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August 18, 2022

How to Build a Data Pipeline: A Comprehensive Guide


Edwin Lisowski

CSO & Co-Founder

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9 minutes

43% of decision-makers in IT dread that the rising levels of data influx might overwhelm their data infrastructures in the future [1]. The necessity for businesses to make data-driven decisions, coupled with the ever-increasing disparity of data sources, has necessitated the need for robust data infrastructures that can extract, transform, and load data rapidly and efficiently.

Also, because most businesses generate data in a transactional database, which is not ideal for running analytics, the need for a data transfer system that can move data from the source to the storage and process it in real-time has never been more apparent. That’s where data pipelines come in.

Data pipelines offer organizations the most convenient and efficient way to manage their data. Read on as we explore the concept of data pipelines in its entirety, from what it is to how to build one from scratch.

How to Build a Data Pipeline: Defining Data Pipelines

Data pipelines are a set of aggregated components that ingest raw data from disparate sources and move it to a predetermined destination for storage and analysis (usually in a data warehouse or a data lake) [2]. But, before data flows into the repository, it undergoes various processing and transformations, including aggregations, filtering, and masking, to ensure appropriate standardization and integration [3].

To understand how data pipelines work, consider a pipe that receives input from a source and then carries it to give a desired output at the destination. The input can be sourced from various sources, including SQL, APIs, NoSQL databases, CRMs, etc. However, since this data isn’t ready for use, it has to be prepared and structured to meet business use cases.

The type of processing involved in a data pipeline is usually determined by predefined business requirements and exploratory data analysis. After processing and transformation, the data can be stored in a repository and extracted for use.

A well-implemented data pipeline architecture can serve as the foundation for a wide range of data projects, including data visualizations, exploratory analysis, and machine learning projects.

It might be interesting – Stream data model and architecture

Types of data pipelines

Batch processing

Batch processing typically involves processing loads or ‘ batches’ of data into a repository at set time intervals [4]. These processes are usually scheduled during off-peak hours since batch processing usually works with big data, which can be very taxing on the overall system. By scheduling the processes to off-peak hours, you can prevent the process from impacting other workloads.

batch pipeline architecture

Batch pipeline architecture

The sequential nature of batch processing makes it the optimal form of data pipeline when you don’t need to analyze a specific data set in real-time. Batch processing tasks typically form a workflow of sequential commands. This way, one command’s output becomes the input in the next command.

Streaming data

Streaming data pipelines [5] come in handy when data needs to be continuously updated. Good use-case examples are apps and POS (point of sale) systems. These systems need real-time data updates to update sales history and inventory. With a well-executed system, business owners can effectively track their inventory.

In streaming data pipelines, a single action like a delivery request is considered an event, and related events like the customer confirming delivery are typically grouped together as a stream. These events are then transported through message brokers to the repository.

On the downside, despite having a lower latency than batch processing systems, streaming processes aren’t considered very reliable since messages can spend too long in the queue or be dropped unintentionally. Message brokers present a workaround for these concerns through acknowledgement. Here, a customer can confirm the message has been processed, thereby allowing the message broker to remove it from the queue.

Components of a data pipeline

A typical data pipeline architecture has three basic processes: data ingestion, transformation, and loading. That said, the big data may undergo various other processes depending on the business’s specific needs. In such cases, the data pipeline is designed to handle more advanced processes. Here are the basic components of a modern data pipeline.

Data source

The first component of any data pipeline is the data source. Data sources can be any systems that generate data in your organization like IoT devices, APIs, social media, and storage systems like data warehouses and data lakes [6].


This is where all the collected data ends up. Depending on a business’s needs, the destination can be anything from powering big data analytics and visualization tools to a storage system like a data lake or data warehouse.

Typical data pipeline


Data flow

As the name suggests, data flow is the actual movement of data from origin to destination. This also includes all the changes it undergoes along the way.


Storage is basically the system that stores data as it moves through the pipeline. The storage options an organization chooses to go with depend on various factors, including the volume of the data, the frequency and volume of queries tasked on the system, and the business’s use cases.


Processing typically involves all processes and activities for ingesting data from sources, transforming the data, and delivering it to the repository. There are two data ingestion methods in data pipelines: batch processing and stream processing. In batch processing, data is collected periodically and delivered to the storage system, while in stream processing, data is extracted, processed, and loaded in real-time.


Workflow typically involves defining a sequence of processes and their dependence on each other within the data pipeline.

Workflow dependencies can either be technical or business-oriented. In technical dependencies, data is collected from multiple sources, held in a central queue, subjected to further validations, and finally dumped into a final destination.
In business-oriented dependencies, collected data is first cross-verified against other sources to ensure accuracy and consistency before validation.


There are numerous potential failure scenarios in data pipelines. Therefore, businesses need to implement a monitoring component to ensure data integrity. The primary purpose of the monitoring component is to alert administrators about any mishaps in the data pipeline.

How to build data pipelines

Numerous factors come into consideration when building a data pipeline. And the sheer number of potential failures is enough to get on any data engineer’s nerves. But, with these simple steps, you’ll be well on your way to building an efficient data pipeline.

Pipeline development process


Determine your goals

Before you build a data pipeline, you need to consider what value you want to derive from big data. Therefore, you first need to determine what your objectives are for building the pipeline, how you’ll measure the pipeline’s success, and the use cases your data pipeline will serve.

Determine your data sources

Your data sources will ultimately determine the ingestion strategy [7] you choose. Everything from potential data sources, the data format, and how you connect to your data sources directly impacts your overall data pipeline architecture.

Determine your data ingestion strategy

Having numerous data sources is one thing, but collecting and bringing the data into the data pipeline is another. Various factors come into play when determining the best data ingestion strategy for your data pipeline.

For instance, you need to consider which communication layer you’ll be using to collect data, whether you’ll be utilizing third-party integration tools to ingest data, whether you’ll be collecting data in batches or in real-time, and whether you’ll be storing data in intervals or in real-time.

Design a data processing plan

Ingested data in raw form cannot provide insights. Therefore, data needs to be processed and transformed after ingestion to make it useful down the line. When designing a data processing plan, you need to consider the data processing strategies you’ll use on the data and whether you’re going to enrich the data with specific attributes. While you’re at it, you’ll also need to consider how you’re going to remove redundant data.

Set up a data storage system

In order to derive meaningful insights from big data, you must first create a repository from which data can be extracted. Here, you need to consider factors like whether to use data lakes or data warehouses, whether to store data on-premises or on the cloud, and the preferable data format.

Plan the data workflow

You need to determine the sequencing of processes for your data pipeline to run efficiently. Here, you need to consider things like if you have jobs in the downstream that require completion of an upstream hob, whether you have jobs running in parallel, and how you plan on handling failed jobs.

Build and implement a data monitoring and governance framework

A data monitoring and governance framework [8] can help you monitor the pipeline to ensure that it is secure, reliable, and performs as intended. Therefore, you’ll need to determine how you’ll mitigate attacks, what you need to monitor, who’ll be in charge of the monitoring, and whether your storage repository is meeting the estimated thresholds.

Design the data consumption layer

At the end of any data pipeline architecture, there are numerous services that consume the processed data. Therefore, it is important to design the consumption layer appropriately so your pipeline runs smoothly. When designing the consumption layer, you need to consider things like the most optimum way to harness and utilize processed data, whether you have all the data you need for your intended use case, and how your data consumption tools connect to the data repository.

Final thoughts on the data pipeline architecture

Before you deploy a data pipeline, you first have to understand your business objectives. With that in mind, you can build a data pipeline architecture that is not only scalable but also flexible enough to keep up with your business’s ever-changing use cases.

Despite being a lengthy and time-consuming process, a well-deployed data pipeline can help your business make decisions faster and more efficiently. It can also help power reporting and analytics to enhance your business’s products and services. Ultimately, this will give you a competitive advantage regardless of your primary industry.

Do you want to know more? Check our big data consulting services.


[1] The Zettabyte World: Securing Our Data-Rich Future. URL: Accessed August 1, 2022
[2] What is a Data Pipeline?. URL: Accessed August 1, 2022
[3] Data Transformation. URL: Accessed August 1, 2022
[4] What is Batch Processing?. URL: Accessed August 1, 2022
[5] Streaming Data. URL: Accessed August 1, 2022
[6] Types of Data Sources. URL: Accessed August 1, 2022
[7] Data Ingestion Methods. URL: Accessed August 1, 2022
[8] Data Governance Framework. URL: Accessed August 1, 2022


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