What is Big Data?
Volume, Variety and Velocity – the 3 V’s of big data. Probably in all the definitions of big data we have seen till date, the 3 V’s are constant. The reason why big data is booming is because of the increase in the 3 V’s at an alarming rate. Traditional databases cannot solve the issue of high intensity data management due to increasing volume, variety and velocity.
What do the 3 V’s mean in the context of data management :
Volume : The size or sheer volume of data that can be measured. It could range from Gigabytes to Petabytes.
Variety : All your data does not come only one source. The sources may range from websites, external links, social media etc. This is the variety of sources from where data is generated.
Velocity : In today’s world, it is crucial for enterprises to reduce the time from data generation to providing users with actionable insights. Collection, storage, processing and analysis of data has to happen within a short time window – ranging from daily to real time.
Your Need for Big Data
A big data problem definitely needs to be recognized at the earliest and recognizing a big data problem happens only when you think in terms of big data. Organizations today do not realize that they have a big data problem. When existing database management systems and applications cannot keep up with the increased influx of data, organizations can benefit from big data in a big way.
A few challenges that could be faced by not addressing the big data problem are escalating costs, reduced efficiency and productivity. By migrating the existing workloads to technologies woven around big data, organizations can reduces costs and increase operational efficiency.
Why is Big Data Important?
Obtaining data and analyzing it can give you solutions to issues like cost reduction, time management etc. However, the combination of big data and some good analytics can help you solve business related issues as well. i.e.
- Identify problem areas in business in near real time.
- Detecting fraudulent behavior in advance
- Analyze customer buying habits and modify PoS strategy
- Current strategy risk analysis can be done in minutes.
How Does Big Data Work?
The use of big data technologies makes it useful to not only collect and store huge datasets but to analyze them in order to obtain actionable insights. The usual data flow in big data processing is from collection of raw data to obtaining actionable insights.
- Process and Analyze
- Consume and visualize
It starts with the collection of raw data from various sources and a good big data platform enables developers to ingest a variety of data, be it structured or unstructured and at any speed.
A secure, scalable and durable repository is required by a big data platform before processing of data happens.
The transformation of data from its raw state into a consumable format happens in this stage. The use of advanced functions and algorithms makes this possible. It is then stored for further processing or is available for consumption using data visualization or business intelligence tools.
Gaining high value actionable insights from your raw data is what big data is all about. Each analysis will be different and depending on this, statistical predictions (predictive analytics) or recommended action (prescriptive analytics) may be used.
Big Data Processing-The Journey
The big data ecosystem is evolving at a rapid pace and big data has come a long way. From Descriptive analytics – what happened and why?
To a phase where prediction was made possible – Predictive analytics – forecasting, fraud detection etc.
And it went a step further to Prescriptive analytics – recommendation as to what course of action must be taken.
Now that time to insight has become such a big factor, more big data frameworks like Apache Spark, Apache Storm etc. have come up apart from the Hadoop framework. This has happened in order to support real time streaming and data processing.
Making Big Data Work for You at Indium Software :
1. Understanding the customer pain area in the current production system and seeing what will be the ideal choice of technologies to overcome the problem.
2. Discuss and decide the architecture on the black board.
3. Implement the solution on premises or on cloud, based on the client’s feasibility.
4. Develop applications from scratch or integrate the solution in the existing applications.
5. Test and debug the application in a pre-production environment.
6. Upon successful completion on #5, deploy and monitor the cluster and assist on support basis.