Challenges of real time data vs batch data

Given the constant growth in internet connected devices such as smart phones, household appliances, RFID devices, etc. – all this is leading to massive volumes and variety of data and data traffic. This presents various challenges for the security and management of data-as-a-service application. And, with the development of internet of things (IOT), the processing of large-scale real-time data based on real-time sensor data is soon becoming the focus of most organizations and a nightmare for some.

Challenges faced in Big Data:
  • Data is both structured as well as unstructured, but finding that essential data to make the correct decision is a real challenge
  • The ability to access and propagate that data in any time frame or latency, and the ability to enhance performance through offload processing and multithreading makes it even more difficult
  • Maintaining the scalability and ability to handle large amounts of data without jeopardizing performance & to process more data in the same amount of time
  • Cost control is critical in both real-time as well as batch data
  • To be able to capture data and deliver it in real time, and further to capture the changed data from multiple platforms is what the challenge that real-time data process faces today

Batch data processing is an efficient way of processing high volumes of data transactions collected over a period of time while Real time data processing involves a continual input, process and output of data. In the latter, data must be processed in a small time period (or near real time).

Hadoop with its batch-oriented approach, was relatively easy to implement and also provided great value as prior to Hadoop there was no way to store and process petabytes on commodity hardware using open source software. Hadoop’s MapReduce provided not just the capability to handle big data but also came cheap and “democratized big data”.

Today, the challenge is not just with respect to making big data easy to manage. The challenge lies in how the ROI performs on storing and analyzing petabytes of data and its performance quickly!

To overcome the problem of combining batch processing of big data and real-time data the Lambda architecture was developed.

Lambda architecture combines all three elements –batch, speed and presentation but it is still in its nascent stages. Analyzing data as a stream brings benefits in terms of scalability and flexibility regardless if the data is real-time or historical. But even above this all, the benefit in terms of the simplicity that comes from removing batch from one’s big data processes can ultimately bring advantages to the ROIs.

Once you pipeline the data and constantly receive analytics in real-time, as operations take place, it dramatically brings down cycle time and helps do things much faster. This kind of performance is important to the bottom line- the ROI of an improved customer satisfaction and reduced churn time.

Challenges galore

The real challenge for real-time processing of the massive stream of heterogeneous data is the lack of support for massive real-time data processing framework and implementation techniques. The processing of this real-time stream data is much different from that of static data. It needs to meet the extremely high data throughput and strict real-time requirements.

With the advent of visualization for real time data and analytics, Ultra-large-scale data visualization adds a new complexity. These require a lot of spending in computing and GPU resources. The rewards for these are great as the insights acquired which when acted upon prudently and at the right time, increases the companies competitive edge in the market and thereby increased ROIs (Forrester claims a 66 percent increase in firms’ use of streaming analytics according to a 2014 survey of 740 decision makers).

Other challenges include additional computational problems in handling elasticity by allocating resources on-the-fly to handle increased demand and performance.

We at AgileSoft Systems understand big data problems and challenges and have helped numerous customers solve complex problems.