Big-data represents a large and rapidly growing volume of information that is mostly untapped by existing analytical applications and data warehousing systems.

Voice, video, text communication, online purchases, tax information, census statistics, online reviews and social media posts all now count as data.

Organizations across the tech world are interested in capturing and analyzing this as it adds significant value to the decision making process.

Processing loads of data involves complex workloads that push the capabilities of traditional data warehousing and data management technologies and techniques.

Real Time Big-Data
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What exactly is Big-data?

The term, big-data, was coined to refer to an ecosystem of problems and solutions arising from increased inflow of data.

As much as Big-Data may come in many forms, and the data sizes are colossal.  The amount of data being generated on a daily basis has increased substantially, as Google’s Eric Schmidt claims that every two days “we create as much information as we did from the dawn of civilization up until 2003”.

Just how “big” is this data? While most people are used to working with megabytes, gigabytes and terabytes, large companies and government organizations may have to manage petabytes (1,000 terabytes) or exabytes (1,000 petabytes) of data. Approximately 2.5 quintillion bytes of data are created every day. The continued proliferation of smartphones, tablets, and wearable technology across the globe will add some steam to the big-data train.

To top it all, the data is being generated at a rapid pace. If users around the world send out 100,000 tweets per second, billions of sensor data gets generated per minute. This is data generation on a large scale in such a short period of time.

Real Time Big-Data (RTBD)

As the concept has gained popularity, various techniques have evolved to handle (ingest, manage, use, analyze and store) big data. The primary challenge is to receive such large quantity of data, leading to storage, and finally data processing and analysis.

Multiple solutions were discovered to tame big-data into various usages, like the Hadoop ecosystem. Eventually the techniques became quaint, largely due to latency (delay) in data input to output analysis.

Real Time Big-Data is a name given to a more refined and much needed technique of handling big data today.  RTDB, as name suggests, possesses the ability to process and analyze large volumes of data in real time or near-real time.

Here are the key differences between Big Data in Real Time and Non-Real Time.

Differentiation Non Real Time Big Data Real Time Big Data
Data Storage Require data to be stored before it is processed, storage is key element of NRTBD RTBD relies only on a temporary storage in Qs (explained later)
Processing / Computation Works with heavy computation resources ad Queries are implemented on LARGE data sets. Works with many small / micro processing units, often distributed across various processors, can work on small machines
Time to Compute Takes a lot of time to run each discovery job depending on complexity of query. More Complex queries take more time. More variety of information also likewise. Each worker (process) is designed to execute micro job in fractions of a second, hence with multiple micro processes the job gets done within fractions of seconds
Scalability Low in Scalability as with larger volume and variety of data need different techniques to optimize and scale Scales seamlessly over distributed computing environment. Scales faster and uninterrupted With Cloud and Auto-Scaling (like Amazon AWS)

Key Elements of Real Time Big Data

Queues In a high velocity system, with different stages of processing, one stage lagging behind other can result into loss of information. Queuing system implements a mezzanine state of data for temporary storage, while the forward system is busy processing earlier data. Just as the forward system completes processing, more data processing from the queue and the workflow continues.

Examples: AWS Kinesis, Kafka.

Emitters or Spouts These are set of programs, servers or micro services working toward splitting data and submitted them for processing by big-data workers. A spout takes the data and sends it to different processing agents – workers – and acts as data source for the workers.
Workers Workers execute micro tasks/operations on data received from spouts. Workers can be simple programs written in Node JS, Python, Java or they can be realized as a Combined Infrastructure and Program Units like Bolts in Apache Storm or AWS Lambda.

Workers can also be realized as an interconnected network of processes in a grid environment, where One process acts as a Data Source (Spout) for Next Worker and likewise.

Rule Engine A process which implements a simple “IF THIS THEN THAT” function can come in handy with RTBD especially when a variety of data is high and Grid of Spouts and Workers is complex. A rule engine, based on data attributes (and/or CONTEXT) can take handy decisions like splitting data, choosing right worker, choosing right action.

RTBD with RULE ENGINE can form core towards key component of Future AI like self learning/evolving systems.

Real Time Databases RTDb is an extension of databases to “EMIT” an “EVENT/SIGNAL” on any “CHANGE”. This process has added a powerful contribution to Real Time world. In Real Time world it is important to have a forward flow of information i.e. one system completing its job and passing the baton to next in line system for forward working, RTDb comes very handy.

RTDb also allows you to track specific element or macro level changes in database to help you write a forward process on the same. Databases like ReThinkDB claim to have evolved ground up to be RTDb.

Who needs Real Time Big Data?

Practically every industry has the need of RTBD in the near future, be it healthcare, manufacturing, retail, or real estate. RTBD is here to stay.

With the traditional way of handling big-data, businesses had to wait until their data size grew exponentially. However, with RTBD, even if the data has low incremental volumes and varieties (in real time), businesses can gain immediate advantage by deploying its systems.

Simple use cases for RTBD can be tracking sensor data from devices, tracking real time sales numbers, or tracking user behavior on your website in real time. Largely RTBD can help business generate reports, alerts, action triggers, and learning in real time.

Large internet Organizations like PayPal which use big data analytics to complete 13 million transactions processes more than 1.1 PB of data. The organisation has gained much via RTBD technology deployment.

No breakthrough technology can make a large scale impact if it remains expensive, as a result. However, RTBD has made the handling of big-data more affordable and usable for smaller businesses.

Conclusion

Big Data is now in the foreground of business intelligence. Anything that exists in the digital world has the potential to be recorded as data and any organization that finds a way to leverage this virtual ocean of information will gain new opportunities for growth in the real world.

For businesses of any size, success is derived by time, as a result Big Data needs to be embraced now, before it becomes a necessity for survival.

As the consumer appetite is driven by new age technologies,Real Time handling of information is the only way forward. The  traditional way of handling Big Data via storing and analysing may not be possible at all. As a result RTBD is a colossal opportunity for business efficiency, innovation leading to profitable success.

Driven by wide adoption across industries, the worldwide market for big data technology and services is expected to grow to $48.6 billion by 2019.