Data pipelines: Critical components and best practices

According to David Menninger, Executive Director of software research at ISG, data integration and data engineering continue to be the biggest challenges in today’s AI and analytics initiatives.

Without robust data pipelines, organizations will struggle to ingest, transform, and govern data at scale, limiting their ability to extract meaningful insights from their data. AI-driven analytics and automation are increasing the demand for real-time, high-quality data pipelines.

Modern data pipelines must be scalable and efficient, especially as businesses rely on analytics—and increasingly AI—to process vast amounts of structured and unstructured data.

What is a data pipeline?

In simplest terms, a data pipeline is a series of steps that move data from one or more data sources to a destination system for storage, analysis, or operational use. The three elements of a pipeline are sources, processing, and destination.

Data pipelines convert raw, messy data into something trustworthy, structured, and ready for analysis. While batch pipelines, which process data on a fixed schedule, can still be useful, today’s data pipelines must be powerful enough to handle huge volumes of data being processed at speed. Thanks to cloud platforms that decouple storage and compute, batch pipelines have largely given way to real-time (streaming) and cloud-native pipelines.

ETL vs ELT pipelines

The shift toward data processing at scale and speed required a new way of transforming data. Traditional ETL (Extract, Transform, Load) architectures transform data before loading it into a warehouse. In contrast, ELT (Extract, Load, Transform) pipelines load raw data into the warehouse, transforming it in parallel using affordable on-demand cloud computing.

The comparison table highlights the key differences between ETL and ELT. Today, many practitioners consider ETL pipelines as a subcategory of data pipelines, used mainly to load legacy databases in the data warehouse. dbt is purpose-built for ELT workflows where data is already loaded into the data warehouse for processing. The remainder of this article focuses primarily on ELT pipelines.

Components of a data pipeline

Data flows through a pipeline from source to destination, passing through multiple stages along the way. While some frameworks simplify this into three steps—ingestion, transformation, and storage—these can be broken down further to reveal the complexity and value of modern data pipelines. The following components reflect that expanded view:

• Ingestion - Ingestion involves selecting and pulling raw data from source systems into a target system for further processing. Data engineers evaluate—either manually or using automation—data variety, volume, and velocity to ensure that only valuable data is ingested.
• Loading - In this step, the raw data lands in the cloud data warehouse or lakehouse platform (e.g. Snowflake, BigQuery, Redshift, or Databricks). We’ve broken out this step from ingestion to emphasize that this the “L” in ELT that allows the data to be transformed within the data repository.
• Transformation - This is where raw data is cleaned, modeled, and tested inside the warehouse. This includes filtering out irrelevant data, normalizing data to a standard format, and aggregating data for broader insights.‍ With dbt, these transformations become modular, version-controlled code. This makes data workflows more scalable, testable, and collaborative.
• Orchestration - Sometimes subsumed into the “transformation” component, automated orchestration schedules and manages the execution of pipeline steps. This ensures transformations run in the right order at the right time.
• Observability and Testing - The observability and testing component includes data quality checks, lineage tracking, and freshness monitoring. These are critical for building trust, ensuring governance, and catching issues before they impact downstream analytics.
• Storage - The transformed data is then stored within a data repository, typically a centralized data warehouse or data lake, where it can be retrieved for analysis, business intelligence, and reporting.
• Analysis - In this last step, data teams ensure that the stored data is ready for analysis, documenting final models, aligning them with a semantic layer, and making them easily discoverable. The semantic layer turns complex data structures into familiar business terms, making it easier for analysts and data scientists to explore data using SQL, machine learning, and BI tools.

Common data pipeline challenges

The point of a data pipeline is to move and transform raw data into reliable, structured insights that drive business decisions. Data scientists and engineers face several common challenges when building and deploying data pipelines.

Lack of observability across the data estate

Today’s modern data stack is complex and fragmented, making it difficult for businesses to gain visibility across their data estate. Without observability, data engineers are flying blind, unable to detect anomalies, trace root causes, assess schema changes, or ensure reliable data for analytics and AI.

Ingestion of low-quality data

When data enters the pipeline with missing values, schema drift, or inconsistent formats, it can silently corrupt insights and models. Without a clear understanding of your data sources—including their reliability, quality, and governance policies—teams risk producing inaccurate outputs and drawing flawed conclusions.

Pipeline scalability

The explosion of data volume and sources presents an ongoing challenge to building fast, reliable data pipelines. Large, monolithic SQL scripts are hard to debug and maintain, while pipeline bottlenecks slow down data processing, delaying insights. The continued use of manual rather than automated processes also negatively impacts scalability and speed.

Untested transformations

When data transformations are deployed without automated testing, even minor logic errors or schema mismatches can later propagate through the pipeline. The result is broken downstream models, inaccurate dashboards and reports, or flawed machine learning outputs.

Lack of trust in data outputs

In complex data pipelines, trust breaks down when consumers struggle to identify reliable models or metrics. Data trust depends not only on accuracy, completeness, and relevance but also on transparency. Without clear lineage, semantic definitions, and documented assumptions, consumers may question a metric’s validity or ignore it altogether.

Best practices for data pipelines

Today’s pipelines are largely built on cloud-native architectures that decouple storage and compute, enabling scalable, cost-efficient data processing. But even with cloud-native infrastructure, challenges in observability, data quality, scalability, and trust persist. The following best practices help teams overcome these obstacles to ensure reliable, high-performance data workflows.

Adopt a data product mindset

Data pipelines aren’t just about moving data—they’re about creating trustworthy, usable outputs that drive business value. ELT data pipelines accelerate this transformation, but today’s organizations must go further by adopting a data product mindset. This approach focuses on delivering well-documented, version-controlled, and consumption-ready data products to the end user, reinforcing data trust and empowering self-service analytics.

How dbt can help

As organizations adopt a data product mindset, ensuring trust, transparency, and usability in pipeline outputs becomes critical. dbt provides key capabilities to reinforce data trust and self-service analytics:

• Gain visibility across the data estate with column-level lineage - dbt Explorer’s column-level lineage helps consumers understand the journey of individual columns from raw input to final analytical models. This transparency builds trust by allowing users to trace data origins and transformations.
• Ensure metric consistency with semantic alignment - dbt’s Semantic Layer centralizes metric definitions, ensuring consistency across all pipelines and datasets. This prevents metric drift and accelerates the process of creating reliable, reusable data products.
• Strengthen governance with centralized workflow management - dbt’s workflow governance enables analysts and engineers to standardize on a single platform, ensuring version control, lineage tracking, and access management. This reinforces trust by making data transformations auditable and reliable.

Ensure end-to-end data quality

Poor-quality data entering a pipeline leads to silent errors, broken dashboards, and flawed insights, eroding trust in analytics. Early data quality checks prevent errors from spreading, while automated validation, anomaly detection, and schema enforcement ensure reliable downstream analytics and applications.

How dbt can help

• Integrate with industry-leading data quality, observability, and governance tools - dbt integrates with leading data quality and data governance tools to ensure that the data entering your pipelines won’t cause downstream errors later.
• Identify pipeline errors early - Without guardrails, teams risk unreliable outputs. dbt tests validate uniqueness, non-null values, and referential integrity. And by treating data like code with automated tests and reviews, you can prevent failures from reaching production.
• Continuously monitor pipelines - With dbt, teams can embrace continuous integration (CI) to test code before deployment and monitor pipeline health. dbt tracks the state of production environments, allowing CI jobs to validate modified models and their dependencies before merging. This reduces the risk of breaking changes and ensures smooth pipeline execution.

Optimize for scalability

Modern data pipelines must adapt to changing data structures, optimize query performance to ensure fast analytics, and support real-time ingestion and integration. This demands balancing scalability with cost. Linear scalability, where teams grow as pipelines proliferate, is unsustainable.

Technologies like Snowflake Snowpipe Streaming and Databricks Lakeflow enable efficient ingestion and transformation by leveraging high-throughput, low-latency processing, ensuring pipelines scale effectively without excessive overhead.

How dbt can help

• Implement modular data transformations - dbt enables modular, version-controlled transformations, making pipelines easier to debug and maintain. Each model is self-contained, making it easier to isolate and fix errors without affecting the entire pipeline.
• Use version control and enable collaboration - dbt’s Git-based version control tracks data changes as code, enabling teams to collaborate, audit, revert updates, and maintain a single source of truth for scalable pipeline management.
• Optimize with incremental model processing - dbt’s incremental model processing transforms only new or updated data, reducing costs, minimizing reprocessing, and improving efficiency. This approach enhances query performance, lowers warehouse load, and accelerates transformations.
• Improve performance with parallel execution - dbt’s parallel microbatch execution processes data in smaller, concurrent batches, reducing processing time and improving efficiency compared to sequential runs.

Automate orchestration for scalable pipelines

Automated orchestration ensures data pipelines run efficiently by synchronizing processes from ingestion to analysis. Event-driven triggers and CI/CD automation detect failures early, reducing downtime and improving reliability. By automating execution, teams can scale pipelines without manual intervention, ensuring consistent and optimized workflows.

How dbt can help

• Automate pipeline execution with state-aware orchestration – dbt Fusion optimizes workflows by running models only when upstream data changes, reducing redundant executions and improving efficiency.
• Build event-driven execution with hooks and macros – dbt’s hooks automate operational tasks like managing permissions, optimizing tables and executing cleanup operations. Macros bundle logic into reusable functions, enabling parameterized workflows and event-driven execution.
• Enable continuous integration (CI) for reliable deployments – dbt integrates with CI/CD workflows, automatically testing modified models and their dependencies before merging to production.

Whether you're building or refining data pipelines, streamlining workflows, and ensuring data trust are key. By implementing dbt as your pipeline backbone, you gain automation, orchestration, and version control, helping you create scalable, reliable pipelines that drive business value.

To learn more about using dbt to implement effective data pipelines, request a demo today.