Building Data pipelines: A guide to Data flow automation in Data Engineering
Intro/overview :
Data is the most vital instrument of business organizations. Data when extracted from various Data sources are organized,manage and analyzed for Decision making .
It is Data that reveals the weakness and strength of business organization and helps them to tighten their loss ends to stay competitive.
Thus, company sought after ways to transport Data from disparate sources, in order to analyze them for decision making process. This is where Data Pipelines comes to play.
A Data pipeline is a channel of transporting Data from various sources to a destination e.g Data warehouse, Data lakes or any other type of Data repository. During the process of transporting the Data, data management and optimization is done getting it set to a state where it can be use for analysis.
Basically, they are three components of Data Pipelines:
The Data Source:- where data is extracted.Examples Flat files, APIs, the Internet of things etc.
Transformation: The act of streamlining Data, getting it ready for analysis.
Destination:e.g Data warehouse or Data lakes or any other repository where Data are stored.
POPULAR DATA PIPELINES TOOLS
In the automation or workflow of Data pipelines there are tools which makes it possible.
Apache Airflow: This is an open source platform well-suited for automatic data pipelines. It harnesses the Directed Acyclic Graph (DAGs) to define workflows where each nodes represent a task, and edge which denote task dependencies .
It enables the definition of task dependencies ensuring tasks are executed only when their dependencies are successfully completed.
Airflow support dynamic workflows generation making it scalable and flexible.
Moreover, it also integrates various Data storage systems , cloud services etc. This allows the data engineer to design pipelines across different platforms.
Most excitingly, it provides a web based user interface for monitoring workflow, status and historical information this aids in debugging and to optimize performance. It can also distribute task across multiple workers making it suitable for dealing with large Datasets .
Luigi: This is another popular Data pipeline tool . It is a workflow management system to launch a group of tasks with defined dependencies efficiently.
It is an python based Application programming Interface (API) that Spotify developers to build and automate pipelines.
Data engineers can utilize it to create workflows, manage complex Dara processing and also for Data integration .
Unlike Apache Airflows. Luigi doesn’t use DAG. Instead, it uses two building blocks .
Target and Task.
Task are the basic unit of work in the pipeline. A task is said to be completed when it reaches it target.
Target can be a result of a task or an input for another task
Others examples of popular Data Pipeline Tools include:
1.Perfect
2.Talend
3.AWS glue
All are packed with architectural features that aids the automation process of pipeline
Conclusion:
Automating Data workflows aids in efficiency and scalability of data. Large Datasets can easily be integrated without significant manual effort.
Moreover, it improve Data quality and integrity when Data is transformed.
It also provide flexibility and adapting to changing data requirements and evolving business needs of organization. This in return, will enable companies to stay updated and respond to data changes quickly enabling effective business decision making.
It would have been of my best interest to provide Hand-on experiment procedures for the automation process but I do not have what it takes at the moment to do so.
However I’ll provide link at the end of the article that will help you with that.
Happy Reading !!!
See ya soon.
Recommendations:
https://youtu.be/XItgkYxpOt4?si=F-YBEUu3vkUQqOR7
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