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.

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What are the Components of a Data Pipeline?

Now that you know what a data pipeline is, let’s read about its components. The components of a Pipeline are as follows:

  • Origin: Origin is the point of entry for data from all data sources in the pipeline. Most pipelines have transactional processing applications, application APIs, IoT device sensors, etc., or storage systems such as Data Warehouses, Data Lakes, etc. as their origin.
  • Destination: This is the final point to which data is transferred. The final destination depends on the use case. The destination is a Data Warehouse, Data Lake, or Data Analysis and Business Intelligence tool for most use cases.
  • Dataflow: This refers to the movement of data from origin to destination, along with the transformations that are performed on it. One of the most widely used data flow approaches is ETL (Extract, Transform, Load). The three phases in ETL are as follows:
    • Extract: Extraction can be defined as the process of gathering all essential data from the source systems. For most ETL processes, these sources can be Databases such as MySQL, MongoDB, Oracle, Customer Relationship Management (CRM), Enterprise Resource Planning (ERP) tools, or various other files, documents, web pages, etc.
    • Transform: Transformation can be defined as the process of converting the data into a format suitable for analysis such that it can be easily understood by a Business Intelligence or Data Analysis tool. The following operations are usually performed in this phase:
      • Filtering, de-duplicating, cleansing, validating, and authenticating the data.
      • Performing all necessary translations, calculations, or summarizations on the extracted raw data. This can include operations such as changing row and column headers for consistency, standardizing data types, and many others to suit the organization’s specific Business Intelligence (BI) and Data Analysis requirements.
      • Encrypting, removing, or hiding data governed by industry or government regulations.
      • Formatting the data into tables and performing the necessary joins to match the Schema of the destination Data Warehouse.
    • Load: Loading can be defined as the process of storing the transformed data in the destination of choice, normally a Data Warehouse such as Amazon Redshift, Google BigQuery, Snowflake, etc.
  • Storage: Storage refers to all systems that are leveraged to preserve data at different stages as it progresses through the pipeline.
  • Processing: Processing includes all activities and steps for ingesting data from sources, storing it, transforming, and loading it into the destination. While data processing is associated with the data flow, this step focuses on the implementation of the data flow.
  • Workflow: Workflow defines a sequence of processes along with their dependency on each other in the Pipeline.
  • Monitoring: The goal of monitoring is to ensure that the Pipeline and all its stages are working correctly and performing the required operations.
  • Technology: These are the infrastructure and tools behind Data Flow, Processing, Storage, Workflow, and Monitoring. Some of the tools and technologies that can help build efficient Pipelines are as follows:
    • ETL tools: Tools used for Data Integration and Data Preparation, such as Hevo, Informatica PowerCenter, Talend Open Studio, Apache Spark, etc.
    • Data Warehouses: Central repositories that are used for storing historical and relational data. A common use case for Data Warehouses is Business Intelligence. Examples of Data Warehouses include Amazon Redshift, Google BigQuery, etc.
    • Data Lakes: Data Lakes are used for storing raw Relational or Non-relational data. A common use case for Data Lakes in Machine Learning applications being implemented by Data Scientists. Examples of Data Lakes include IBM Data Lake, MongoDB Atlas Data Lake, etc.
    • Batch Workflow Schedulers: These schedulers give users the ability to programmatically specify workflows as tasks with dependencies between them to automate and monitor these workflows. Examples of Batch Workflow Schedulers include Luigi, Airflow, Azkaban, Oozie, etc.
    • Streaming Data Processing Tools: These tools are used to handle data continuously generated by sources that must be processed as soon as it is generated. Examples of Streaming Data Processing tools include Flink, Apache Spark, Apache Kafka, etc.
    • Programming Languages: These are used to define pipeline processes as code. Python and Java are widely used to create Pipelines.

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:

Data Pipeline Architecture
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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.

Data Pipeline Architecture
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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.

Data Pipeline Architecture
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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 and Pipeline are terms that are often used interchangeably. ETL stands for Extract, Transform, and Load. When comparing Data Pipeline vs. ETL, ETL pipelines are primarily used to extract data from a source system, transform it based on requirements and load it into a Database or Data Warehouse, primarily for Analytical purposes.

Then What is a data pipeline? It can be seen as a broader term that encompasses ETL as a subset. It refers to a system that is used for moving data from one system to another. This data may or may not go through any transformations. Based on business and data requirements, it may be processed in batches or in real-time.

This data might be loaded onto multiple destinations, such as an AWS S3 Bucket or a Data Lake.

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

Modern data pipelines offer the following features that provide a more elegant and efficient way of replicating data:

  • Cloud data warehouses like Google BigQuery, Snowflake, and Amazon Redshift offer demand scaling with a robust analytical engine that can effectively handle fluctuating workloads without compromising performance.
  • Several cloud solutions provide user-friendly solutions to data engineers for monitoring and handling unusual scenarios and failure points.
  • With transformations possible in the data warehouse, data analysts can eliminate the dependency on the technical team and perform the transformations using SQL queries.
  • Modern cloud-based No-code ETL solutions like Hevo Data provide a completely automated data pipeline where they monitor it 24/7 and ensure that data flows seamlessly while you focus on getting insights from your freshly available data.

Use Cases of Data Pipelines

There are multiple examples in various industries where data pipelines have been implemented to streamline data flow across the business:

  • The finance sector has various use cases for data pipelines. You can integrate data from stock prices and transaction data and load it into a data warehouse for analysis and reporting, which can further be used for risk management, fraud detection, and compliance monitoring.
  • In the healthcare industry, you can use data pipelines to integrate data from electronic health records and lab results and use it for patient monitoring, population health management, and clinical research.
  • You can effectively manage inventory, customer segmentation, and personalized marketing by integrating customer data from e-commerce platforms and point-of-sale systems.

A few real-life examples of organizations that implemented modern data pipelines for their use case are:

  • Uber needs real-time data to execute dynamic pricing, calculate the maximum estimated time of arrival, and forecast demand and supply. Using technologies like Apache Flink, they run streaming pipelines that ingest current data from driver and passenger apps. This real-time data is fed to machine learning models to generate predictions minute by minute.
  • Hewlett Packard(HP) Enterprise wanted to improve customer experience with the predictive maintenance feature. They achieved this by building a data pipeline using streaming engines such as Akka Streams, Apache Spark, and Apache Kafka.
  • Dollar Shave Club needed data in real-time to feed their recommender system to define which products to promote and how to rank them in a monthly email sent to a specific customer. They built an automated data pipeline on Apache Spark to solve this problem.

Data Pipeline Tools

Building a data pipeline from scratch might not always be the most effective choice. You can simply process or get a completely automated data pipeline using several data pipeline tools available in the market. The different types of data pipelines can be classified into 3 categories:

  • Batch vs. real-time data pipeline tools: Tools like Talend and Pentaho allow you to transfer data in large chunks(batches) at regular intervals. However, there is data latency, and you won’t get real-time outputs. If your business requires real-time analytics, then you can opt for real-time data pipeline tools like Hevo Data and Confluent. For instance, you can extract data streaming sources, such as your mobile application, to get insights into user interactions.
  • Open Source vs. Proprietary Data Pipeline Tools: With source code freely available to the public, open-source tools like Apache Spark allow you to make customizations according to your business needs. However, you would need to have the expertise to write the custom scripts and maintain the data pipelines. Proprietary tools like Hevo Data and Stitch Data are already customized to the user’s needs and can be directly put to use for multiple use cases.  
  • On-Premise vs. Cloud-Based Pipeline Tools: Traditionally, data has been stored in on-premise databases. As businesses have control over the data infrastructure, on-premise data pipelines are often more secure. Cloud-based tools allow you to replicate data from your cloud applications and databases to cloud data warehouses and data lakes. Though tools like Hevo Data and Stich Data also offer reliable data protection with all the security protocols and regulations in place.

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: By eliminating unnecessary dependencies between components of a data pipeline allows you to easily trace back to the point of failure, thereby enhancing data pipeline predictability.
  • Scalability: With varying workloads and data volumes increasing exponentially, an ideal data pipeline architecture should flexibility scale.
  • End-to-End Visibility: With continuous monitoring and quality checks, you can ensure consistency and proactive security.
  • Testing: After carrying out changes based in the quality checks, you have a stable data set to run through the pipeline. After defining a test set, you can use it in a separate testing environment and compare it through the production version of your data pipeline and a second time with your new version.
  • Maintenance: Repeatable processes and following strict protocols promotes a maintainable data pipeline for the coming years. 

Data Pipeline FAQs

Here’s a list of frequently asked questions based on data pipelines:

How do I maintain and troubleshoot a data pipeline?

Maintaining a data pipeline involves monitoring performance, detecting and resolving errors, and updating the pipeline as data sources and processing needs change. It is important to have good logging and error handling in place and to have a process for troubleshooting and resolving issues.

What is Data Pipeline Automation?

Data pipeline automation refers to the process of utilizing technology and tools to streamline, manage, and execute the tasks involved in the creation, maintenance, and operation of data pipelines. A data pipeline is a set of processes that move and transform data from one system to another, often from source systems to a destination such as a data warehouse or a data lake.

How can I scale my data pipeline to handle increasing amounts of data?

There are multiple ways to scale your data pipelines, namely:

  • Horizontal Scaling: Add more machines or nodes to handle the increased data volume.  
  • Vertical Scaling: Add more resources, such as memory or storage, to existing machines or nodes.
  • Partitioning: Partition the data into smaller chunks that can be processed in parallel by different parts of the pipeline.
  • Caching: Use caching to temporarily store frequently accessed data in memory, reducing the need to read it from storage.
  • Data Compression: Compress the data to reduce its storage footprint and speed up data transfer times.

How can I ensure data security in my data pipeline?

Here’s a short list of safety measures you can introduce in your data pipelines:

  • Data Encryption: Encrypt sensitive data both at rest and in transit to protect it from unauthorized access. This can be achieved by using technologies like AES, RSA, or SSL/TLS.
  • Secure Authentication and Authorization: Implement secure authentication and authorization mechanisms to control access to the data pipeline and ensure that only authorized users can access the data.
  • Data Masking: Mask sensitive data to protect it from unauthorized access. This can be achieved by using techniques like tokenization, data obfuscation, or data redaction.
  • Network Security: Implement network security measures such as firewalls and intrusion detection systems to protect the data pipeline from external threats.
  • Regular Security Updates: Keep all the systems and software used in the pipeline updated with the latest security patches to fix any known vulnerabilities.
  • Data Governance: Implement data governance policies and procedures to ensure compliance with data security regulations and standards.

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Final Thoughts

From defining data pipelines to building the right pipelines for your business, you now have a comprehensive knowledge base ready for implementation. After you have jotted down the business requirements, you select the best data pipeline solution. For cases when you need to replicate data rarely, you can ask your engineering team to build custom connectors. However, when you need fresh data every few hours from a sea of sources with complex data transformations, monitoring and maintaining custom data pipelines is a time-consuming and resource-intensive task. To remedy that, you can opt for an automated data pipeline offered by cloud-based no-code tools like Hevo Data which offers 150+ plug-and-play integrations. 

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Saving countless hours of manual data cleaning & standardizing, Hevo Data’s pre-load data transformations get it done in minutes via a simple drag and drop interface or your custom python scripts. No need to go to your data warehouse for post-load transformations. You can run complex SQL transformations from the comfort of Hevo’s interface and get your data in the final analysis-ready form. 

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

Manik Chhabra
Former Research Analyst, Hevo Data

Manik has a keen interest in data, software architecture, and has a flair for writing hightly technical content. He has experience writing articles on diverse topics related to data engineering and infrastructure. The problem solving and analytical thinking ability combined with the impact he can make in data professional's day to day life motivate him to create content.