A Guide to Data Pipelines (And How to Design One From Scratch)

What are Data Pipelines? 

A data pipeline is a systematic sequence of components designed to automate the extraction, organization, transfer, transformation, and processing of data from one or more sources to a designated destination. Dmitriy Rudakov, Director of Solutions Architecture at Striim, describes it as “a program that moves data from source to destination and provides transformations when data is inflight.” 

Benjamin Kennady, Cloud Solutions Architect at Striim, emphasizes the outcome-driven nature of data pipelines. “A data pipeline can be thought of as the flow of logic that results in an organization being able to answer a specific question or questions on that data,” he shares. “This question could be displayed in a dashboard for decision makers or just be a piece of the required puzzle to answer a larger question.” 

Because of this, data pipelines are vital when data is stored in formats or locations that hinder straightforward analysis. As Kennady notes, “The reason a pipeline must be used in many cases is because the data is stored in a format or location that does not allow the question to be answered.” The pipeline transforms the data during transfer, making it actionable and enabling your organization to answer critical questions.

AI and Data Pipelines

Another quintessential function of data pipelines is for integrating artificial intelligence (AI) into organizational processes, enabling the seamless flow of data that powers AI-driven insights. Because AI models require vast amounts of data to learn, adapt, and make predictions, the efficiency and robustness of data pipelines directly impact the quality of your organization’s AI outcomes. 

A well-designed data pipeline ensures that data is not only transferred from source to destination but also properly cleaned, enriched, and transformed to meet the specific needs of AI algorithms.

Why are data pipelines important? 

Without well-engineered, scalable, and robust data pipelines, your organization risks accumulating large volumes of data in scattered locations, making it difficult to process or analyze effectively. Instead of being a valuable resource, this data becomes a bottleneck, hindering your ability to innovate and grow.

Kennady adds, “The capability of a company to make the best decisions is partly dictated by its data pipeline. The more accurate and timely the data pipelines are set up allows an organization to more quickly and accurately make the right decisions.” 

Data Pipeline Use Cases

Data pipelines are integral to virtually every industry today, serving a wide range of functions from straightforward data transfers to complex transformations required for advanced machine learning applications. Whether it’s moving data from a source to a destination or preparing it for sophisticated recommendation engines, data pipelines are the backbone of modern data architectures.

Some use cases where building data pipelines is crucial include:

• Processing and storing transaction data to power reporting and analytics to enhance business products and services
• Consolidating data from multiple sources (SaaS tools, databases) to a big data store (data warehouses, data lakes) to provide a single source of truth for the organization’s data
• Improving overall backend system performance by migrating data to large data stores, reducing the load on operational databases
• Ensuring data quality, reliability, and consistency for faster data access across business units

What are Six Key Data Pipeline Components? 

Understanding the essential components of data pipelines is crucial for designing efficient and effective data architectures. These components work in tandem to ensure data is accurately ingested, transformed, and delivered, supporting everything from real-time analytics to machine learning applications. Here are six key components that are fundamental to building and maintaining an effective data pipeline.

Data Sources

The first component of a modern data pipeline is the data source, which is the origin of the data your business leverages. This can include any system or application that generates or collects data, such as:

• Behavioral Data: User behavior data that provides insights into how customers interact with your products or services.
• Transactional Data: Sales and product records that capture critical business transactions and operations.
• Third-Party Data: External data sources that your company does not collect directly but integrates to enhance insights or support decision-making.

These data sources serve as the starting point for the pipeline, providing the raw data that will be ingested, processed, and analyzed.

Data Collection/Ingestion 

The next component in the data pipeline is the ingestion layer, which is responsible for collecting and bringing data into the pipeline. This critical step leverages data ingestion tools to interface with diverse data sources, both internal and external, using various protocols and formats.

The ingestion layer supports multiple data types and formats, including:

• Batch Data: Data collected and processed in discrete chunks, typically from static sources such as databases or logs. Historically, batch processing was sufficient for many use cases. However, in today’s fast-paced environment, where real-time insights are crucial, batch data can become outdated by the time it is processed. This delay limits the ability to respond to immediate business needs or emerging trends. 
• Streaming Data: Real-time data that continuously flows from sources such as IoT devices, sensors, or live transaction feeds. This data requires immediate processing to provide up-to-the-minute insights and enable timely decision-making, making it the ideal choice for modern businesses. 

“Data pipelines can be thought of as two different types: batch loading and continuous replication,” says Kennady. “Continuous replication via CDC is an event driven architecture. This is a more efficient data pipeline methodology because it only gets triggered when there is a change to the source.” 

Striim, for instance, facilitates the seamless integration of real-time streaming data from various sources, ensuring that it is continuously captured and delivered to big data storage targets. This real-time capability is essential in today’s environment, where immediate insights and rapid response are crucial for staying competitive and making timely decisions. Furthermore, Striim also supports real-time data replication and real-time analytics, which are both crucial for your organization to maintain up-to-date insights. By efficiently handling data ingestion, this component sets the stage for effective data processing and analysis. 

Data Processing 

That brings us to our next step: Data processing. The processing layer is responsible for transforming data into a consumable state through various operations, including validation, clean-up, normalization, transformation, and enrichment. The approach to this processing depends on the data pipeline architecture, specifically whether it employs ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) processes.

In an ETL-based architecture, data is first extracted from source systems, then transformed into a structured format, and finally loaded into data stores, typically data warehouses. This method is advantageous when dealing with structured data that requires pre-processing before storage.

Conversely, in an ELT-based architecture, data is initially loaded into storage systems such as data lakes in its raw form. Transformation occurs post-loading, allowing for flexible and scalable processing. This approach is beneficial for handling large volumes of diverse data types and enables on-demand transformation to meet various business use cases.

Both ETL and ELT architectures serve distinct needs, and the choice between them depends on the organization’s specific requirements for data storage, processing efficiency, and analytical flexibility.

Data storage

Data storage follows. This component is responsible for providing durable, scalable, and secure storage solutions for the data pipeline. It typically includes large data repositories designed to handle varying types of data efficiently.

• Data Warehouses: These are optimized for storing structured data, often organized in relational databases. They support complex querying and analytical processing, making them ideal for business intelligence and reporting. Data warehouses offer high performance and scalability, enabling organizations to manage large volumes of structured data efficiently.
• Data Lakes:  Data lakes are designed to store structured, semi-structured, and unstructured data, providing a flexible and scalable solution. They retain raw data in its native format, facilitating extensive data ingestion and integration from various sources. This approach supports large volumes of diverse data, enabling advanced analytics, machine learning, and data exploration by transforming and analyzing data as needed.

Both data warehouses and data lakes play crucial roles in a data pipeline, providing the necessary infrastructure to store and manage data efficiently. They ensure that data is preserved with durability, protected with robust security measures, and scaled to meet the growing demands of modern data processing and analytics. Because of this, many organizations leverage both. 

Data Consumption 

The consumption layer is essential for extracting and leveraging data from storage systems. It offers scalable and high-performance tools that enable efficient data access and utilization. This layer integrates a variety of analytics tools tailored to different user needs and analytical methods. 

It supports SQL-based queries for precise data retrieval, batch analytics for processing large datasets, and reporting dashboards for visualizing key metrics and trends. Additionally, it facilitates machine learning applications, allowing for advanced data analysis and predictive insights. By providing these diverse tools and capabilities, the consumption layer ensures that all users—from data scientists to business analysts—can derive actionable insights and drive informed decision-making across the organization.

Data Governance

The security and governance layer ensures the protection and management of data throughout the entire pipeline. It includes:

• Access Control: Restricts data access to authorized users through robust authentication and permissions management.
• Encryption: Secures data both at rest and in transit to prevent unauthorized access.
• Network Security: Utilizes firewalls, intrusion detection systems, and secure communication channels to safeguard data from cyber threats.
• Usage Monitoring: Tracks data access and usage patterns to detect anomalies and enforce security policies.
• Auditing Mechanisms: Maintains a detailed audit trail of all data operations and user activities for compliance and oversight.

This layer is integrated with all other pipeline components to ensure consistent application of security measures and governance practices across the data pipeline.

How to Build Data Pipelines in Eight Steps 

Designing data pipelines involves many considerations, and the decisions made early on can significantly impact future success. This section serves as a guide for asking the right questions during the initial design phase of a data pipeline.

In this guide, we’ll design a data pipeline for a hypothetical movie streaming service called “Strimmer.” Strimmer will offer a library of films and TV series accessible across Web, iOS, and Android platforms. Our goal is to create a data pipeline that supports a machine learning (ML) recommendation engine, enhancing movie recommendations for users.

Step 1: Determine the goal in building data pipelines 

Your first step when building data pipelines is to identify the outcome or value it will offer your company or product. At this point, you’d ask questions like: 

• What are our objectives for this data pipeline?
• How do we measure the success of the data pipeline?
• What use cases will the data pipeline serve (reporting, analytics, machine learning)?
• Who are the end-users of the data that this pipeline will produce? How will that data help them meet their goals?

Strimmer: For our Strimmer application, the data pipeline will provide data for the ML recommendation engine, which will help Strimmer determine the best movies and series to recommend to users.

Step 2: Choose the data sources

In the next step, consider the possible data sources to enter the data pipeline. Ask questions such as: 

• What are all the potential sources of data?
• In what format will the data come in (flat files, JSON, XML)?
• How will we connect to the data sources?

Strimmer: For our Strimmer data pipeline, sources would include: 

• User historical data, such as previously watched movies and search behaviors stored in operational databases like SQL, NoSQL
• User behavior data/analytics, such as when a user clicks a movie detail
• 3rd party data from social media applications and movie rating sites like IMDB

Step 3: Determine the data ingestion strategy

Now that you understand your pipeline goals and have defined data sources, it’s time to ask questions about how the pipeline will collect the data. Ask questions including: 

• Should we build our own data ingest pipelines in-house with python, airflow, and other scriptware?
• Would we be utilizing third-party integration tools to ingest the data?
• Are we going to be using intermediate data stores to store data as it flows to the destination?
• Are we collecting data from the origin in predefined batches or in real time?

Strimmer: For our Striimmer data pipeline, we’ll leverage Striim, a unified real-time data integration and streaming platform, to ingest both batch and real-time data from the various data sources.

Step 4: Design the data processing plan

Once data is ingested, it must be processed and transformed for it to be valuable to downstream systems. At this stage, you’ll ask questions like: 

• What data processing strategies are we utilizing on the data (ETL, ELT, cleaning, formatting)?
• Are we going to be enriching the data with specific attributes?
• Are we using all the data or just a subset?
• How do we remove redundant data?

Strimmer: To build the data pipeline for our Strimmer service, we’ll use Striim’s streaming ETL data processing capabilities, allowing us to clean and format the data before it’s stored in the data store. Striim provides an intuitive interface to write streaming SQL queries to correct deficiencies in data quality, remove redundant data, and build a consistent data schema to enable consumption by the analytics service.

Step 5: Set up storage for the output of the pipeline

Once the data gets processed, we must determine the final storage destination for our data to serve various business use cases. Ask questions including: 

• Are we going to be using big data stores like data warehouses or data lakes?
• Would the data be stored on cloud or on-premises?’
• Which of the data stores will serve our top use cases?
• In what format will the final data be stored?

Strimmer: Because we’ll be handling structured data sources in our Strimmer data pipeline, we could opt for a cloud-based data warehouse like Snowflake as our big data store.

Step 6: Plan the data workflow

Now, it’s time to design the sequencing of processes in the data pipeline. At this stage, we ask questions such as:

• What downstream jobs are dependent on the completion of an upstream job?
• Are there jobs that can run in parallel?
• How do we handle failed jobs?

Strimmer: In our Strimmer pipeline, we’ll utilize a third-party workflow scheduler like Apache Airflow to help schedule and simplify the complex workflows between the different processes in our data pipeline via Striim’s REST API. For instance, we can define a workflow that independently reads data from our sources, joins the data using a specific key, and writes the transformation output to our data warehouse.

Step 7: Implement a data monitoring and governance framework

You’ve almost built an entire data pipeline! Our second to final step includes establishing a data monitoring and governance framework, which helps us observe the data pipeline to ensure a healthy and efficient channel that’s reliable, secure, and performs as required. In this step, we determine:

• What needs to be monitored? Dropped records? Failed pipeline runs? Node outages?
• How do we ensure data is secure and no sensitive data is exposed?
• How do we secure the machines running the data pipelines?
• Is the data pipeline meeting the delivery SLOs?
• Who is in charge of data monitoring?

Strimmer: We need to ensure proper security and monitoring in our Strimmer data pipeline. We can do this by utilizing fine-grained permission-based access control from the cloud providers we use, encrypting data in the data warehouse using customer-managed encryption keys, storing detailed logs, and monitoring metrics for thresholds using tools like Datadog.

Step 8: Plan the data consumption layer

This final step determines the various services that’ll consume the processed data from our data pipeline. At the data consumption layer, we ask questions such as:

• What’s the best way to harness and utilize our data?
• Do we have all the data we need for our intended use case?
• How do our consumption tools connect to our data stores?

Strimmer: The consumption layer in our Strimmer data pipeline can consist of an analytics service like Databricks that feeds from data in the warehouse to build, train, and deploy ML models using TensorFlow. The algorithm from this service then powers the recommendation engine to improve movie and series recommendations for all users.

Where does Striim Come into Play When Building Data Pipelines? 

Striim radically simplifies and manages the development, deployment, and management of real-time data pipelines. Historically, creating data pipelines involved manually stitching components together with scripts, a process that was often cumbersome, difficult to maintain, and prone to errors. Modern frameworks have improved this with visual design tools, but Striim takes it further by simplifying and automating the entire process.

As Dmitriy Rudakov notes, “The Striim platform contains all tools necessary for running a data pipeline: a multitude of sources and targets, schema evolution, a transformation layer called continuous query, and integration with UDFs. These capabilities are integrated into what’s called a Flow Designer, which provides a simple drag-and-drop interface and a monitoring framework that ensures smooth execution.” This comprehensive suite of features makes it easier to design, execute, and manage data pipelines with minimal complexity.

Striim also offers low-code and REST APIs so that data teams can automate the entire deployment, monitoring, and security of the data pipelines with CI/CD processes. 

Unlike traditional batch processing systems that rely on scheduled updates and often require additional logic to handle data changes, Striim offers a more streamlined approach. As Benjamin Kennady highlights, “Striim reads inserts, updates, and deletes as they occur and replicates them into the target. This methodology means that the source dataset does not require a field for capturing the updated time or when it was deleted. By not capturing when the last value was deleted, this saves on storage and processing requirements. This is also a more straightforward and lightweight way to work with a data pipeline.”

Striim’s real-time data integration ensures that changes are captured and processed instantly, eliminating the need for complex update schedules and reducing the overall workload. By connecting directly to the source database and table, Striim initiates the replication process with ease, thereby accelerating data pipelines and simplifying workflow management. “It simplifies development, deployment and management of real time data pipelines,” shares Dmitriy Rudakov. “In the past programmers used to stitch everything together with scripts which were hard to maintain and understand while modern frameworks tend to provide visual pipeline design studio that allow to automate running and monitoring of the user applications.” 

Flexibility and Scalability are the Keys to Sustainable Data Pipelines

Data pipelines enable companies to make faster, more informed decisions, gain a competitive edge, and derive substantial value from their growing data assets. Designing a scalable and adaptable data pipeline is crucial for managing increasing data volumes and evolving use cases.

With over 150 automated connectors, Striim integrates data from various sources—applications and databases—streaming trillions of events daily for diverse applications.

Schedule a demo today to discover how Striim can transform your data management strategy.