden meaning to empower the organization to be competitively advantaged. The key to big data is the ability of the organization to respond to their market with confidence but usually at fantastic cost.
The key to reducing cost is in the ability to leverage cloud. The nature of cloud computing lends itself to elastic scaling and pay for what you use. With cloud computing the guesswork is reduced and often the quality of the implementation is often driven by the SLA of the cloud service providers.
Step Ahead’s Big Data Services Frameworks
Step Ahead’s deals with Big Data projects in a disciplined manner. First our approach to management of data volume in terms of how we store, scale and archive along with security, governance and data quality then comes the rate of data velocity and finally how we optimize and infer intelligence from variety of data.
Management of Data Volume: Big data is in all forms and types and comes at a great speed often out growing conventional IT infrastructure in no time! It requires scalable or better still elastic cloud type storage and distributed approach to querying. Conventional relational database infrastructures cannot cope with big data, massively parallel processing architectures such as Greenplum or Hadoop as it places no conditions on the structure of the data it can process. At its core, Hadoop is a platform for distributing computing problems across a number of servers. It implements the MapReduce approach pioneered by Google in compiling its search indexes. Hadoop’s MapReduce involves distributing a dataset among multiple servers and operating on the data which is the “map” stage. The partial results are then recombined into the “reduce” stage. To store data, Hadoop utilizes its own distributed file system, HDFS, which makes data available to multiple computing nodes. A typical Hadoop usage pattern involves three stages:
Load data into HDFS,
MapReduce operations, and
Retrieve results from HDFS.
Hadoop is typically useful in drawing correlations, understanding behaviors or identifying patterns like the social media sites who are the major consumers of Hadoop – store the core data in a database and search Hadoop for behaviors, likes, interests and then transfer back the results to reflect in your social media pages as statistical numbers. Google, Facebook, Myspace, etc all use Hadoop.
Velocity of Data: The key to the above analysis is the velocity of data. How soon the data gets in and how quickly you are able to interpret it? It does not matter whether you bulk the streaming data for later batch processing or are able to compile large histories of customers’ every click and interaction and not just the final sales or customer’s choice. The competitive advantage belongs to those who are able to process that data and infer intelligent information to be ahead in their competitive landscape. The key to velocity of data is not only in the data inputs, but also in the outputs. Therefore, optimizing your feedback loop is where Step Ahead lays emphasis on to help companies using Big Data to stay competitive.
Variety of Data: Data comes in all forms. For long the organizations were driven to optimize and report from structured data content often fulfilling the needs of their relational data systems. Today, due to the explosive growth in Social Media, email, blogs, Smart Phones and mobile devices, people communicate and share information in all forms. Intelligence has now diverged into a variety of data sources. Unless we have a system that is able to integrate data from variety of data sources both structured and unstructured data into one comprehensive reporting analytics platform, competitive advantage cannot be truly realized. The challenge is how effectively we use both SQL based Relational Databases for structured data as well as NoSQL databases for unstructured data to a certain extent and derive value.