You might have heard about the term “Big Data.” Big data is not big without data variations, data volume, and velocity of data. The data can be of any format, of any size and any type. If it satisfies, then there would be no hesitation to call that data as Big Data. Big Data is now the need of almost every organization as data is generated in large volumes and these large volumes contain data of every known or unknown type/format. Big Data creates problems like handling data, manipulating data and Analytics for generating reports, business, etc. There comes a solution too as Every problem is a solution. This solution is the development of Data Pipeline.
Big Data helps to produce solutions like Warehouse, Analytics, and Pipelines. Data Pipeline is a methodology that separates compute from storage. In other words, Pipeline is commonplace for everything related to data whether to ingest data, store data or to analyze that data.
Let us assume a case that you have many works such as Data Analytics, Machine learning, etc. Are in line up and store for that data is shared. In this case, we can Ingest data from many resources and store it in their raw format at Data Storage layer. Now, It will be easy to perform any work from this data. We can also transform that data into data warehouses.
Differentiating Big Data Pipeline and ETL?
Sometimes, people get confused by two terms as some use cases use both As keywords interchangeably. But Both are, in fact, different as ETL (Extraction, Transformation and Load) is a subset of Data Pipeline Processing.
ETL is usually performed on Batches (here batch processing)
Data Pipeline contains both Batch and Real-Time Processing as Batch Engine and Real Time data processing Layer
Store (no limits storage) for storing data files of large sizes in Raw format
Unlimited Bandwidth for transmission
Additional Processing Units or Cloud (Fully Managed or Managed)
Data Pipelines Use Cases
Most of the time every use case describes how it is essential and how they are implementing it. But why is necessary too. There are some why points for some of the use cases for Public organizations. Imagine a Forecasting system where data is the core for financing and Marketing team. Now, Why they use Pipeline? They can use it for Data aggregation purposes for managing product usage and report back to customers. Imagine a company using Ad marketing, BI tools, Automation strategies, and CRM. Here, Data is necessary to manage and collect for occasional purposes. Now, if a company is relying on these tasks individually and want to upgrade their workflow.
They have to merge all work under one place, and here Data pipeline can solve their Problem and help them build a strategic way to work. Imagine a company that works on crowdsourcing. It is obvious to understand that they are using many different data sources for crowdsourcing, and they are also Performing some analytics on that data. So, to obtain better output from crowdsourcing in near real time and for analytics and ML, It is best for that company to build a data pipeline for the same to collect data from many sources and use it for their purposes.
Data Pipeline is needed in every use case that one can think of in contrast to big data. From reporting to real-time tracing to ML to anything, data pipelines can be developed and managed for these problems.For making strategic decisions based on data analysis and interpretation we advise taking the following steps -