What is a Data Pipeline?
Last updated: September 26, 2024 Read in fullscreen view
Recommended for you
- 05 Jul 2020
What is Sustaining Software Engineering? 1139 - 21 Dec 2023 Top 12 Low-Code Platforms To Use in 2024 1097
- 20 Mar 2022 What is a Multi-Model Database? Pros and Cons? 1015
- 03 Jul 2022 What is the difference between Project Proposal and Software Requirements Specification (SRS) in software engineering? 907
- 22 Sep 2022 Why is it important to have a “single point of contact (SPoC)” on an IT project? 808
- 16 Jun 2022 Rapid Application Development (RAD): Pros and Cons 774
- 20 Jan 2021 Fail early, fail often, fail cheap, fail safe but always fail forward 659
- 30 Jan 2022 What Does a Sustaining Engineer Do? 518
- 21 Jun 2021 6 Useful Tips To Streamline Business Processes and Workflows 511
- 13 Nov 2021 What Is Bleeding Edge Technology? Are bleeding edge technologies cheaper? 441
- 20 Jan 2022 TIGO Self-Organization Practice: Change Management Workflow 410
- 16 Sep 2022 Examples Of Augmented Intelligence In Today’s Workplaces Shaping the Business as Usual 369
- 11 Nov 2021 What is an IT Self-service Portal? Why is it Important to Your Business? 347
- 31 Dec 2022 The New Normal for Software Development 336
- 17 Jun 2021 What is IT-business alignment? 332
- 13 Feb 2021 Why is TIGOSOFT a software house for Enterprise Application Development? 331
- 01 Mar 2023 What is Unit Testing? Pros and cons of Unit Testing? 329
- 27 Jul 2024 Positive Psychology in the Digital Age: Future Directions and Technologies 315
- 11 Oct 2022 Why choose Billable Viable Product (BVP) over Minimum Viable Product (MVP) 301
- 03 Apr 2021 How digital asset management streamlines your content workflow? 295
- 02 Nov 2021 [Case Study] Streamlined Data Reporting using Tableau 272
- 19 Dec 2023 How AI is Transforming Software Development? 270
- 02 Dec 2024 The Intersection of AI and Business Analytics: Key Concepts to Master in Your Business Analytics Course 228
- 25 Apr 2021 What is outstaffing? 223
- 16 Aug 2022 What is a Headless CMS? 218
- 02 Dec 2022 Success Story: Satsuki - Sales Management Software, back office app for School Subscription Management 214
- 18 Jan 2024 Self-healing code is the future of software development 198
- 03 Sep 2022 The secret of software success: Simplicity is the ultimate sophistication 180
- 18 Jul 2024 The 8 Best ways to Innovate your SAAS Business Model in 2024 177
- 06 Nov 2023 How do you streamline requirement analysis and modeling? 172
- 18 Aug 2024 The Future of Web Development: Emerging Trends and Technologies Every Developer Should Know 165
- 25 Sep 2024 Enhancing Decision-Making Skills with an MBA: Data-Driven Approaches for Business Growth 163
- 10 Apr 2022 What is predictive analytics? Why it matters? 162
- 27 Feb 2025 How AI Agents are Changing Software Development? 161
- 31 Dec 2023 Software Development Outsourcing Trends to Watch Out for in 2024 157
- 01 Jan 2024 The pros and cons of the Centralized Enterprise Automation Operating model 152
- 22 Nov 2024 The Role of AI in Enhancing Business Efficiency and Decision-Making 151
- 03 Nov 2023 Why Is Billable Viable Product An Alternative To Minimum Viable Product? 149
- 10 Sep 2024 AI in Email Marketing: Personalization and Automation 148
- 03 Jan 2024 Why Partnership is important for Growth? 138
- 17 Mar 2025 Integrating Salesforce with Yardi: A Guide to Achieving Success in Real Estate Business 131
- 10 Sep 2024 Leading Remote Teams in Hybrid Work Environments 121
- 31 Dec 2022 Future of Software Development Trends and Predictions for 2023 118
- 30 Jul 2024 The Future of IT Consulting: Trends and Opportunities 115
- 09 Oct 2024 Short-Form Video Advertising: The Secret to Captivating Your Audience 100
- 20 Aug 2025 What Is Agentic AI? The Next Phase of Artificial Intelligence 81
- 23 Jun 2025 AI Avatars in the Metaverse: How Digital Beings Are Redefining Identity and Social Interaction 74
- 20 Feb 2025 How Machine Learning is Shaping the Future of Digital Advertising 68
- 25 Jan 2025 The Decline of Traditional SaaS and the Rise of AI-first Applications 64
A data pipeline refers to the process of ingesting raw data from various sources, filtering it, and finally moving it to a destination for storing or analyzing. The process includes a series of steps that incorporate three main elements: a source, processing steps, and a destination. For example, you may move data from an application to a data lake or data warehouse, or from data storage to an analytics tool.
Data pipelines are becoming more meaningful in environments where microservices are sought after. Microservices are applications with small codebases, and they move data between various applications to make the output more efficient. One data pipeline example could be a social media post that could potentially fuel a social listening report, a sentiment analysis, and a marketing app that counts brand mentions. Even though the data source is singular, it can feed different pipelines and bring out helpful insights.
Data Pipeline Process
Data pipeline processes vary from business case to business case. It may be a straightforward process of data extraction and loading or it could be a complex data flow in which data is processed for the purpose of training machine learning models. The process consists of the following elements:
Source
Data sources comprise relational databases and SaaS applications whereby the data is pulled from multiple sources via an API call, webhook, or a push mechanism. Depending on the business case, data can be moved in real-time or at certain intervals that are relevant to the concrete goal. Businesses can choose between various platforms like AWS S3, Google Bucket, and Azure blob storage delivered by the cloud service provider of their preference.
Processing
When it comes to processing, there are two main data pipeline architectures: batch processing, which involves gathering data at certain intervals and wiring it to the destination, and stream processing, which sends data as soon as it is generated. There is a third data pipeline example that combines both aforementioned models. Lambda is a pipeline component that facilitates real-time processing while providing a platform to store and access historical batch data. This way, developers can create additional pipelines to correct errors in previous pipelines or wire them to new destinations if a new business case emerges.
Transformation
Data transformation brings datasets to a format that is required for a certain business goal. This step involves standardizing datasets, sorting them out, removing duplicates, and validating and verifying information. Another vital aspect of data transformation is normalization and denormalization where the former refers to removing data redundancy and the latter combines data into a single source. The purpose of this step is to pave the way to further analyze datasets in the most efficient way.
Destination
Destination varies depending on the logic of the data pipeline. In some cases, it may land in a data lake, data warehouse, or data mart while in others it can go to analytics tools. For example, platforms like Google Big Query, AWS Glue, Lambda, AWS EMR, Azure Bus transfer, etc. are commonly used for analytic purposes.
Monitoring
Monitoring is an essential part of a well-functioning data pipeline as it adds quality assurance to the process. Administrators must be notified when something goes wrong that can jeopardize data integrity. For example, a source can go offline and stop fueling the pipeline with new information. The most prominent monitoring tools are Looker and Tableau.
Main Features of Data Pipelines
An efficient data pipeline needs to encompass a list of features that make the team more productive and the results more viable.
Cloud-based Architecture
Cloud-based solutions simplify and optimize Big Data ETL setup and management and allow companies to focus on business goals rather than technical tasks. Cloud providers carry the responsibility for the rollout and support of services and architectures and maintain their efficiency.
Continuous Data Processing
Market trends change rapidly, and the need for real-time data processing is becoming more critical. Modern data pipelines collect, transform, process, and move data in real-time, which allows companies to have access to the most relevant and recent information. On the other side of the spectrum is batch processing, which delivers data with a few hours or even days of delay. This can be hazardous to the business as trends change incredibly quickly. Real-time data processing offers a competitive edge and helps businesses make data-driven decisions—especially businesses that rely on real-time data, such as logistics firms who are required to adopt this type of processing power.
Fault-tolerant Architecture
Fault-tolerant architecture refers to an automatic backup plan that turns on another node in case the original one fails. This feature protects businesses from unexpected failures and substantial monetary losses. These types of pipelines offer more reliability and availability and guarantee stable and smooth processing.
Big Data Processing
The amount of data generated on a daily basis is enormous and unimaginable, and 80% of this data is unstructured or semi-structured. An efficient data pipeline needs to have the capacity to process large data volumes of unstructured data, such as sensor or weather data, as well as semi-structured data such as JSON, XML and HTML. On top of that, a pipeline has to be able to transform, clean, filter, and aggregate datasets in real-time, which requires significant amounts of processing power.
DataOps and Automation
DataOps is a set of methodologies and principles that helps developers shorten the development cycle. It does so by automating the processes across the entire lifecycle. In the long run, such pipelines are easier to scale up and down, run tests with, and modify in the pre-deployment phase.
Data Democratization
Data democratization opens up access to the data across the company and removes gatekeeping that hinders the development of the business. Instead of only a few executives having access to important information, everyone that works at the company can analyze the data and use it for their decision-making.
Explore more on these topics
Guest Blogging: The Definitive Guide
Top Trending Topics
Share This
Link copied!
Recently Updated News