{"id":11320,"date":"2026-04-29T02:18:04","date_gmt":"2026-04-29T06:18:04","guid":{"rendered":"http:\/\/data-mania.com\/blog\/?p=11320"},"modified":"2026-04-29T02:18:04","modified_gmt":"2026-04-29T06:18:04","slug":"data-pipeline-design-and-build-101","status":"publish","type":"post","link":"https:\/\/www.data-mania.com\/blog\/data-pipeline-design-and-build-101\/","title":{"rendered":"Data Pipeline Design And Build 101"},"content":{"rendered":"<p><span style=\"font-weight: 400\">Curious about how to get started with data pipeline design and build processes? Here\u2019s what you need to know\u2026<\/span><\/p>\n<p><img decoding=\"async\" class=\"alignnone size-full wp-image-11324 lazyload\" data-src=\"http:\/\/data-mania.com\/blog\/wp-content\/uploads\/2022\/06\/Data-Pipeline-Design-and-Build-101.png\" alt=\"data pipeline design\" width=\"2240\" height=\"1260\" data-srcset=\"https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2022\/06\/Data-Pipeline-Design-and-Build-101.png 2240w, https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2022\/06\/Data-Pipeline-Design-and-Build-101-300x169.png 300w, https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2022\/06\/Data-Pipeline-Design-and-Build-101-1024x576.png 1024w, https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2022\/06\/Data-Pipeline-Design-and-Build-101-768x432.png 768w, https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2022\/06\/Data-Pipeline-Design-and-Build-101-90x51.png 90w, https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2022\/06\/Data-Pipeline-Design-and-Build-101-1536x864.png 1536w, https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2022\/06\/Data-Pipeline-Design-and-Build-101-2048x1152.png 2048w, https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2022\/06\/Data-Pipeline-Design-and-Build-101-800x450.png 800w, https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2022\/06\/Data-Pipeline-Design-and-Build-101-600x338.png 600w, https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2022\/06\/Data-Pipeline-Design-and-Build-101-1154x649.png 1154w\" data-sizes=\"auto, (max-width: 2240px) 100vw, 2240px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 2240px; --smush-placeholder-aspect-ratio: 2240\/1260;\" \/><\/p>\n<h2><span style=\"font-weight: 400\">What Is a Data Pipeline?<\/span><\/h2>\n<p><span style=\"font-weight: 400\">A data pipeline enables you to move data from a certain source to another destination. The pipeline transforms and optimizes the data and ships it in a state suitable for analysis. It includes steps that aggregate, organize, and move data, typically using automation to reduce the scope of manual work.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">A continuous data pipeline usually phases through the following tasks:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Loading raw data into a staging table for interim storage<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Transforming the data\u00a0<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Adding the transformed data to the destination reporting tables<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">This basic process may change according to the use case and individual business requirements and needs. Data pipelines are used for a variety of business processes including big data analytics, <\/span><a href=\"https:\/\/www.run.ai\/guides\/machine-learning-operations\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400\">machine learning operations (MLOps)<\/span><\/a><span style=\"font-weight: 400\">, data warehouses, and data lakes.<\/span><\/p>\n<h2><span style=\"font-weight: 400\">Data Pipeline Design Process<\/span><\/h2>\n<p><span style=\"font-weight: 400\">A data pipeline design is a process designed for shifting data from one place to another. The process can change depending on each scenario. For example, a simple data pipeline can include mainly data extraction and loading, while a more advanced pipeline can include training datasets for artificial intelligence (AI) machine learning (ML) use cases.<\/span><\/p>\n<h3><b>Here are key phases typically used in data pipeline design processes:<\/b><\/h3>\n<ul>\n<li style=\"font-weight: 400\"><b>Source<\/b><span style=\"font-weight: 400\">\u2014you can use various data sources for your pipeline, including data from SaaS applications and relational databases. You can set up your pipeline to ingest raw data from several sources using a push mechanism, a webhook, an API call, or a replication engine that can pull data at regular intervals. Additionally, you can set up data synchronization at scheduled intervals or in real-time.<\/span><\/li>\n<li style=\"font-weight: 400\"><b>Destination<\/b><span style=\"font-weight: 400\">\u2014you can use various destinations, including an on-premises data store, a cloud-based data warehouse, a data mart, a data lake, or an analytics or business intelligence (BI) application.<\/span><\/li>\n<li style=\"font-weight: 400\"><b>Transformation<\/b><span style=\"font-weight: 400\">\u2014any operation that changes data is associated with the transformation process. It may include data standardization, deduplication, sorting, verification, and validation. The goal of transformation is to prepare the data for analysis.<\/span><\/li>\n<li style=\"font-weight: 400\"><b>Processing<\/b><span style=\"font-weight: 400\">\u2014this step applies data ingestion models, such as batch processing to collect source data periodically and send it to a destination system. Alternatively, you can use stream processing to source, manipulate, and load data as soon as it is created.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400\"><b>Workflow<\/b><span style=\"font-weight: 400\">\u2014this step includes sequencing and dependency management of processes. Workflow dependencies can be business-oriented or technical.<\/span><\/li>\n<li style=\"font-weight: 400\"><b>Monitoring<\/b><span style=\"font-weight: 400\">\u2014a data pipeline requires monitoring to ensure data integrity. Potential failure scenarios include an offline source or destination and network congestion. Monitoring processes push alerts to inform administrators about these issues.<\/span><\/li>\n<\/ul>\n<p><a href=\"https:\/\/www.data-mania.com\/lptrck-wby-dcc-v1\/\"><img decoding=\"async\" data-pin-nopin=\"nopin\" class=\"alignnone size-full wp-image-10712 lazyload\" data-src=\"http:\/\/data-mania.com\/blog\/wp-content\/uploads\/2022\/02\/monetizing-data-expertise.jpg\" alt=\"monetizing data expertise\" width=\"810\" height=\"275\" data-srcset=\"https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2022\/02\/monetizing-data-expertise.jpg 810w, https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2022\/02\/monetizing-data-expertise-300x102.jpg 300w, https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2022\/02\/monetizing-data-expertise-768x261.jpg 768w, https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2022\/02\/monetizing-data-expertise-90x31.jpg 90w, https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2022\/02\/monetizing-data-expertise-600x204.jpg 600w\" data-sizes=\"auto, (max-width: 810px) 100vw, 810px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 810px; --smush-placeholder-aspect-ratio: 810\/275;\" \/><\/a><\/p>\n<h4><b>Automated pipeline deployment<\/b><\/h4>\n<p><span style=\"font-weight: 400\">Keep in mind that a pipeline is not static. Over time, you will have to iterate on pipeline stages to resolve bugs and incorporate new business requirements. To do this, it is a good idea to keep your entire pipeline and all the tools it comprises using infrastructure as code (IaC) templates. You can then establish an <\/span><a href=\"https:\/\/codefresh.io\/learn\/software-deployment\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400\">automated software deployment<\/span><\/a><span style=\"font-weight: 400\"> process that updates your pipeline whenever changes are needed, without disrupting the pipeline\u2019s operation.<\/span><\/p>\n<h2><span style=\"font-weight: 400\">Types of Data Pipeline Tools<\/span><\/h2>\n<p><span style=\"font-weight: 400\">Various data pipeline tools are available depending on the purpose. The following are some of the popular tool types.<\/span><\/p>\n<h3><span style=\"font-weight: 400\">Batch and Real-Time Tools<\/span><\/h3>\n<p><span style=\"font-weight: 400\">These batch data pipeline tools can move large volumes of data at regular intervals, known as batches. Batch tools can impact real-time operations. That is the reason most people usually prefer these tools for on-prem data sources and use cases that don\u2019t require real-time processing.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Real-time extract, transform and load tools process data quickly and are suited to real-time analysis. They work well with streaming sources.<\/span><\/p>\n<h3><span style=\"font-weight: 400\">Open Source and Proprietary Tools<\/span><\/h3>\n<p><span style=\"font-weight: 400\">Open source tools use publicly available technology and require customization based on the use case. These tools are usually free or low-cost, but you need the expertise to use them. They can also expose your organization to <\/span><a href=\"https:\/\/www.mend.io\/open-source-security\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400\">open source security risks<\/span><\/a><span style=\"font-weight: 400\">.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">Proprietary data pipeline tools suit specific uses and don\u2019t require customization or expertise to maintain.\u00a0<\/span><\/p>\n<h3><span style=\"font-weight: 400\">On-Premises and Cloud Native Tools<\/span><\/h3>\n<p><span style=\"font-weight: 400\">Traditionally, businesses stored all their data on-premises in a data lake or warehouse. On-premise tools are more secure and rely on the organization\u2019s infrastructure.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400\">Cloud native tools can transfer and process cloud-hosted data and rely on the vendor\u2019s infrastructure. They help organizations save resources. The cloud service provider is responsible for security.\u00a0<\/span><\/p>\n<h2><span style=\"font-weight: 400\">Best Practices for Data Pipeline Design and Build<\/span><\/h2>\n<h3><span style=\"font-weight: 400\">Manage the Data Pipeline Like a Project<\/span><\/h3>\n<p><span style=\"font-weight: 400\">Viewing data pipelines as projects, like software development pipelines, is important for making them manageable. Data project managers must collaborate with end-users to understand their data demands, use cases, and expectations. Including data engineers is also important to ensure smooth data pipeline processes.<\/span><\/p>\n<h3><span style=\"font-weight: 400\">Use a Configuration-Based Approach<\/span><\/h3>\n<p><span style=\"font-weight: 400\">You can reduce the coding workload by adopting an ontology-based data pipeline design approach. The ontology (configurations) helps keep the data schema consistent throughout the organization\u2014this approach limits coding to highly complex use cases that a configuration-based process cannot address.<\/span><\/p>\n<h3><span style=\"font-weight: 400\">Keep the Data Lineage Clear<\/span><\/h3>\n<p><span style=\"font-weight: 400\">The data continuously changes when applications evolve, with teams adding or removing fields over time. These constant changes make it difficult to access and process data. Labeling the data tables and columns with logical descriptions and details about their migration history is crucial.<\/span><\/p>\n<h3><a href=\"https:\/\/www.data-mania.com\/data-superhero-quiz\/\" rel=\"attachment wp-att-10190\"><img decoding=\"async\" data-pin-nopin=\"nopin\" class=\"alignnone size-full wp-image-10190 lazyload\" data-src=\"http:\/\/data-mania.com\/blog\/wp-content\/uploads\/2018\/03\/free-data-career-quiz-and-guidance.png\" alt=\"Data superhero quiz\" width=\"810\" height=\"275\" data-srcset=\"https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2018\/03\/free-data-career-quiz-and-guidance.png 810w, https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2018\/03\/free-data-career-quiz-and-guidance-300x102.png 300w, https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2018\/03\/free-data-career-quiz-and-guidance-768x261.png 768w, https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2018\/03\/free-data-career-quiz-and-guidance-90x31.png 90w, https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2018\/03\/free-data-career-quiz-and-guidance-600x204.png 600w\" data-sizes=\"auto, (max-width: 810px) 100vw, 810px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 810px; --smush-placeholder-aspect-ratio: 810\/275;\" \/><\/a><\/h3>\n<h3><span style=\"font-weight: 400\">Use Checkpoints<\/span><\/h3>\n<p><span style=\"font-weight: 400\">Capturing intermediate results during long calculations and implementing checkpoints is useful. For instance, you can store computed values using checkpoints and reuse them later. This method helps reduce the time it takes to re-execute a failed pipeline. It should also make it easier to recompute data as needed.\u00a0<\/span><\/p>\n<h3><span style=\"font-weight: 400\">Divide Ingestion Pipelines into Components\u00a0<\/span><\/h3>\n<p><span style=\"font-weight: 400\">Data engineers can benefit from accumulating a rich source of vetted components to process data. These components offer flexibility, allowing data teams to adapt to changing processing needs and environments without overhauling the entire pipeline. You must ensure continued support for your initiatives by converting the technical benefits of the component-based approach into tangible business value.<\/span><\/p>\n<h3><span style=\"font-weight: 400\">Keep Data Context<\/span><\/h3>\n<p><span style=\"font-weight: 400\">You must keep track of your data&#8217;s context and specific uses throughout the pipeline, allowing each unit to define its data quality needs for various business use cases. You must enforce these standards before the data goes into the pipeline. The pipeline is responsible for ensuring the data context is intact during data processing.<\/span><\/p>\n<h3><span style=\"font-weight: 400\">Plan to Accommodate Change<\/span><\/h3>\n<p><span style=\"font-weight: 400\">Usually, the data pipeline frequently delivers data to a data warehouse or lake that stores data in a text format. When you update individual records, this can often result in duplicates of already delivered data. You must have a plan to ensure your data consumption reflects the up-to-date records without duplicates.<\/span><\/p>\n<h2><span style=\"font-weight: 400\">Conclusion<\/span><\/h2>\n<p><span style=\"font-weight: 400\">In this article, I explained the basics of data pipeline design and build. I also provided some essential best practices to consider as you build your first pipeline:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Manage the data pipeline like a project with an iterative development process<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Use a configuration-based approach and plan for future changes<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Keep data lineage clear and keep the context of data throughout the pipeline<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Use checkpoints to capture intermediate results, enable error checking and recovery<\/span><\/li>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Divide ingestion pipelines into components, to ensure easier updates of pipeline elements<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400\">I hope this will be useful as you take your first steps in a data pipeline project.<\/span><\/p>\n<p>&nbsp;<\/p>\n<p>Hey! If you liked this post, I\u2019d really appreciate it if you\u2019d share the love by clicking one of the share buttons below!<\/p>\n<h2>A Guest Post By&#8230;<\/h2>\n<p><img decoding=\"async\" data-pin-nopin=\"nopin\" class=\"alignleft lazyload\" data-src=\"http:\/\/data-mania.com\/blog\/wp-content\/uploads\/2022\/06\/giladimage.jpg\" alt=\"Gilad David Maayan\" width=\"200\" height=\"200\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 200px; --smush-placeholder-aspect-ratio: 200\/200;\" \/>This blog post was generously contributed to Data-Mania by Gilad David Maayan. Gilad David Maayan is a technology writer who has worked with over 150 technology companies including SAP, Samsung NEXT, NetApp and Imperva, producing technical and thought leadership content that elucidates technical solutions for developers and IT leadership.<\/p>\n<p>You can follow Gilad on <a href=\"https:\/\/www.linkedin.com\/in\/giladdavidmaayan\/\" target=\"_blank\" rel=\"noopener\">LinkedIn<\/a>.<\/p>\n<p>If you&#8217;d like to contribute to the Data-Mania blog community yourself, please drop us a line at communication@data-mania.com.<\/p>\n<hr\/>\n<p><em>Want a clean, repeatable system for measuring B2B growth? Get the free <a href=\"https:\/\/www.data-mania.com\/growth-metrics-os-email-course\/\"><strong>Growth Metrics OS<\/strong><\/a> \u2014 a 6-day email course for technical founders and operators who want to measure growth and make better decisions.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Curious about how to get started with data pipeline design and build processes? Here\u2019s what you need to know\u2026 What Is a Data Pipeline? A data pipeline enables you to move data from a certain source to another destination. The pipeline transforms and optimizes the data and ships it in a state suitable for analysis. [&hellip;]<\/p>\n","protected":false},"author":4,"featured_media":11324,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"gallery","meta":{"_wp_convertkit_post_meta":{"form":"-1","landing_page":"","tag":"0"},"footnotes":"","_links_to":"","_links_to_target":""},"categories":[582],"tags":[570],"class_list":["post-11320","post","type-post","status-publish","format-gallery","has-post-thumbnail","hentry","category-startups","tag-data-pipeline-design","post_format-post-format-gallery"],"_links":{"self":[{"href":"https:\/\/www.data-mania.com\/blog\/wp-json\/wp\/v2\/posts\/11320","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.data-mania.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.data-mania.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.data-mania.com\/blog\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/www.data-mania.com\/blog\/wp-json\/wp\/v2\/comments?post=11320"}],"version-history":[{"count":1,"href":"https:\/\/www.data-mania.com\/blog\/wp-json\/wp\/v2\/posts\/11320\/revisions"}],"predecessor-version":[{"id":20270,"href":"https:\/\/www.data-mania.com\/blog\/wp-json\/wp\/v2\/posts\/11320\/revisions\/20270"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.data-mania.com\/blog\/wp-json\/wp\/v2\/media\/11324"}],"wp:attachment":[{"href":"https:\/\/www.data-mania.com\/blog\/wp-json\/wp\/v2\/media?parent=11320"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.data-mania.com\/blog\/wp-json\/wp\/v2\/categories?post=11320"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.data-mania.com\/blog\/wp-json\/wp\/v2\/tags?post=11320"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}