Data Pipeline 101: Components, Types & Why it Matters

The modern-day data pipeline is as fascinating as it is useful. To help you grasp this concept, let’s go over a real-life situation.

Picture this: Let’s say you run a gigantic online shop. It is open round-the-clock. Users place multiple orders and pay for them instantly- every minute of every hour. That means your outlet must process hundreds of tiny bits of data like order ID, credit card details, and user ID. Quite a tall order, right?

Besides running day-to-day operations, you must analyze transactional data. For instance, you are analyzing the sale of shoes. It means you must amass this data from business databases and move it elsewhere, perhaps to a much larger software that can handle complex streams of data. Only after moving will you be able to analyze the data. But how do you move the data streams? It would be best if you had a reliable infrastructure- software or hardware- that can allow you to do that. You need a high-end data pipeline.

Level Zero: What is a Data Pipeline?

A data pipeline is a dedicated set of activities and tools for moving data. Mostly, data moves from one system with its method of data processing and storage to another different system where its storage and management is also different.

Pipelines also allow for the virtual acquisition of data from many isolated sources, then consolidating it into a single, top-performing data storage. Simply said, a data pipeline applies transformation logic (mostly split into diverse stages) before sending the data to a load destination, say a data warehouse.

As popular as data pipelines are, only 35% of marketers think that their pipeline is efficient. Here at Helios, we are determined to change that. With the advent of virtual marketing and the dizzying pace of tech, pipes will inevitably become a critical component. They will become a lifeline for the collection, transformation, migration, and visualization of intricate data. Count on us every step of the way.

The 8 Main Components of a Data Pipeline

To understand truly how a pipeline works, let’s take a look at its various components. They include:

Origin is the point of entry of data in a typical pipeline. A company’s data storage systems (including data warehouses) or its data sources (IoT device sensors, application APIs, and transaction processing applications) can be a possible origin.

Destination is the endpoint at which the info stream halts. Often, the destination relies on a use case. That means data either moves to a storage center like a database or sources to analytical tools.

Data flow refers to the transfer of data from origin through destination, including the data stores it passes through and the changes it undergoes along the route. One of the essential approaches of data flow, as we mentioned, is ETL- extract, transform, and load.

Processing entails high-level activities and steps for extracting data from sources. It also involves storing, molding, and delivering the data to a final destination.

Monitoring includes checking how the data pipeline works and whether the independent stages are working. More so, whether it retains efficiency with the ever-growing data load, or whether data remains consistent and flawless as it moves through the various stages of processing.

Workflow outlines a sequence of tasks (processes) and the dependencies between them in any given pipeline. Upstream, downstream, and jobs are the most peculiar terms here.

Technology is a toolbox of infrastructure responsible for data flow, storage, transformation, workflow, and regulation. Tooling options depend on an array of factors- volumes of data, organization size, and budget; data use cases, security requirements, and so on. Here, we highlight the foundational blocks of the data pipeline, which include:

  • ETL Tools, including data integration and data preparation software (Talend Open Portfolio, Apache Studio, etc.)
  • Data Lakes– They are repositories for unprocessed data, both relational and non-relational.
  • Programming Languages Java, Ruby, and Python to code the entire pipeline process.
  • Batch Workflow Schedulers (Azkaban, Airflow) that enable users to define workflows as tasks that have dependencies.

Why a Data Pipeline is an Important Addition To Any Business

1.Simple and Efficient

Although data pipelines have sophisticated work processes and infrastructure, their navigation is relatively simple. Moreover, the learning curve of creating a data pipeline is straight. Courtesy of ecosystems such as the Java Virtual Machine (JVM), reading, and writing of code files becomes an easy job for programmers.

2.Inbuilt Components

Data pipelines boast built-in components that allow you to get data in and out of the pipe seamlessly. Once you activate the built-in feature, you can start working with data remotely- that is, via stream operators.

3. Meta Information Flexibility

The isolation of custom tuples and records is one of the high-level attributes of the data pipeline. Thanks to metadata, you can easily track down the data source, creator, instructions, tags, visibility options, and new additions.

4. Compatibility with Multiple Apps

The structure of the pipeline is more or less embedded. It makes it effective for use by digital marketers and customers alike. Add to that its compatibility, and you won’t have to install, depending on a server, or have to configure files. The icing on the cake is that you accomplish data access by just embedding the tiny size of the pipeline into a module (or an app).

5.Live Data Segmentation

Whether your info resides in a remote database, in the form of an excel file, or at an online social media portfolio- data pipelines can break down the data into smaller bits, which are imperatively part of the bigger workflow stream.

Real-time functioning is an incredible feature, too. It doesn’t extend the amount of time needed to process your data. This leaves just enough room for you to infer and process data at hand more seamlessly.

6. In-memory Processing

With the existence of end-to-end data pipelines, you don’t have to save new data changes in a remote database or a disk file. Pipelines contain in-memory capability that makes accessibility of info much quicker than when stored elsewhere.

The Bottom-line: Data Pipeline is the Future of Big Data

Data pipelines are just as relevant as they were a decade ago, perhaps even more tech-inclined today. Their superiority in the data sphere will accentuate going forward. They will accommodate larger data chunks and boast cutting-edge transformation ability. That said, it’s up to you to stay on the move. As data pipelines evolve to achieve impeccable design, enhanced performance, compliance, and superior scalability, make sure your business is right at the heart of it.

Helios Company is the #1 vendor of the data pipeline for online businesses. We boast 200+ customized connections, which allow you to collect crucial data from any location. We go above and beyond cloud data warehouse, data analysis, and visualization to provide you with powerful insights for your big-picture business idea. Our team of professionals is always here to lend a hand. Feel free to contact us today!



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