The Concept of Data Pipeline Explained

You might have watched the iconic movie Charlie and the Chocolate Factory. The film is a legendary classic featuring Johnny Depp (of course!). The first scene we remember is when the high-speed conveyor belt roars up. Exquisite varieties of candy are made from absolutely nothing, and by the end of the scene, you can’t help but grin at the kids running around stuffing their mouths, hats, and pockets full of chocolates. What more could they possibly ask for? Maybe I’m a little over the top, but it’s the perfect analogy showing how useful a data pipeline is for data-hungry analysts and enterprise owners.

 A data pipeline is simply a set of actions that moves data from diverse sources to a single destination for analysis and storage. It’s more like a framework built for data, and nothing else but data. It allows a seamless, scalable, and automated transmission of data– for instance, from a SaaS app to a data warehouse.

The logical flow of data from one point to another is an important process in the modern data-driven enterprise. Besides, data is vital for useful analysis to happen.

“What do you mean by an efficient and automated flow of data?”

Data flow can be inconsistent (and flawed), mainly because so many things can go wrong during transportation from one location (system) to another. For example, data channels might be corrupted, sources may generate conflicts or duplicates, or data can hit bottlenecks.

Enter the data pipeline, a software that defines how, where, and when data is collected. It automates the processes involved in copying, cleansing, transforming, and loading data for advanced visualization and analytics.

The data pipeline offers end-to-end velocity by removing errors and combating latency and bottlenecks and has the plug-and-play capability making it flexible and versatile. It can process multiple data streams at a go, and do so with unrivaled efficiency.

In short, the data pipeline views all data as flowing data, and it allows for flexible patterns. Regardless of whether it comes from real-time sources (such as online ticketing transactions) or static sources (like partitioned databases), the data pipeline breaks down each data stream into smaller chunks. It processes these chunks in parallel (both hourly and in real-time), thereby adding more computing power.

Think of a data pipeline as the ultimate assembly line. As such, it doesn’t require the final destination to be a data warehouse. It can stream data to a different location, such as Salesforce or visualization tool. (If candy was data, imagine how calm the kids would have been!)

Creating a Data Pipeline

For those who don’t know, a data pipeline has three main components:

  • Ingestion
  • Processing
  • Persistence

Let’s delve into all of them one after the other.


Ingestion includes all the components of a data pipeline that capture data from sources- the belts in our conveyor analogy. An application programming interface (API) makes data extraction much easier. Before you can build code that calls the API, though, you must isolate which data you want to read through a process called data profiling- analyzing data for its structure and components, and assessing how it well it fits business goals.

2. Processing (or Transforming)

Once data is mined from source locations, its format or structure may need some adjustments.

Data processing includes mapping coded values to more definitive ones, as well as aggregating and filtering those values. Part of this is a combination- a type of transformation where data-driven relationships can be leveraged to bring together related columns, tuples, and tables.

The timing of processing depends upon what data replication process a company decides to implement in its data pipeline: ELT (extract, load, transform) or ETL (extract, transform, load). With the more futuristic ELT, your company can enjoy the benefits of cloud-based data warehouses. Loading happens without applying any transformations, and tech leaders can apply their own changes to data within a data lake or data warehouses.

3. Persistence (or Destination)

Now that you’ve figured out what you want to do with the data and how to do it, the next vital step is placing the data in appropriate destinations. A data warehouse fits this requirement just fine, and it is where all cleaned, mastered data ends up.

These highly-specialized databases contain all the transformed data in a centralized location for use in business intelligence, analytics, and reporting by executive and lead analysts.

Less-structured data can stream into data lakes. Here, data experts work their magic and access large pools of minable data.

Alternatively, your company may choose to feed data into an analytical service or tool that accepts data streams.

Get Started With Custom Data Pipelines

Ok, so you’re 100% convinced that your firm requires a data pipeline. How do you go about it?

You could hire a team (of coders and engineers) to build and manage your own data pipeline in-house. 

This entails, among other things, monitoring incoming data streams, transforming each chunk to match its destination’s schema, and altering existing schemas as company requirements change or expand. Count on the investment to be costly, both in terms of time and resources. A simpler, more cost-efficient solution is to invest in a proficient data pipeline, such as those from Helios. In addition to getting exceptional value for money, we give you a chance to cleanse, transform, move, and enrich your data on the spin. Our avant-garde data pipelines ensure you get secure, real-time data analytics, even from different sources simultaneously, by placing the data in cloud-based data warehouses.

If you’re ready to learn how Helios can help you solve your biggest data challenges, contact us today.



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