CommunityDevOps ExchangePartners
10 minutes

The Data Processing Pipeline: Top Benefits of Automated and Efficient Data Management

Written by
Copado Team
Table of contents

Data is a precious commodity and your organization likely stores, handles, and transports a large volume of it every single day. A data processing pipeline is an organized, programmatic method of getting data from its source to its destination(s) while making any necessary transformations along the way. Modern data processing pipelines employ automation to streamline the flow of data and create flexible and scalable pipeline architectures. In this blog, we’ll look at the components of a typical data processing pipeline before explaining the benefits of an automated approach to data management.

What Does a Data Processing Pipeline Look Like?

A basic data processing pipeline typically includes the following stages:

Discovering and Analyzing Data Sources

Data sources are the origination points of the data flowing through your pipeline, including databases, SaaS applications, storage systems, etc. An automated data processing pipeline often uses data discovery or data profiling tools to locate and analyze data across your data sources before it’s pulled into the pipeline. That allows for automated, intelligent processing of data in later stages of the pipeline according to characteristics, such as the data’s structure, value, and risk.


The ingestion stage pulls data from your data sources into the pipeline. It involves processes and technologies like API calls, replication engines, and webhooks. There are generally two methods to ingest data into a data processing pipeline:

  1. Batch ingestion: Data enters the pipeline as a group according to a schedule or in response to external triggers.
  2. Streaming ingestion: Individual data enters the pipeline in real time.


Often, raw data in a pipeline needs to be reformatted or altered in some way to make it compatible with its destination, a process known as transformation. Transformation may occur before the data reaches its destination in an ETL — or extract, transform, load — pipeline. ETLs are commonly used for on-premises data destinations. Data processing pipelines with cloud-based destinations often use ELT — or extract, load, transform — which first loads data to its final destination and then applies transformations.


The pipeline delivers data to its final destination. This happens either before data transformation, in the case of an ETL pipeline, or after with an ELT pipeline. Often, the destination is a data lake or data warehouse. These locations store massive amounts of data for analytics, machine learning, and other big data applications. However, pipelines frequently deliver smaller amounts of data to another application, or even another microservice within the same application. A pipeline may even have the same source and destination. In this case, it serves purely to automatically process and transform the data.


It’s not enough to deliver data to its destination — it needs to be put into a format people can actually use. Dashboard and reporting tools turn data into business insights. 

Data processing pipelines take data from point A to point B and facilitate efficient, automatic data transformation and processing.

The Top Benefits of a Data Processing Pipeline

At a very basic level, the data processing pipeline breaks down data transfer and processing into a series of programmatic steps. This has numerous advantages, including:

  • Automation. Breaking a process down into smaller, repeatable steps facilitates the use of automation. Automating steps in your pipeline will help reduce the human bottlenecks between stages so you can process data much faster. Automation can also consistently apply the correct data governance policies to everything that passes through your pipeline.
  • Efficiency. An automated data pipeline can transport and transform massive amounts of data very quickly. It can also process multiple parallel data streams at the same time. Automated data processing also weeds out redundant and erroneous data, which means your applications and analytics work more efficiently at the end of the pipeline.
  • Flexibility. The data you need to process may come from many different sources and have a variety of different formats and characteristics. A data processing pipeline allows you to work with a large variety of data without worrying about compatibility with destination storage volumes or applications. Automated data pipelines also make it easier to adapt to changes in the pipeline itself, such as new data sources or transformation processes.
  • Analytics. Processing pipelines aggregate and prepare data for analytics so you can get fast and reliable business insights. Additionally, a data processing pipeline enables security and compliance best practices, so you can make use of analytics without exposing sensitive data. 
  • Value. A data processing pipeline allows you to extract more value out of your data by facilitating business analytics, machine learning, and other data science applications. These tools can provide data analysis and visualizations that give you actionable insights, empowering your organization to spot new opportunities, improve operational processes, and make better business decisions.

Implementing a Data Processing Pipeline for Automated and Efficient Data Management

A data processing pipeline helps you manage, transport, and transform data more efficiently. If you need help building a data processing pipeline that follows industry best practices, you should reach out to the experts at Copado Strategic Services. We’ll support you throughout the data pipeline implementation process, so you get an end product that delivers automated and efficient data management.



Book a demo

About The Author

#1 DevOps Platform for Salesforce

We Build Unstoppable Teams By Equipping DevOps Professionals With The Platform, Tools And Training They Need To Make Release Days Obsolete. Work Smarter, Not Longer.

Enhancing Customer Service with CopadoGPT Technology
What is Efficient Low Code Deployment?
Copado Launches Test Copilot to Deliver AI-powered Rapid Test Creation
Cloud-Native Testing Automation: A Comprehensive Guide
A Guide to Effective Change Management in Salesforce for DevOps Teams
Building a Scalable Governance Framework for Sustainable Value
Copado Launches Copado Explorer to Simplify and Streamline Testing on Salesforce
Exploring Top Cloud Automation Testing Tools
Master Salesforce DevOps with Copado Robotic Testing
Exploratory Testing vs. Automated Testing: Finding the Right Balance
A Guide to Salesforce Source Control
A Guide to DevOps Branching Strategies
Family Time vs. Mobile App Release Days: Can Test Automation Help Us Have Both?
How to Resolve Salesforce Merge Conflicts: A Guide
Copado Expands Beta Access to CopadoGPT for All Customers, Revolutionizing SaaS DevOps with AI
Is Mobile Test Automation Unnecessarily Hard? A Guide to Simplify Mobile Test Automation
From Silos to Streamlined Development: Tarun’s Tale of DevOps Success
Simplified Scaling: 10 Ways to Grow Your Salesforce Development Practice
What is Salesforce Incident Management?
What Is Automated Salesforce Testing? Choosing the Right Automation Tool for Salesforce
Copado Appoints Seasoned Sales Executive Bob Grewal to Chief Revenue Officer
Business Benefits of DevOps: A Guide
Copado Brings Generative AI to Its DevOps Platform to Improve Software Development for Enterprise SaaS
Celebrating 10 Years of Copado: A Decade of DevOps Evolution and Growth
Copado Celebrates 10 Years of DevOps for Enterprise SaaS Solutions
5 Reasons Why Copado = Less Divorces for Developers
What is DevOps? Build a Successful DevOps Ecosystem with Copado’s Best Practices
Scaling App Development While Meeting Security Standards
5 Data Deploy Features You Don’t Want to Miss
Top 5 Reasons I Choose Copado for Salesforce Development
How to Elevate Customer Experiences with Automated Testing
Getting Started With Value Stream Maps
Copado and nCino Partner to Provide Proven DevOps Tools for Financial Institutions
Unlocking Success with Copado: Mission-Critical Tools for Developers
How Automated Testing Enables DevOps Efficiency
How to Keep Salesforce Sandboxes in Sync
How to Switch from Manual to Automated Testing with Robotic Testing
Best Practices to Prevent Merge Conflicts with Copado 1 Platform
Software Bugs: The Three Causes of Programming Errors
How Does Copado Solve Release Readiness Roadblocks?
Why I Choose Copado Robotic Testing for my Test Automation
How to schedule a Function and Job Template in DevOps: A Step-by-Step Guide
Delivering Quality nCino Experiences with Automated Deployments and Testing
Best Practices Matter for Accelerated Salesforce Release Management
Maximize Your Code Quality, Security and performance with Copado Salesforce Code Analyzer
Upgrade Your Test Automation Game: The Benefits of Switching from Selenium to a More Advanced Platform
Three Takeaways From Copa Community Day
Cloud Native Applications: 5 Characteristics to Look for in the Right Tools
Using Salesforce nCino Architecture for Best Testing Results
How To Develop A Salesforce Testing Strategy For Your Enterprise
What Is Multi Cloud: Key Use Cases and Benefits for Enterprise Settings
5 Steps to Building a Salesforce Center of Excellence for Government Agencies
Salesforce UI testing: Benefits to Staying on Top of Updates
Benefits of UI Test Automation and Why You Should Care
Types of Salesforce Testing and When To Use Them
Copado + DataColada: Enabling CI/CD for Developers Across APAC
What is Salesforce API Testing and It Why Should Be Automated
Machine Learning Models: Adapting Data Patterns With Copado For AI Test Automation
Automated Testing Benefits: The Case For As Little Manual Testing As Possible
Beyond Selenium: Low Code Testing To Maximize Speed and Quality
UI Testing Best Practices: From Implementation to Automation
How Agile Test Automation Helps You Develop Better and Faster
Salesforce Test Cases: Knowing When to Test
DevOps Quality Assurance: Major Pitfalls and Challenges
11 Characteristics of Advanced Persistent Threats (APTs) That Set Them Apart
7 Key Compliance Regulations Relating to Data Storage
7 Ways Digital Transformation Consulting Revolutionizes Your Business
6 Top Cloud Security Trends
API Management Best Practices
Applying a Zero Trust Infrastructure in Kubernetes
Building a Data Pipeline Architecture Based on Best Practices Brings the Biggest Rewards
CI/CD Methodology vs. CI/CD Mentality: How to Meet Your Workflow Goals
DevOps to DevSecOps: How to Build Security into the Development Lifecycle
DevSecOps vs Agile: It’s Not Either/Or
How to Create a Digital Transformation Roadmap to Success
Infrastructure As Code: Overcome the Barriers to Effective Network Automation
Leveraging Compliance Automation Tools to Mitigate Risk
Moving Forward with These CI/CD Best Practices
Top 3 Data Compliance Challenges of Tomorrow and the Solutions You Need Today
Top 6 Cloud Security Management Policies and Procedures to Protect Your Business
What are the Benefits of Principle of Least Privilege (POLP) for My Organization?
You Can’t Measure What You Can’t See: Getting to know the 4 Metrics of Software Delivery Performance
How the Public Sector Can Continue to Accelerate Modernization
Building an Automated Test Framework to Streamline Deployments