6 Best Practices to Build Data Pipelines

Data pipelines have become a crucial component of modern data-driven organizations. They facilitate a variety of processes that extract, transform, and load data from multiple sources and move it to a cloud storage or other target location.

In other words, you can call data pipelines a workflow that assists businesses in processing huge volumes of structured and unstructured data to fetch important insights.

Howеvеr, building and maintaining pipеlinеs is not as еasy as it sееms. It requires meticulous planning, tеsting, dеsigning, and monitoring to еnsurе data quality and rеliability.  

With that in mind, let’s dive deeper into a couple of best practices that can help you build data pipelines with ease. 

Best Practices To Build Data Pipelines

Creating data pipelines is a complex and challenging process that involves different components working seamlessly together. That's why one needs proven practices to minimize risks and fully harness resources.

With that said, let's look at some best practices you should follow when creating your scalable pipeline system:

1. Start With Gaining a Clear Understanding of Your Goals

Gain a clear-cut understanding of outcomes, your business goals, and KPIs. Define your goals precisely and add quantifiable criteria for the success of your data pipeline.

Have clarity of the problem you want to solve, the required data sources, what you want to transform, your expected outputs, and which metrics you will use to measure your data pipeline's success. 

Clearly defined goals let you gain better control over your project and prioritize tasks to avoid complexity.

2. Select Appropriate Tools and Technologies

Opt for tools and technologies that are the right fit for your data pipelines.  You might require various solutions to create a data pipeline depending on your data sources' velocity, volume, veracity, and variety.

For example, you can consider Apache Spark for extensive and retrospective data analysis through batch processing. At the same time, you can use Apache Flink or Kafka to achieve real-time and event-driven data processing. Don't forget to explore established architectures such as lambda or kappa for further insights.

Other than this, you need different systems for storage, like S3, GCS, ADLS, data lakes, NoSQL databases, or data warehouses to store data. And query engines for querying data from single or multi-hybrid cloud storage.

Assess each option's plus and minus points and select the one that best suits your business needs and budget. Remember the problem you intend to solve while evaluating your options.

3. Integrate Frequent Data Quality Checks

Apply data checks and validations from time to time within your data pipeline. Make sure that your data pipeline can easily handle missing, invalid, or inconsistent data and alerts you when something goes wrong.

Implement data validations and quality checks at every stage of your data pipeline, which includes verifying your data schema, comparing results with expected benchmarks, and applying business rules to your data.

Let’s have a detailed overview of basic quality checks:

• Validity: Ensure that your data values are within acceptable limits.
• Consistency: Verify the uniformity of data values both within individual datasets and across multiple datasets.
• Timeliness: Check whether the data is latest and updated.
• Uniqueness: Make sure that there are no duplications of data values or records.

4. Choose the Right Tech Stack

To ensure your data pipelines' scalability, choosing a proper tech stack is extremely important. Your chosen tech stack should be able to handle large data volumes and is to be budget-friendly. Also, remember that choosing the wrong tech stack may create unnecessary hurdles or performance issues.

Consider crucial factors like data variety, velocity, and volume while evaluating technologies for your data pipelines. If your business handles real-time data streaming sites (e.g., social media feeds and IoT readings), consider Apache Kafka or RabbitMQ. Alternatively, opt for tools like Hadoop MapReduce or Apache Spark for high-volume batch processing (e.g., daily reports and ETL jobs).

Along with this, you need to make sure that the cost of your selected tech stacks aligns with your allocated budget. Besides this, evaluate how fast the data can be processed, ingested, and stored in your pipeline. For example, if you are working with streaming data requiring low-latency requirements or real-time processing, then Apache Kafka or Apache Cassandra are the best bets.

So, consider all these factors carefully and adopt a tech stack that suits your business needs and budgets.

5. Use a Modular Architecture

Building data pipelines often becomes complicated while dealing with a huge chunk of data. And one such wrong approach businesse adopt is to create a monolithic pipeline that handles everything in a single shot.

This makes it challenging to troubleshoot problems and can adversely impact the pipeline’s overall performance. To address this problem, it’s advisable to break down complicated pipelines into manageable components. 

Other than this, you can use microservices to manage individual components within the pipeline. They make it easier for you to manage and scale individual aspects as and when needed. 

Overall, the use of modular architecture proves to be helpful in building data pipelines that are easy to scale. Plus, you can make sure that your pipelines are flexible enough and easy to maintain.

6. Monitor Your Data Pipelines Constantly

Your task does not get finished after making a data pipeline; you need to monitor it. In monitoring pipelines, you need to do different tasks like:

• Track the core data pipeline’s performance metrics like latency, error rate, throughput, memory, and CPU utilization.
• Seek out opportunities to make your data pipelines efficient and scalable. These opportunities include parallelizing tasks using advanced tools like Hive, Hadoop MapReduce, or Spark.
• Identify possible bottlenecks in data pipelines, such as memory leaks or data skewness.

Final Words

Dеvеloping and maintaining data pipеlinеs is crucial for thе growth of data-drivеn еntеrprisеs.  By following thе abovе mеntionеd practicеs, you can еnsurе thе scalability, еffеctivеnеss,  rеliability, and maintainability of data pipеlinеs.  

Thе corе factors in succеssfully building data pipеlinеs arе thе kеy to sеlеcting thе right tools, sеtting clеar objеctivеs, prеcisе documеntation, data quality implеmеntation, and continuous tеsting and monitoring. 

So what are you waiting for? Just intеgratе thеsе bеst practicеs to makе thе most of your pipеlinе dеvеlopmеnt еfforts.