All the hype about the huge amount of data that we are generating regularly is definitely stuck in all of our minds. We are a generation where data is probably the most valuable asset to us. As it is often said, “Data is the new oil”, I think soon data will be a more valuable asset than oil ever was.
And the velocity with which we are generating data each day is something way beyond the imagination of a normal human being. And if we carry on with the same speed of producing data regularly, then the speed of light and speed of data generation by humans would be equivalent pretty soon. (Lol.)
All this data is of utmost importance for all the organizations out there. Employing Big Data analytics into their systems, organizations have observed some never before results. They are able to achieve things that previously seemed impossible to them. And that’s the power Big Data gives them – to achieve the unachievable.
Why Many Companies are leveraging Big Data Analytics
This power of Big Data analytics is the major reason why it has gained so much popularity in the recent few years and has shown no signs of slowing down yet.
Big Data has grabbed the attention of some of the biggest organizations in the world. In short, Big Data is well and truly in the spotlights and will remain there for many years to come.
Companies are leveraging Big Data analytics. Seems easy, isn’t it? But ever had the thought of how organizations are managing these huge amounts of data? No, you didn’t.
As the old saying goes, “Good things won’t come easy”, Big Data analytics is the perfect example of it. Handling and processing Big Data is not a piece of cake.
Dealing with Big Data is a much more complex process than it seems to be. It takes a well-designed architecture to deal with this humongous amount of data.
A properly designed and executed architecture is the heart and soul of Big Data analytics. Without it, it’s not even worth thinking of Big Data analytics.
Do I Need A Big Data Architecture?
Yes, I mentioned earlier that one needs a robust Big Data architecture to ensure maximum benefits from it. But it doesn’t mean that everyone needs it.
Unless and until you don’t have to deal with data that is at least in the order of petabytes or more, you don’t need a Big Data architecture.
For all those who deal with data that is of greater size than that, you need a properly formulated architecture to make sure that you don’t end messing up everything.
Another prerequisite sort of thing is that you must be working on a Big Data project which has data arriving from various sources and that too has a velocity greater than the average velocity.
Big Data Architecture
Big Data architecture is a layered model that performs complete processing Big Data to get the best insights out of it. All the layers are significant and the output of each layer acts as an input to the subsequent layer.
Here are all the important layers of a properly structured Big Data architecture-
1) Sources Layer
The first and probably one of the most important things that have to be kept in mind while designing the Big Data architecture is its sources.
The sources of Big Data are the ones that dictate the complete architecture. Since the data is arriving into the system from varied sources, in different formats, and with a high velocity, this layer decides the overall working of the architecture.
The data in different formats include data from relational databases, real-time data from servers and sensors, etc. The data basically arrives in two forms, either as batch data or as real-time data.
All of these sources generate huge amounts of data in seconds. So the Big Data architecture has to be designed that has the potential of managing this data.
2) Data Ingestion
The first encounter of Big Data with its architecture happens through this layer. It is the layer that marks the beginning of Big Data processing. The data arriving from numerous sources is fed into the system through this layer.
This layer then classifies the data into different tags for the smooth processing of data in all the layers of the architecture.
This layer ensures the trouble-free flow of data from layer to the other. The two most commonly used data ingestion tools are Kafka streams and the relevant REST APIs.
3) Storage Layer
The layer that receives Big Data is the storage layer. The data that is coming from the varied sources is gracefully stored in this layer. It carries out the modifications to data as per the requirement of the system.
Generally, HDFS is the most commonly used tool for storing huge amounts of batch data and RDBMS for storing structured data. The storage layer is designed on the basis of the data format and the purpose of that data.
4) Analysis Layer
The major reason behind all this hype of Big Data is the insights gained through its analysis. These insights are then significant for making data-driven decisions. Thus, the analysis layer is the most important layer of all the layers. This gives us the power to harness Big Data capabilities.
An array of tools is required for the same. Handling the structured data is not a relatively easy task as compared to the unstructured data which requires some advanced tools for its analysis.
- Batch Processing
Keeping in mind the huge size of Big Data, the architecture cannot be built without a batch processing system that stores, filters, and processes data in advance for further analysis.
These are generally the long-running batch jobs. Apache Hadoop is the most commonly used and probably the best tool for batch processing. This processing involves fetching data from the storage layer, processing it, and writing the outputs in new files.
- Real-time Processing
Processing the real-time data signifies that Big Data has been utilized in its best form. A Big Data architecture without a real-time data processing system is a sort of monotonous.
This fetches and processes data in real-time which implies processing the data the moment it arrives. This processing of data is something that is making the difference today and requires a robust system for it.
5) BI Layer
After the complete analysis of Big Data is carried out, the next and the final step is to store the valuable outputs and insights of it. The BI layer takes care of the same. Once it receives the output, it then classifies it according to humans, applications and business processes.
The cycle of Big Data analysis includes receiving data from varied sources, repeatedly processing of data, and then portraying these results into a report. These reports are significant for organizations for making data-driven decisions.
Big Data analysis is a field of science but carrying it out in a properly planned manner is an art. Before heading towards Big data analysis, you should know the art of handling it.
Unless you don’t know this art, you won’t be able to make the most out of Big Data. You won’t be able it unleash its true powers. Big Data is huge, way beyond your imagination and handling it isn’t a cakewalk.
With Big Data there comes a lot of challenges as well. You must be ready in advance to deal with it. The major hurdle is that of security.
You’re going to deal with a lot of sensitive data of the people and with the amount of data breaching incidents taking place all around, securing it is a lot harder than you can imagine.
So design the architecture accordingly because if there’s a single loophole in it, Big Data can backfire as well.