Data pipelines form the essential framework of your business’s data infrastructure. When analyzed right, these pipelines can help your team turn raw data into valuable insights for getting real business benefits. According to research by IBM, companies using rapid data expect to make “better-informed decisions based on analytics (44%), enhance data quality and consistency (39%), boost revenue (39%), and lower operational costs (39%).” In this guide, we explore all aspects of data pipelines – whether you are new to the concept or want to improve your existing systems. This will be the ultimate guide to building data pipelines that you will ever need.
What are Data Pipelines? Why are They Important?
Dmitriy Rudakov, Director of Solutions Architecture at Striim, defines it as “a program that moves data from source to destination and provides transformations when data is in flight.” A data pipeline can be viewed as a logical sequence that allows an organization to respond to particular questions related to that data. The resulting insights may be displayed to stakeholders or decision-makers to resolve broader issues.
Without well-designed and reliable data pipelines, companies often find themselves stuck with vast data stored in disparate locations. Instead of serving as a valuable asset for making timely and precise decisions, data becomes a bottleneck, stifling innovation and growth.
How to Create Reliable Data Pipelines
Step 1: Determine the Goal in Building Data Pipelines
The first step in creating data pipelines is to clarify the outcomes they will deliver for your organization. You need to figure out why you’re building this pipeline. What kind of data will it handle? Who will use the results, and for what purpose? This could be for real-time analytics, machine learning models, or simply to create reports for business decisions.
Step 2: Choose the Data Sources
Next, identify the data sources that will feed into the pipeline. You can decide this by thinking:
- What potential data sources exist?
- In what formats will the data arrive (e.g., flat files, JSON, XML)?
- How will we connect to these sources?
Data sources can be internal (like transactional databases) or external (like 3rd-party APIs).
Step 3: Determine the Data Ingestion Strategy
The next step to build your data pipeline is to decide how you will collect this data. There are a lot of ways you could go about this. You could build your own pipeline tools (like Python or Airflow) or use third-party integrations. You will also have to decide if you want to collect data in predefined batches or in real-time.
Real-time streaming might be useful for tracking user behavior on a movie platform. But batch ingestion might be better for pulling historical data once a day.
Step 4: Design the Data Processing Plan
Depending on your needs, you’ll either process data as it comes in (real-time) or after it’s stored (batch). Here, you’ll perform tasks like removing duplicates, converting formats, normalizing data, or enriching it with additional details. Data processing steps in the pipeline could involve filtering out irrelevant data (like bot clicks), normalizing different data formats (e.g., ensuring all timestamps are consistent), or even joining multiple data sources to get a fuller picture of user behavior.
Designing data pipelines is not everyone’s cup of tea and it can only be done by professionals who have a thorough understanding of all the mechanisms. If you are thinking of building your own team for the job, you could benefit a lot from big data analytics training to start with. EducationNest is one such leading corporate trainer in India providing big data training to corporate teams to effectively help them build the skills they need to succeed!
Step 5: Decide on Data Storage
This is where you decide if it is gonna be a data warehouse or a data lake. The former is typically used for structured data that needs to be queried. Data lakes can store raw, unstructured data for flexible analysis. You could do a combination of both too. Cloud storage is also a common solution because it scales easily and handles large volumes of data.
For a movie recommendation engine, you might store user interaction data in a structured data warehouse, while raw logs could go into a data lake for deeper analysis later.
Step 6: Set Up Data Workflow
At this stage, you will map out how different components of the data pipeline interact with one another. You may need to schedule tasks, such as ingesting data at regular intervals, processing it after ingestion, or triggering other jobs once specific data is ready. Tools like Apache Airflow are useful for managing these workflows. Ask:
- What jobs depend on upstream processes?
- Can any jobs run in parallel?
- How will we handle failures?
Step 7: Monitoring and Governance
As part of the last steps to build a robust data pipeline, you need to set up monitoring and governance to keep an eye on it. This will typically include data quality checks, monitoring for errors, and ensuring pipeline runs aren’t failing. Governance will secure your data and ensure it is accessed only by authorized users.
Step 8: Build Data Consumption Layer
Finally, you need to think about how the processed data will be used. The data consumption framework is where analytics, ML models, or reports are generated. Questions to ask include:
- What is the best way to utilize our data?
- Do we have all the data needed for our use case?
- How will our consumption tools connect to data stores?
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Conclusion
Data pipelines are at the heart of modern data strategy. They give companies the ability to turn raw data into powerful insights. A lot of the time, smarter decisions and faster responses depend on the way you process your data. It is the only way to gain a real competitive edge. As businesses grow and data pours in from all directions, having a well-built, scalable pipeline becomes crucial. This is not just for managing data today but also for handling whatever challenges come tomorrow.
At EducationNest, we are all about making sure your team is ready for this challenge. Our big data analytics training program is designed to train your employees with the skills to build and maintain cutting-edge data pipelines. We know what it takes to stay competitive in the fast-moving data world, and our training is built to give your workforce the practical know-how they need to do just that. Want to get your team up to speed and ahead of the game? We’ve got you covered.
