Building a Data Pipeline: A Comprehensive Checklist

Data pipelines form a crucial component of an organization’s data management strategy, as they help to collect, process, and analyze data in a streamlined and automated way. However, building a data pipeline is not an easy task as it requires careful planning and execution. In my opinion, it needs a mix of both software engineering and data modeling skills. In this article, I will discuss the five important phases and share some insights on what you need to do to successfully build a data pipeline.

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1. Requirements and Data Discovery

This phase is the most crucial one and usually involves a lot of interaction with the stakeholders (mainly data product owners and data scientists/analysts). It is important to not worry about spending too much on this as this phase determines the success of your data pipeline. The crucial steps in this phase involve:

• Understand the business problem: There is no value in building products that no one is going to use. So, get a clear picture of your data pipeline’s business value.
• Clearly define the user requirements: Make sure to discuss the requirements with the stakeholders to ensure everyone is on the same page.
• Explore and define the source datasets: This is where you need to explore various datasets and define the right sources for your data pipeline. This step requires data modeling skills to build possible datasets that meet the business requirements.
• Perform a preliminary data analysis: Once you have defined the source datasets, analyze those datasets (mainly using SQL) to understand any gaps in the data and identify any data quality and data completeness issues. It is also important to discuss these issues with the stakeholders. They may or may not be okay with them and you may be asked to explore other datasets or find solutions to solve those issues.

2. Design the architecture

This phase is where you need to showcase your system design skills and build the architecture for your data pipeline. It is important to understand the volume of the data that you are dealing with to decide on the right tools to use and build an efficient architecture. In this phase, you need to decide on the right technologies to use for the following four processes:

• Data ingestion: Source data may come from existing core datasets or may need to be consumed from an API or a Kafka topic.
• Data processing: This single-handedly can determine the efficiency of your data pipeline. So, choose the right data processing framework. The most popular frameworks include Apache Spark and Apache Flink.
• Data storage: This is where you will need to store your final dataset and this can be a data warehouse or a data lake. You may even need to publish your dataset into a new Kafka topic or provide API endpoints for your final dataset.
• Data visualization: This is mostly the job of data analysts. But as a data engineer, if you are asked to build dashboards, you can choose from the popular ones such as Tableau, Power BI, or Looker Studio that support real-time dashboards and provide connectors for your data endpoints. Some companies might have their own internal tools to build dashboards as well.

3. Implement the data pipeline

Now, it is time to start implementing your data pipeline using the tools and techniques you decided on in the second phase. This is where you will do actual coding (Widely used are Python and Scala as they support Apache Spark capabilities) for performing data transformations. Follow the software engineering best practices and keep the following key things in mind while building the application:

• Use the modular approach: Makes it easy to debug errors and integrate changes in the future.
• Prioritize scalability: Your code should be able to handle variations in data volume and provide support for optimization based on it.
• Error handling and logging: The application should be able to detect errors in data sources and alert the engineers. It is also important to log the progress of data pipelines to easily fix failures or data quality issues.
• Use a CI/CD tool: This automates the building, testing, and deployment of data pipelines and boosts productivity.

4. Test and deploy

Before you can start using your data pipeline, you need to test it thoroughly to ensure that it is working correctly. A data pipeline deployed into production without rigorous testing can result in tedious rework in terms of fixing data quality issues in the final dataset. Develop a testing plan and perform these essential steps in this phase of building a data pipeline:

• Testing: Perform both unit testing to test individual modules and integration testing to test the entire architecture.
• Performance testing: It is very important to run performance tests on your data pipeline to ensure it can handle large volumes of data.
• Data quality testing: Perform a thorough analysis of your final dataset to ensure it meets the user requirements. It is even better to get data validated from the stakeholders as well.
• Production deployment: Once the testing is completed, it is time to deploy the pipeline into production. Also, monitor your production pipeline in real-time to identify and fix any issues and have the plan to recover from issues quickly.

5. Document

Last, but not the least, you need to document your data pipeline. This can include the business value, data architecture, tools and technologies used, contact information of the dataset-owning team, data sources and data transformations involved, references to code repositories, the project timeline, and a user manual explaining the dataset, its attributes, and how to use it. By having these components, you will have a well-documented data pipeline that can be easily maintained and improved over time. Documentation is also extremely helpful in case a new team member joins the team and wants to get familiar with the data pipeline.

This is the end of the checklist for building a data pipeline. Do remember to continuously monitor and maintain your pipeline to ensure that it is providing value to the business. Regularly connect with the data product owner to understand how the dataset is bringing value and incorporate any changes needed to your data pipeline.

Thank you for reading! I hope you found it helpful. I will continue to write about my learning and experiences, which I hope will be helpful to the data engineering community. Please comment with your thoughts and feedback.