Data Pipeline is an embedded data processing engine which is used for Java Virtual Machine (JVM). This transforms or filters data that it gathers. But before even learning how to use a data pipeline, it is best to understand what you can use it for, which will be in a lot of ways that you can imagine.
If data is a core part of your job, then you need a data pipeline. Whether you are a small or a large company, as long as you use data, “why” data pipeline is important is what you need to understand. It is a mix of software technologies which converts data from different sources which can easily be used when needed.
What Can a Data Pipeline Do?
The Data Pipeline can do whatever you command it to do. This means that data pipelines are evolving depending on your business needs. But on business strategies, a data pipeline has two uses:
- Data-enabled Functionality
Data pipeline can do automated customer targeting, financial fraud detection, RBA or robotic process automation, as well as real-time medical care. Because of this, given the ability to create flexible and scalable data pipelines which help leverage technologies, promotes industry innovation and makes the data-driven business be ahead compared to its competitors.
- BI and Analytics.
When talking about Big Data, data pipelines allow companies to be at their best and think about new ideas all the time. Data pipelines should be convenient which tends to every need of an organization. There BI and analytics tools that offer an overall solution which doesn’t require much of personalization as well as optimization.
Determining the Best Data Pipeline.
There are three important criteria that make a good data pipeline. This ensures that the data and research results are reliable and useful to the business. You have to understand each of these criteria to help you understand the data pipeline that will be presented by your data scientist. The criteria are as follows:
- Data science should go through testing and validation. Data scientists are using tools and software engineering which allows them to isolate the analysis code, data sources, and the randomness of an algorithm which makes the data pipelines reproducible.
- It is important that you secure all data sources to provide consistent data and reproducible data pipelines.
- A Common Extract, Transform and Load (ETL) Process. This is responsible for importing data from the source and store it in a data repository. Share ETL between research production to lessen errors and to ensure its reproducibility in using the data pipeline.
Data Pipeline: The Solution
Whatever your goal is, whether to use BI and analytics to formulate a decision, or maybe deliver data-driven functionality to products and services, data pipelines are the best solution to all these. But remember that data pipelines don’t work like magic. These are the best solution to meet expectations of business leaders. After knowing all these information, the next thing to do is hire the best developers and data scientists to help and guide you towards the success of your business.