How to Use Data Streaming for Big Data

by | Jan 12, 2018 | Big Data

8 Min Read. |

Data is inevitable in today’s world. Its importance has made corporate companies and startups to pause their operations and reinvest in data analytics for optimized performances.

However, with more enterprises increasingly waking up to the value of data and the significant impact it can bring into a business, there has been a demand for instantaneous data processing techniques such as Data Streaming that is capable of delivering results in real-time.

Data Streaming

For instance, consider the online financial services portals that calculate EMI, mutual fund returns, loan interests, and others. Such websites take in data and give you results on the returns you are likely to get from different mutual fund companies, the market conditions and tons of other details you would need to make an informed decision.

All of this happens in a fraction of a second and takes into consideration sets of values and your information to deliver the results you were looking for. This is called data streaming and is one of the process’ simplest examples.

A Simple Definition of Data Streaming

Removing all the technicalities aside, data streaming is the process of sets of Big Data instantaneously to deliver results that matter at that moment. With this process, users get real-time information on something they are looking for and help them make better decisions.

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In data streaming, it is the data in motion that is processed over different clusters of servers before they are actually stored in a disk. The data is sent in chunks of the size of kilobytes and processed per record.

Analytics happens simultaneously, and by the time you see your results, tons of operations, filtering, sampling techniques, and aggregations have already happened to the set of data you have fed. One of the most crucial elements of data streaming is speed, and this is what makes it different from batch processing, which is almost similar to data streaming.

Some of the common data types that are processed in this technique include:-

  • User log files from websites and mobile activities
  • In-game activities
  • Information on your Twitter, Facebook, Instagram and other social profiles
  • Any purchase you have made from online stores
  • Geospatial services
  • Finance portals, and
  • Information shared between connected devices in an IoT ecosystem

Data Streaming Examples

For practical understanding, imagine you intend to sign up for an online video streaming website. As part of the sign-up process, you log in using your Facebook handle and complete the procedure.

When you sign in, you will find flicks and shows you are most likely to watch and in different regional languages in your feed, apart from trending and popular television series or movies.

Before you were taken to the next page, tons of operations have happened at the backend.

  • The portal has tracked and collected countless pieces of information from your Facebook handle to analyze your place of residence, your ethnicity and the languages you are familiar with.
  • To be more precise, it has collected your interests through the pages you have liked, topics that you have posted or shared about, your photos, the locations you have been to and the pages of celebrities you have liked as well.

For instance, if you have liked Al Pacino, you will receive recommendations on his films, interviews, documentaries and even shows or films he has done a cameo. This has happened in real time and fast to give you a better and personalized viewing experience.

Another example we can quote is from the driverless car technology. As you know, self-driving cars are technological marvels that are based on the IoT infrastructure.

What looks like a sleek car, has hundreds of sensors and software programs processing massive chunks of data per second. Every single moment, data is constantly captured, transferred and streamed into the processing systems for instantaneous results.

For instance, if the sensor notices a damaged road or a sudden pedestrian crossing in a short distance, the car immediately reroutes to its nearby lane or stops with the results the system infers from the data received and processed through data streaming.

Driveless Car

Hypothetically, if this had to be done in batch processing, self-driving cars wouldn’t have left the computer simulation stage. This is also the same case with airplanes and satellites that require tons of involuntary precautious measures to be taken at every other instance.

If you didn’t know, a single flight duration of Boeing generates as much as one terabyte of data every single hour of its flight. So, you can do the math and calculations on the complexity of data streaming in its most practical applications.

You can also consider examples on RPG gaming or mobile games like Clash of Clans, where the system recognizes you are playing with your friend, tracks your activities and immediately comes up with challenges, missions or incentives based on your then in-game scenario.

As far as e-commerce portals are concerned, you are also likely to receive products or services recommendations depending on your region, your online activities and any demographic specific offers or promotions peculiar to your region or locality. For instance, the sale of Marathi books, Tamil movies, fog masks at discounted prices and more.

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How is Data Streaming Different from Batch Processing?

To understand data streaming better, it is important to know how this technique is different from batch processing. Apart from speed, one of the major differences between data streaming and batch processing lies in the fact that batch processing takes a massive chunk of data into consideration and gives aggregated results that are optimized for in-depth analysis.

On the other hand, data streaming considers fragments of data or micro-sets that deliver more efficient results and recommendations at one particular instance. For instance, batch processing is applied and more effective when an HR manager is analyzing attrition rates, employee satisfaction levels across diverse departments or working on incentives and appraisals.

If you notice, the amount of data fed in each process is enormous and processed for an overall inference. If the HR manager had to apply data streaming, he or she could use it during recruitment, wherein a potential candidate could be immediately tested on whether he or she would be committed to the job or company, fit into the company culture, would leave within a short span or if salary negotiations are required.

Technically, understand that the batch processing works on queries from diverse datasets while data streaming works on individual records or most recent data sets.

Latency in batch processing ranges from one minute to several hours whereas latency in data streaming ranges between seconds and milliseconds.

Also, complex analytics techniques go into the processing of data in batch processing while simple operations like response functions, rolling metrics, aggregation and more are deployed in data streaming.

Data Streaming Tools

Thanks to its crucial role in offering an experience that is indeed once in a blue moon, you can also call this technique fast data because if the latency is huge, a user might never experience what he could have with data streaming.

For positive achievements, there have to be equally fast and responsive tools that complement the process and deliver results that analysts and companies visualize.

Some of the data streaming tools used include the following:-

Apache Spark

Apache Spark

Superfast, sophisticated and user-friendly – these three attributes mark Apache Spark’s streaming capabilities. Built for the pros by the pros, Spark Streaming allows you to develop and deploy streaming applications that are fault-tolerant and scalable. This is one of the most commonly used streaming applications and if you become a Data Scientist, you need to master this essential tool for career growth.


Apache Storm

Popularly known as Apache Storm, it is compatible with every single programming language you can think of and is renowned for processing more than a million tuple per one second per one node. It is deployed for real-time data analytics, high data velocity, distributed Machine Learning and more.


Apache Flink

Also from Apache, Flink is the more stream-centric application when compared to Storm and Spark. Its functioning is more like a blend of both and is optimized for batch and stream processing and comes up with diverse APIs.


Amazon Kinesis

From Amazon, this data streaming tool lets you create custom streaming apart from serving as a platform to upload and trigger data streaming. While the Amazon Kinesis Firehose allows you to load and perform data streaming, the Kinesis Streams enables you to build one according to your specific needs.

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Challenges in Data Streaming

Like every other technique, there are a few challenges analysts and Big Data specialists encounter in data streaming as well.

One of the most crucial challenges that define the entire process is speed followed by its built. This technique requires the presence of two distinct layers of operation – the fundamental storage layer and the processing layer. The role of the storage layer is to record data from users and pave the way for faster, seamless and replayable relays of data into the processing layer.

On the other hand, the processing layer is responsible for taking in data available in the storage layer, perform computations on the data set and in turn notify the storage layer to permanently delete any chunk of data that is not needed for processing and storing.

All these operations have to happen in micro or milliseconds to achieve significant results. Apart from these, challenges are also evident in data cleaning techniques, planning scalability, fault tolerance, and data durability.


Now you have an idea of what all happens under the hood for that one perfect moment in your online time. Capitalizing on your needs at one particular moment and a need of yours is what companies and startups strive to achieve, and this is immensely supported by data streaming.

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To sum it up, data streaming is just like the climax from Doctor Strange, where a war between two worlds has been fought, and timelines have been shifted and altered. But when everything was done, people never had an idea of what happened.

Data streaming is tackling millions of Dormamus under the hood to deliver you the best of online and personalization experience every single day and hour. How cool is that!

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