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Idan Novogroder
Idan Novogroder Author

Idan has an extensive background in software and DevOps engineering....

Last updated on April 26, 2024

Data is the lifeblood of any business. It drives decision-making, powers strategies, and boosts customer relationships. However, due to the enormous volume of data collected or its poor quality, most businesses still struggle to unlock its value.

With the right data pipeline automation system in place, teams can clean and prepare data to improve your product and service, allowing your business to expand faster.

This article dives into the essentials of data pipeline automation and the solutions that help you get there.

What is Data Pipeline Automation?

Data pipeline automation is the process of automating the flow of data from one system or application to another, often across several platforms or technologies. This enables the extraction of data from various sources, as well as its preparation and transformation before services like business applications and analytics solutions use it in production.

Automating the data pipeline saves time and money over manually transferring data between systems. An automated procedure also enhances data quality and facilitates data management at scale.

But what is a data pipeline in the first place?

A data pipeline is a set of procedures or stages via which data is processed, converted, and stored in a usable manner. Data pipelines typically include phases such as:

Phase Description
Data Ingestion Gathering data from databases, APIs, microservices, apps, and other sources and incorporating it into the pipeline.
Data Processing The process of cleaning, verifying, converting, and enriching data so that it is usable and helpful.
Data Storage Putting data into a database, data warehouse, or other solution so that it may be accessed later.
Data Analysis Analyzing data to develop insights that will help businesses make choices, applying methods such as machine learning and predictive analytics.
Data Visualization Making data accessible through dashboards, reports, push alerts, and so on.

As you can see, data moves across various systems and apps as it advances along the pipeline.

Automated Data Pipeline Components

Automated data pipeline designs are made up of multiple components, each of which serves a particular purpose:

  • Data Source – The data source is where your data comes from. It can include everything from a data warehouse to real-time data streams.
  • Data Processing – Data processing is the critical stage in which data is cleansed, processed, and enhanced. This stage ensures that the data is relevant and in the format required for analysis or other uses. This is also where schema development is frequently handled automatically, allowing the pipeline to adapt to changes in the structure of the incoming data without requiring direct intervention.
  • Data Destination – The data destination is the endpoint to which your processed data is loaded. Depending on your use case, it might be stored in databases, data warehouses, or data lakes.
  • Workflow Management Tools – The workflow management tools serve as the data pipeline’s control system. They control how data goes through the pipeline and is processed. These tools manage scheduling and error management, ensuring the pipeline runs smoothly.
  • Monitoring and Logging Services – Monitoring and logging services track the health and performance of the data pipeline. It records data for auditing, troubleshooting, and performance optimization.
  • Data Version Control System – Data versioning is a critical component of every data pipeline. Using automated checks ensures high data quality and helps teams identify issues before data moves further along the pipeline.

Classification of Automated Data Pipelines

Batch vs Real-Time Data Pipelines

Batch processing pipelines handle data in huge batches, gathering it over time and processing it all at once. This pipeline is often used to analyze historical data and provide periodical reports.

Real-time or streaming pipelines, on the other hand, process data as it arrives, either in real-time or near real-time. This kind is appropriate for applications requiring quick information, such as monitoring systems or financial markets.

On-Premises vs Cloud-Native Data Pipelines

On-premises pipelines have typically been used in businesses where data is stored and processed on local equipment. These pipelines are configured and maintained on local servers. They provide a high level of control and protection over the data because it is all stored within the organization’s physical location.

Cloud-native pipelines, on the other hand, are created and managed in the cloud. They take advantage of cloud computing to deliver scalability and cost-efficiency. These pipelines are suitable for teams that wish to swiftly scale up or down their data operations in response to demand. They also reduce the requirement for an initial hardware investment and continuous maintenance expenditures.

ETL (Extract, Transform, Load) vs ELT (Extract, Load, Transform)

Finally, there are two types of pipelines: ETL (Extract, Transform, Load) and ELT. An ETL pipeline extracts data from sources, converts it to a standard format, and then loads it into a destination system.

ELT pipelines, on the other hand, start by putting raw data into the destination and then processing it there. The decision between ETL and ELT is based on data quantities, processing requirements, and individual use cases.

Benefits of Data Pipeline Automation

Higher Data Quality

Data engineers are overwhelmed with cleaning and fixing data, debugging pipelines, upgrading pipelines, managing drift, ensuring the pipeline’s technologies work well together, and other data-related responsibilities. As a result, they devote a lot of time to tiresome jobs, and data quality might suffer as a result.

Standardization is the core result of automation. The danger of mistakes, oversights, and drift is decreased by standardizing how data is transferred through the pipeline, regardless of source or format. This makes the data more consistent, accurate, and up-to-date, resulting in improved quality.

Faster and More Effective Insights

Better data quality leads to better and quicker insights, giving organizations a competitive edge by allowing them to extract more value from their data. This might contain business or data engineering insights, such as identifying duplicate data or tracking how data changes.

Efficiency and Productivity

Automation improves efficiency and productivity by reducing the amount of effort necessary to move and process data in the pipeline, as well as update data columns. Without having to manually execute these data-related activities, the process can be speedier and involve less manual work, resulting in increased efficiency and fewer data point mistakes.

Process Simplification

Automation of the data pipeline process simplifies tiresome operations such as connecting various sources or removing extra commas from columns. This increases efficiency while also improving the data engineering experience.

Streamlined Data Integration

By providing data engineers with the necessary resources, companies can ensure that data pipelines are optimized and function as expected. Teams will also be able to concentrate on more strategic activities instead of wasting time on manual ones.


Automated data pipelines are simpler to scale because they can be built to scale horizontally or vertically in response to workload needs, and resources may be optimized for efficiency. This enables the pipeline to readily accommodate rising data quantities and processing demands without needing considerable manual intervention or reconfiguration.

Cost Reduction

Businesses can save money by enhancing data quality, eliminating the need for manual labor, and making data more usable sooner.


The term “reproducibility” refers to the ability to replicate any specific pipeline run. If the pipeline is automated, the same individual executing the same code on the same system could replay it and be sure to get the same results.

Data Pipeline Automation Use Cases

Automating your data pipelines leads to benefits across the following use cases:

  • Enhanced BI and Analytics – Business intelligence tools and no-code platforms for non-technical business users, as well as democratized data access throughout the organization, enable enterprises to be data-driven. Empowered business users can schedule and manage data pipelines, linking and integrating them with cloud-based databases and business applications to get the insights they need to achieve their objectives.
  • Managing Dynamic Data Changes and Tracking – the ability to manage data versions that occur due to backfills.
  • Instant Data Set Comparison – the need to compare results from different runs of the pipeline. For example, after fixing a bug, you may wish to compare the new pipeline outcome to the old one to make sure you got the right thing.
  • Standardized Data Cleanup – Data cleaning is the process of detecting and repairing flaws, inconsistencies, and missing information in a dataset. It includes deleting duplicates, fixing misspellings, filling in missing numbers, and ensuring all data is properly structured. Manual data cleansing accounts for even 90% of the data science life cycle. Automation can help to minimize workload and save time.

How to Create an Automated Data Pipeline

Before you begin automating your data pipeline, make sure you plan thoroughly and select the right tools and technology. With this in mind, let’s dive into the step-by-step instructions.

Step 1: Planning and Designing the Pipeline

Outline the goals and needs for the data pipeline. Identify the data sources, the volume of data to be processed, the final format required, and the frequency with which data must be processed.

At this point, you also need to determine the type of data pipeline you require. Consider if batch or real-time processing is more appropriate, whether the data infrastructure should be on-premises or in the cloud, and whether an ETL or ELT strategy is better. This step is critical for ensuring that the pipeline operates effectively.

Step 2: Selecting the Right Tools and Technologies

Next, choose the tools and technologies. You can select between low-code/no-code and code-based solutions. Low-code/no-code solutions are easier to use but may lack flexibility. Code-based solutions provide more control but also call for programming skills. Consider the positives and downsides based on your team’s strengths and needs – as well as your use case, resources, budget, required integrations, and data volume/frequency.

Step 3: Setting up the Data Sources and Destinations

It’s time to link your data sources to the pipeline. This might be databases, cloud storage, or external APIs. Connectors included with data pipeline technologies and platforms make it simple to connect to numerous data sources and destinations. Also, you need to specify where the processed data will be saved or transmitted. Ensure that all connections are secure and in accordance with data protection standards.

Step 4: Implementing Data Transformations

At this stage, organize your data transformation operations. This includes cleansing for mistakes, and sorting in a logical and relevant order to finally transform data to the necessary formats. Create error-handling methods to resolve any difficulties that may emerge during transformation. Errors may include missing data, mismatched formats, or unexpected outliers.

Step 5: Automate the Data Flow

Next, automate the data flow across the pipeline. This minimizes the possibility of human mistakes and assures uniformity throughout the processing phases. Automation also allows you to manage larger amounts of data more efficiently since it can carry out repeated operations faster and more correctly than manual processing.

Step 6: Testing and Validation

Before deploying, carry out data pipeline testing with sample data. Run the pipeline with realistic sample data to replicate real-world settings and detect any possible difficulties, mistakes, or faults that may impact data processing.

During the testing phase, pay attention to the pipeline’s performance and output. Take note of any disparities, unexpected findings, or departures from the intended outcomes. If you discover any problems, document them and analyze the underlying reasons to identify the best solutions.

Step 7: Documentation and Knowledge Sharing

When writing the documentation, offer clear and simple descriptions of how the data pipeline works. Explain the many components, their responsibilities, and how they interact with one another. Include step-by-step instructions for setting up, configuring, and maintaining the pipeline.

Step 8: Continuous Monitoring and Optimization

Regularly check the performance of your data pipeline to uncover any mistakes, inefficiencies, or bottlenecks. Maintain a tight check on the pipeline’s operation and output. Check for any unexpected mistakes, data discrepancies, or processing delays. This helps you maintain the pipeline’s dependability and accuracy.

Data Pipeline Architectures

Batch Data Pipeline

Batch data pipelines are run either manually or repeatedly. In each run, they retrieve all data from the data source, perform operations on it, and then publish the processed data to the data sink. They’re completed when all the data has been processed.

Batch data pipelines are typically used for complicated data processing tasks, such as merging hundreds of distinct data sources (or tables) that are not time-sensitive. Examples include payroll, billing, and low-frequency reporting based on previous data.

Streaming Data Pipeline

While batch data pipelines worked well for analytical applications, there has been a growing need for near-real-time data. Streaming data pipeline architectures are often performed in parallel with contemporary data stack pipelines and are mostly used for data science or machine learning applications. Apache Kafka is the most popular tool here.

Data pipeline designs are continually being updated. Two developing data pipeline designs are zero ETL and data sharing.

Change Data Capture Pipeline

Change Data Capture (CDC) is a helpful software method that finds and selectively monitors changes to essential data in databases. CDC provides real-time or near-real-time data mobility when new updates are made to the database.

In many businesses today, data is arriving at an ever-increasing rate. Success requires rapid and precise decision-making. This is where Change Data Capture helps, as it has emerged as an efficient method for providing low-latency and dependable real-time data replication. Furthermore, it is highly useful for cloud migrations, allowing businesses to relocate their data with no downtime.

The four most frequent methods for implementing Change Data Capture are audit column, table deltas, trigger-based CDC, and log-based CDC.

Data Pipelines Challenges

Building a well-architected and high-performing data pipeline calls for early planning and design of multiple aspects of data storage, such as data structure, schema design, schema change handling, storage optimization, and rapid scaling to meet unexpected increases in application data volume, among other things.

This often requires using an ETL method designed to organize the transformation of data over numerous processes. You must also guarantee that ingested data is checked for data quality and loss and monitored for job failures and exceptions not handled by the ETL job architecture.

Some of the most common challenges data engineers face in building and running data pipelines are:

  • Increased data volume for processing
  • Changes to the structure of source data
  • Poor data quality
  • Poor data integrity in the source data
  • Duplicate data
  • Lack of a developer interface for testing

The most common data pipeline automation tools are:

  • Prefect – An orchestration tool that coordinates all data tools. Available in both open-source and paid editions.
  • Airflow – An open-source workflow management software for data engineering pipelines.
  • Dagster – An open-source, cloud-native orchestrator that supports the whole development lifecycle, including integrated lineage and observability, as well as a declarative programming paradigm.
  • Fivetran – A cloud-based automated ETL (Extract, Transfer, Load) technology that helps move data from various sources to data storage locations such as data warehouses or databases.
  • Talend – An ETL tool that offers solutions for data integration, quality, data preparation, big data, and application integration. Talens comes in both open-source and commercial editions.
  • Alteryx – It automates data engineering, analytics, reporting, machine learning, and data science operations.
  • Panoply – A data management tool for synchronizing, storing, and analyzing data from many sources.

Data Pipeline Automation with lakeFS

Continuous integration (CI) of data is all about delivering data to customers only after it has been verified to follow best practices such as format, schema, and PII governance. Continuous deployment (CD) of data ensures data quality at every stage of the production pipeline. Both are key in improving data quality and rely on automation mechanisms.

The open-source data version control project lakeFS allows teams to build CI/CD for data lakes. lakeFS provides a Git-like data versioning management mechanism, making creating CI/CD pipelines easier.

lakeFS comes with a feature called hooks that allows you to automate data checks and validation on lakeFS branches. Certain data actions, such as committing or merging, might trigger these checks.

lakeFS hooks function similarly to Git hooks. They operate remotely on a server and are guaranteed to execute when the right event occurs – for example:

  • Prior to merging or committing
  • Following a merging or commit
  • Pre-create a branch, then post-create it, etc.

You may use the pre-commit and pre-merge hooks in lakeFS to create CI/CD pipelines for your data lakes.

The actions.yaml file defines specific trigger rules, quality checks, and the branch to which the rules apply. When a given event (say, pre-merge) happens, lakeFS executes all validations specified in the actions.yaml file. If validation issues are detected, the merging event is halted.

This allows you to codify and automate the rules and procedures that all Data Lake participants must follow.

Check out this article to learn more about CI/CD for data.


Automating data pipelines is smart, but how do you pick the right solution? A code-based trigger is more difficult to utilize and requires technical expertise, but it allows for customization. Low-code solutions are accessible to a wide range of consumers since they require a minimum of technical knowledge. However, they typically don’t allow for customization.

Remember that picking the right tools for your data pipeline automation project is just one of the steps teams take to build a solid foundation for a data-driven organization.

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