Real-time data streaming and analysis addresses real-world issues and applications

Data analysis has made it easy for the companies to look at the bigger picture. Data analysis has not just paved the way for better and smarter decisions but also helped in retaining their competitive edge and also increase in their market share. It is only because of this that the companies are able to understand what is the requirement in the market and how can they cater better to the same. Hence, companies are more into implementing ways and methodologies that'd pave the way for faster and more intelligent data analysis. Now that the world is inclined to digitization and automation, data analysis becomes a lot more critical. On the same lines, the pandemic seems to have added to this.

Data analysis is complex and time consuming. Hence, the need of the hour is to employ better tools so that meaningful insights from the raw data can be extracted at less cost and complexity. With a built-in fully-integrated support for tools, things can definitely fall in place. This is because all the requirements pertaining to the development, training, management and deployment of Machine learning models would be addressed. All in all, a full-fledged development environment is what the data scientists should be able to access. With this, they can also deal with real-time data taking the decision making ability to next level. Machine learning models are build and trained in accordance with this real-world data thereby resulting in fast, improved and fast decisions.

What real-time data can do?

Earlier, the importance was just on historical data or the data at rest. Simply put, all the machine learning models were trained on the basis of this historical data. All this worked on one basic assumption – A pattern that was observed in the past would continue in the future as well. Companies used historical data to predict what the future would be. On the flipside, this assumption doesn't hold much potential when it comes to addressing the real-world issues and applications. This is exactly where real-time data analysis comes into play. Seeing what the future holds by taking into account real-time data has benefits over historical data that are worth mentioning. One, the performance of the AI based applications is more powerful. Two, the accuracy is top-notch. Three, the insights drawn have a considerable weightage while predicting the future.

This method serves to be better than relying on historical data as the machine learning models can be well aware of the changes in data patterns and ultimately deliver competitive differentiation. Streaming analytics, allows the companies to take smart data-driven decisions as a result of intelligent real-time data analysis.

When companies shift their gear from historical to real-time data, they are a step closer to achieving adaptive learning. They are now inclined towards building and training models that rely on the latest data (real-time / streaming) to improve their operations, cater to the customer's needs in an improved way and create a better business value. Predictions based on real-time data makes all the more sense and thus they can try considering special algorithms for the same. Though, real-time data has got advantages of its own, it isn't a cakewalk to proceed with. This is because the approach that needs to be followed changes completely. Be it on the technological front or the analytics aspect, a different approach is required.

How to get your hands on the streaming data

• To begin with, the first step should primarily focus on building a real-time streaming data pipeline. This ensures that you get the most reliable data to proceed ahead with. Yes, there are plenty of data science tools out there that cater to the very aspect of training the machine learning models. But, a point to note is that a majority of these tools work on historical data or the data at rest. There are comparatively very few tools available that deal with real-time data streams.

• Choosing the right tool is critical. This is because real-time data (streaming data) comprises of time-stamped data packets in series. Hence, the tool employed should be able to deal with this kind of data. The best way to deal with all of this is to employ a data science This is a complete package with a range of built-in Python libraries. This enables the data scientists to work on real-time data sets. Another advantage of such platforms is that it is possible to access both real-time as well as historic data to better understand the model and bring forth the best possible outcome.

• One of the best platforms that caters to real-time data streaming is Kafka. Kafka is a low latency, distributed, horizontally-scalable open source streaming platform. A noticeable feature that's definitely worth a mention is that Kafka can handle trillions of events a day. To draw meaningful insights based on real-time data, the basic requirement is real-time data. This can be collected from an existing Kafka stream into a time series table.

• Data scientists can make use of Nuclio, one of the fastest open source server less framework, to "listen" to the Kafka stream and then ingest its events into the time series table. Hence, this technique aids in speeding up data ingestion.

• To visualize the data stream, Grafana dashboard is to the rescue.

• One can manipulate the time series data in a Jupyter notebook using Python code.

Yes, the approach to follow while dealing with real-time data is different but using the above steps, data scientists can build, monitor and manage real-time machine learning models.