What is a Data Pipeline? Types, Components and Architecture

In today’s data-driven world, siloed information is the enemy of business productivity and innovation. As companies adopt an ever-expanding set of Cloud platforms and SaaS tools, from CRMs to marketing automation to business intelligence, their data becomes fragmented across disconnected systems.

This leads to the pernicious problem of data silos – islands of data that can’t talk to each other. Data pipelines provide the bridge across these silos, enabling the continuous flow of data between systems.

In this article, we will unpack everything you need to know about data pipelines – what they are, what components they require, their different architectural patterns, and how they can help consolidate your organization’s data to unlock more value.

What is a Data Pipeline?

A Data Pipeline is a means of transferring data where raw data from multiple sources is ingested and loaded to a central repository such as data lakes, databases, data warehouses, or a destination of your choice. A data pipeline generally consists of multiple steps, such as data transformation, where raw data is cleaned, filtered, masked, aggregated, and standardized into an analysis-ready form that matches the target(destination) schema. A data pipeline can also be set up to replicate data from an application to a data warehouse or a data lake to an analytics platform. 

An organization might have a single source feeding multiple data pipelines, which could provide data to several destination systems. As data often contains sensitive information, data pipelines are implemented with security protocols and regulations in place to protect the data. Multiple data quality checks are applied throughout different stages of the pipeline to ensure data completeness, accuracy, and reliability.

What are the Components of a Data Pipeline?

A data pipeline consists of the following components:

Origin: The entry point for data from sources like APIs, IoT sensors, or storage systems (e.g., Data Warehouses, Data Lakes).

Destination: The final target where data is delivered, typically a Data Warehouse, Data Lake, or Business Intelligence tool.

Dataflow: The movement and transformation of data through ETL (Extract, Transform, Load):

• Extract: Retrieve data from sources such as MySQL, MongoDB, or CRM/ERP tools.
• Transform: Cleanse, filter, validate, and reformat data for analysis, performing tasks like de-duplication, encryption, and table joins.
• Load: Store the transformed data in destinations like Amazon Redshift, BigQuery, or Snowflake.

Storage: Systems used to hold data at different stages in the pipeline.

Processing: The handling of data ingestion, transformation, and loading into the destination.

Workflow: Defines the order and dependencies between pipeline processes.

Monitoring: Ensures all pipeline stages are functioning correctly.

Technology: Tools enabling efficient pipelines:

• ETL Tools: Hevo, Talend, Apache Spark.
• Data Warehouses: Amazon Redshift, BigQuery.
• Data Lakes: IBM Data Lake, MongoDB Atlas.
• Batch Workflow Schedulers: Airflow, Luigi, Oozie.
• Streaming Tools: Apache Kafka, Flink.
• Languages: Python, Java for coding processes.

Data Pipeline Architecture

A data pipeline architecture provides a complete blueprint of the processes and technologies used to replicate data from a source to a destination system, including data extraction, transformation, and loading. A common data pipeline architecture includes data integration tools, data governance and quality tools, and data visualization tools. A data pipeline architecture aims to enable efficient and reliable movement of data from source systems to target systems while ensuring that the data is accurate, complete, and consistent.

Data Pipeline Architecture Examples

The most common examples of a data pipeline architecture is a batch-based data pipeline. In this scenario let us consider an application like a point-of-sale system that produces multiple data points to be transferred to both data warehouse and BI Tools. Here is what the example will look like:

Let’s take another example of a streaming data pipeline where the data is processed from the point of sales system as soon as it is generated. The stream processing engine has the capability to feed the output from the pipeline to various destinations, including data stores, marketing applications, CRMs, and other relevant systems. Additionally, it can loop back information to the point-of-sale system.

Lambda architecture, the third type, includes both batch and streaming pipelines in one architecture. The Lambda Architecture is widely adopted in big data settings as it provides a solution for addressing both real-time streaming scenarios and historical batch analysis.

A crucial feature of this architecture is its promotion of storing data in a raw format. This approach allows ongoing execution of new data pipelines to rectify any code errors in previous pipelines or to establish new data destinations, facilitating the exploration of new types of queries.

What are the Types of Data Pipelines?

Now that you have understood what is Data Pipeline and ETL. Lets’s read about different types of data pipelines:

• Batch: Batch processing of data is leveraged when businesses want to move high volumes of data at regular intervals. Batch processing jobs will typically run on a fixed schedule (for example, every 24 hours), or in some cases, once the volume of data reaches a specific threshold.

• Real-time: Real-time Pipelines are optimized to process the necessary data in real-time, i.e., as soon as it is generated at the source. Real-time processing is useful when processing data from a streaming source, such as data from financial markets or telemetry from connected devices.

• Cloud-native: These pipelines are optimized to work only with Cloud-based data sources, destinations, or both. These pipelines are hosted directly in the Cloud, allowing businesses to save money on infrastructure and expert resources.
• Open-source: These pipelines are considered to be suitable for businesses that need a low-cost alternative to commercial pipelines or wish to develop a pipeline to fit their unique business and data requirements. However, these pipelines require the support of trained professionals for their development and maintenance.

However, it is important to understand that these types are not mutually exclusive. This means that a Pipeline can have all characteristics of two different types. For example, Pipelines can be Cloud-native Batch Processing or Open-Source Real-time processing, etc.

Data Pipeline vs. ETL

ETL (Extract, Transform, Load):

• Extract: Retrieves data from source systems such as databases, applications, or APIs.
• Transform: Cleans, validates, and modifies the data to meet specific business or analytical requirements (e.g., filtering, formatting, and aggregating).
• Load: Stores the transformed data into a target system, typically a Data Warehouse or Database, for analysis and reporting.

Data Pipeline:

• A broader framework that includes ETL but is not limited to it.
• Purpose: Transfers data between systems, which may include data lakes, cloud storage (like AWS S3), or real-time analytics tools.
• Flexibility: Data may or may not undergo transformation during transit.
• Processing: Supports both batch (scheduled intervals) and real-time (continuous stream) data processing.
• Destinations: Data can be routed to multiple endpoints, including Data Warehouses, Data Lakes, Business Intelligence tools, or cloud storage services like AWS S3.

What are the Benefits of a Data Pipeline?

When companies don’t know what a data pipeline is, they manage their data in an unstructured and unreliable way. But as they start to scale or search for better solutions, they came to know what a data pipeline is and how it helps companies save time and keep their data organized always. A few advantages of data pipelines are listed below:

• Data Quality: The data flows from source to destination can be easily monitored and accessible, and meaningful to the end-users. 
• Incremental Build: Pipelines allow users to create dataflows incrementally. You can pull even a small slice of data from the data source to the user.
• Replicable Patterns: It can be reused and repurposed for new data flows. They are a network of pipelines that creates a way of thinking that sees individual Pipelines as examples of patterns in a wider architecture. 

Features of Modern Data Pipelines

•  Cloud warehouses like Google BigQuery, Snowflake, and Amazon Redshift scale on demand, handling fluctuating workloads efficiently.
• Cloud tools offer user-friendly monitoring for data engineers to manage failures and anomalies.
• Analysts can perform in-warehouse data transformations using SQL, reducing reliance on technical teams.
• No-code ETL platforms like Hevo Data provide 24/7 automated pipelines, ensuring seamless data flow for faster insights.

Data Pipeline Tools

Building a data pipeline from scratch isn’t always the best option; automated tools can simplify the process. Data pipeline tools fall into three main categories:

• Batch vs. Real-Time: Tools like Talend and Pentaho process data in batches, causing latency, while real-time tools like Hevo Data and Confluent provide immediate insights from streaming data.
• Open Source vs. Proprietary: Open-source tools like Apache Spark allow customization but require technical expertise, while proprietary tools like Hevo Data and Stitch Data offer ready-to-use solutions.
• On-Premise vs. Cloud-Based: On-premise pipelines offer more control and security, while cloud-based tools like Hevo Data and Stitch Data provide flexibility with secure data replication.

Hevo’s Solution For Data Pipelines

• Hevo is a completely automated, no-code data pipeline.
• It has features like schema auto mapping, and pre load transformations making transferring of data hassle free.
• Hevo’s simple drag and drop interface makes it easy to use even by non-technical users. There is a choice to write custom Python codes to do transformations too.
• Hevo can migrate full load, historical load and has a feature of CDC, thus loading incremental load too.

Use Cases of Data Pipelines

• Real-time Customer 360 Analytics: Hevo integrates data from CRM, marketing, and customer support platforms for a full customer view. A SaaS company improved retention by unifying customer data in real time.

• Automating Financial Reporting: Finance teams automate data pipelines from systems like QuickBooks and Stripe, streamlining reports. A fintech startup reduced manual work and improved reporting accuracy by 30%.

• Marketing Analytics and Attribution: Hevo centralizes marketing data from ad platforms and CRMs, enabling better campaign performance tracking. A digital agency optimized spend by 20% using attribution models.

• Supply Chain Optimization: Hevo integrates ERP, e-commerce, and inventory systems for better demand forecasting. An e-commerce company reduced out-of-stock events by 15% through improved inventory management.

• Product Analytics for SaaS: SaaS companies sync product usage data for advanced analytics. One SaaS team identified key retention features, boosting user engagement by 25%.

Data Pipeline Best Practices

You can avoid the common pitfalls of poorly designed data pipelines by adopting the following best practices during the data pipeline design phase:

• Easy Troubleshooting: Eliminating unnecessary dependencies in a data pipeline allows for easy failure tracing, enhancing predictability.
• Scalability: An ideal pipeline architecture should flexibly scale to accommodate varying workloads and exponentially increasing data volumes.
• End-to-End Visibility: Continuous monitoring and quality checks ensure consistency and proactive security.
• Testing: After implementing quality checks, use a stable test set in a separate environment to compare with the production version of the pipeline and the new version.
• Maintenance: Adopting repeatable processes and strict protocols promotes a maintainable data pipeline for the long term.

Learn More about,

• What is aws data pipeline
• Top 5 Free Open-source ETL Tools

Final Thoughts

• Define your business requirements before selecting the right data pipeline solution.
• For rare data replication, custom connectors can be built by the engineering team.
• When frequent data updates and complex transformations are needed, maintaining custom pipelines becomes time-consuming and resource-heavy.
• Automated, no-code data pipeline tools like Hevo Data, with 150+ plug-and-play integrations, offer a more efficient solution for handling large, complex datasets.

Hevo Data is a No-code Data Pipeline that offers a fully managed solution to set up data integration from 150+ data sources (including 60+ free data sources) to numerous Business Intelligence tools, Data Warehouses, or a destination of choice. It will automate your data flow in minutes without writing any line of code. You can also perform post-load transformations from the comfort of Hevo’s interface. 

Explore a 14-day free trial to see how Hevo can help your organisations data migrate with ease and simplicity. Check out the Hevo pricing details to understand which plan fulfills all your business needs.

Share your experience of learning about data pipelines! Let us know in the comments section below!

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