Big Data is a broad and popular term used to explain the massive volume of both structured and unstructured data that is available. This is another term for really large and complex data sets. Traditional BI / data processing applications are not adequate enough to handle this data – for a number of reasons.
One way of defining Big Data: The 4 Vs of Big Data:
Extremes of Volume or Velocity can still be handled by traditional BI solutions up to a point. However, extremes of Variety or Variability is where Big Data becomes more feasible and attractive.
Any Solution Framework utilizing a Big Data applicable solution needs to consider the following steps and define accurate processes to execute each of these:
- Data Acquisition
- Data Cleansing
- Data Formatting and Aggregation
- Data modelling, Analysis and Access mechanisms
- Data Representation, Interpretation and Application
Each of these steps involves challenges that prevents a “One Solution – Fits All” approach to any Big Data solution. Heterogeneity of data, the massive scale of volume of data, privacy requirements, and visualization requirements – all need to be addressed in unique ways specific to the instance of the problem.
HADOOP is an open-source software framework for storing and processing big data in a distributed fashion on large clusters of hardware operating in parallel. This open-source platform is fast becoming the de-facto standard in most Big Data solutions. There are multiple advantages of using HADOOP over other traditional data storage solutions:
- Cost Effective: This open-source framework is free. HDFS (Hadoop Distributed File System) uses commodity hardware, and shares the cost of the network and hardware it runs on with the MapReduce layers of the Hadoop stack. Zero licensing costs end up being a huge cost savings over traditional SAN or NAS systems.
- Scalable and Flexible: This can work with a very large number of commodity nodes. An HDFS solution can scale up to thousands of nodes without the cost and bandwidth problems associated with traditional systems.
- Very high bandwidth i.e. Fast: HDFS can deliver data at a very high rate. It can easily exceed over 2 gigabits of data transmission into each computer in the compute infrastructure.
- Fault Tolerance and High availability: This system is highly resilient to failure. Data sent to a node is also replicated to other nodes in the cluster, which means that data copies are available for use in the case of failure. The distributed No NameNode architecture provides protection from both single and multiple points of failure.
How can we help you?
We, at ViKi Technologies, pride ourselves in Techno-functional solutions. Our technology solutions are specifically tailored to the vertical domains of our clients. Our solutions are not “One size fits all”. We work with the clients in finding the optimal fit for the client’s needs.
One of our specializations is Big Data solutions. We work in a variety of models and end up choosing the one that best suits our client. We take up enterprise projects, shared service models, or even provide HADOOP staffing services.
Your need is our concern.