Step-by-Step Guide to Build Data Pipeline

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 such as data warehouses or data lakes, 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 a data pipeline from scratch.

How to build a data pipeline?

Defining pipeline objectives and system architecture

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.

Pipeline objectives should align with business goals, focusing on what insights or outcomes the organization aims to achieve through data processing. This involves identifying the types of data to be ingested, anticipated volumes, and the specific transformations required to convert raw data into actionable insights. Clear objectives help guide the design and implementation of the pipeline, ensuring that it meets both current and future needs.

The system architecture serves as the blueprint for how data flows through the pipeline, detailing each stage from ingestion to storage and consumption. A well-structured architecture is essential for managing the complexities of data processing, allowing for seamless integration of various components such as databases, APIs, and analytics tools. It should emphasize reliability, scalability, and flexibility to accommodate growing data volumes and evolving business requirements.

By carefully defining pipeline objectives and creating a robust system architecture, organizations can optimize their data pipelines for efficiency and effectiveness, ultimately leading to better decision-making and strategic initiatives.

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 (Source: developer.here.com)

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].

Destination

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.

Source: developer.here.com

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

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

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

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.

Monitoring

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 data pipelines. 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 data pipelines effectively.

Source: developer.here.com

Determine your goals

Before building data pipelines, 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 to 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

Numerous services consume the processed data at the end of any data pipeline. 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.

Data ingestion and consumption layer

In fact, data ingestion and consumption layer play a crucial role in ensuring that organizations can effectively harness their data for analysis and decision-making. This layer serves as the gateway through which raw data flows into the pipeline, transforming it into valuable insights that drive strategic initiatives. By efficiently collecting, processing, and making data accessible, this layer lays the foundation for a successful data-driven culture within an organization.

These layers are pivotal in collecting data from various data lakes, data warehouses, and other databases, making it available for analysis.

Data ingestion can be categorized into two primary methods:

• batch processing, where data is collected at scheduled intervals
• real-time processing, which allows for continuous data flow as events occur.

The choice between these methods depends on the specific business requirements, such as the need for timely insights versus the feasibility of processing large datasets at once.

Once data is ingested, it enters the consumption phase, where it becomes accessible for analysis and decision-making. This phase often involves transforming the raw data into a suitable format for querying and visualization.

Tools like ETL (Extract, Transform, Load) processes are commonly employed to ensure that the data is cleansed and standardized before being stored in a data warehouse or lake.

Pipeline automation and implementation best practices

Implementing an automated data pipeline is essential for enhancing efficiency, reducing errors, and accelerating the data processing lifecycle.

Here are some best practices to consider for building a data pipeline automation:

• Define clear objectives: Establish specific goals for your pipeline that align with organizational strategies. This clarity will guide the design and functionality of the pipeline, ensuring it meets business needs effectively.
• Assess current workflows: Analyze existing processes to identify bottlenecks and inefficiencies. Understanding your current workflow helps in designing a pipeline that addresses these challenges and optimizes performance.
• Choose the right tools: Select automation tools that integrate seamlessly with your existing infrastructure. Consider factors such as compatibility, ease of use, and support for automation features to avoid vendor lock-in.
• Implement comprehensive monitoring: Continuous monitoring of the pipeline’s performance is crucial. Utilize tools to track key metrics and set up alerts for failures or anomalies, enabling quick responses to issues that may arise.
• Ensure scalability and flexibility: Design your pipeline to accommodate growth and changes in data volume or processing requirements. Modularizing configurations and using Infrastructure as Code (IaC) can facilitate easier updates and scalability.
• Incorporate security practices: Integrate security checks into your pipeline to ensure compliance and mitigate risks early in the development process. Automating security scans helps maintain a secure codebase throughout the software delivery lifecycle.
• Foster collaboration and communication: Involve cross-functional teams in the pipeline implementation process to enhance understanding and ownership. Shared responsibility leads to quicker resolution of issues and better alignment with business objectives.
• Conduct regular audits: Periodically review the pipeline’s performance and configuration to identify areas for improvement. Regular audits help ensure that the pipeline remains efficient, secure, and aligned with evolving business needs.

Final thoughts on the data pipeline architecture

Building data pipelines requires an in-depth understanding of 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, building a data pipeline gives you a competitive advantage regardless of your primary industry.

This article is an updated version of the publication from Aug 18, 2022.

Step-by-Step Guide to Build Data Pipeline – FAQ

What are data pipelines?

Data pipelines are systems that collect, process, and move data from various sources to a storage destination, such as a data warehouse or data lake. They transform raw data into a usable format for analysis, reporting, and decision-making.

Building a data pipeline. What is the process?

Building a data pipeline requires the following steps:

• Define your business goals.
• Identify your data sources.
• Choose a data ingestion strategy (batch or real-time).
• Design a data processing plan.
• Set up a data storage system.
• Establish a workflow for processing tasks.
• Implement monitoring and governance for data integrity.
• Design the data consumption layer to ensure insights are accessible.

References

[1] Dell.com. The Zettabyte World: Securing Our Data-Rich Future. URL: https://www.dell.com/en-us/perspectives/the-zettabyte-world-ebook/. Accessed August 1, 2022
[2] Ibm.com. What is a Data Pipeline?. URL: https://ibm.co/3bYZ2mP. Accessed August 1, 2022
[3] Sciences.usca.edu. Data Transformation. URL: http://sciences.usca.edu/biology/zelmer/305/trans/. Accessed August 1, 2022
[4] Ibm.com. What is Batch Processing?. URL: https://www.ibm.com/docs/en/zos-basic-skills?topic=jobs-what-is-batch-processing. Accessed August 1, 2022
[5] Amazon.com. Streaming Data. URL: https://go.aws/3QxeIMT. Accessed August 1, 2022
[6] Lumenlearning.com. Types of Data Sources. URL: https://courses.lumenlearning.com/wm-businesscommunicationmgrs/chapter/types-of-data-sources/. Accessed August 1, 2022
[7] Amazon.com. Data Ingestion Methods. URL: https://go.aws/3dBtpjD. Accessed August 1, 2022
[8] Informatica.com. Data Governance Framework. URL: https://infa.media/3SPTFqF. Accessed August 1, 2022