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 different platforms or technologies. This enables extracting the data from multiple sources, preparing and transforming the data and making it ready to be used in production, so it can be consumed by services like business applications and analytics solutions. Automating the data pipeline is more efficient and cost-effective than manually moving the data between systems. The automated process also improves data quality and helps managing data at scale.

This article reviews what a data pipeline is, details the benefits of data pipeline automation, explains how to automate a data pipeline and suggests tools and platforms you can use.

What is a Data Pipeline?

A data pipeline is a series of steps or stages that data goes through in order to be processed, transformed and stored in a usable format. Data pipelines usually comprise multiple stages, including:

  • Data Ingestion: Collecting the data from databases, APIs, microservices, applications and other sources, and adding it to the pipeline.
  • Data Processing: Cleaning, validating, transforming and enriching the data to make it usable and useful.
  • Data Storage: Putting the data in a database, data warehouse, or other solution so it is accessible for future use.
  • Data Analysis: Analyzing the data to generate insights for informing business decisions. Methods like ML and predictive analytics are used.
  • Data Visualization: Presenting the data in an accessible manner on dashboards, reports, push notifications, etc.

As you can see, the data needs to be transferred between different systems and applications to progress in the pipeline.

What are the Benefits of Data Pipeline Automation?

Ensuring data moves in the pipeline is essential for the organization’s ability to consume the data. Today, data engineers are overburdened with having to clean and fix data, debug pipelines, upgrade pipelines, manage drift, ensure the pipeline’s technologies integrate well together, and other data-related tasks. As a result, they spend a lot of time on tedious tasks. In addition, data quality is sometimes adversely impacted.

Having the right tools and platforms to automatically set up and manage data pipelines will help data engineers by ensuring:

    • Efficiency and Productivity: Automation reduces the amount of work required to transfer and process data in the pipeline, update data columns, etc. Without having to manually complete these data-related tasks, the process can be faster and require less manual effort, which helps improve efficiency and reduce errors in data points.
    • Better Data Quality: Standardization derives from automation. By standardizing the way data is moved in the pipeline, regardless of its source or format, the risk of errors, oversights or drift is reduced. This makes the data consistent, more accurate, always up-to-date, and, as a result, of higher quality.
    • Faster and More Effective Insights: Better data quality also informs better and faster insights, which gives businesses a competitive advantage since they can extract more value from their data. This could include business insights or data engineering insights, like duplicate data or tracking how data changed.
    • Process Simplification: Automation of the data pipeline process helps simplify the tedious tasks related to the data pipeline process, like connecting multiple sources, cloudification, or cleaning up columns from irrelevant commas. This drives productivity, but also improves the data engineering work experience.
  • Cost Reduction: By improving data quality, reducing the need of manual labor and making data useful faster, businesses can cut costs associated with risks, mistakes and human errors.
  • Scalability: Automated data pipelines are easier to scale as they can be configured to scale horizontally or vertically based on workload demands, and resources can be optimized for efficiency. This allows for the pipeline to easily handle increased data volumes and processing requirements without requiring significant manual intervention or reconfiguration.

By equipping data engineers with the right resources, they can ensure data pipelines are optimized and perform as intended. They will also be able to focus on more strategic tasks, rather than wasting time on manual tasks. Such platforms will also boost data quality.

How to Automate the Data Pipeline

There are two types of data pipeline architectures that can be automated:

  • Batch Data Pipeline: Moving large amounts of data at a time. A batch data pipeline is used, for example, for ETL processing.
  • Streaming Data Pipeline: Moving data in real-time, when it is created. A streaming data pipeline is used, for example, in messaging systems, for real-time event processing or for populating data lakes.

Automating of the data pipeline process can take place according to various types of triggers, including:

  • Based on a predetermined schedule (e.g every Sunday or every quarter)
  • Following certain events (e.g when a change is made in the data or when there is new version available for a certain source)
  • When manually triggered

Setting up the automation itself can be based on:

  • A code-based trigger
  • Through low-code tools
  • With no-code tools

Which type of solution is right for you? A code-based trigger is more complex to use and requires a technological skill set, but it enables customization. Low-code solutions can be used by many types of users, since they require a minimal amount of tech know-how. They enable customization, to a certain extent. No-code tools can be used by most types of users since there is nearly no tech knowledge required. However, they usually do not enable customization.

Tools, Technologies and Solutions for Data Pipeline Automation

There are many solutions available for data professionals looking to automate their data pipelines. The main ones are:

  • Fivetran – A cloud-based automated ETL (Extract, Transfer, Load) tool that assists in moving data from different sources to data storages, like data warehouses or databases. 
  • Talend – An ETL tool that provides solutions for data integration, data quality, data preparation, big data and application integration. Talens is available in both open-source and premium versions.
  • Alteryx – A solution for the automation of data engineering, analytics, reporting, machine learning and data science processes.
  • Panoply – A data management platform for syncing, storing and analyzing data from multiple sources. 
  • Integrate.io (formerly Xplenty) – A tool for extracting data out of various cloud apps and moving data between various data stores. 
  • Airflow – An open-source workflow management platform for data engineering pipelines.
  • Dagster – An open-source, cloud-native orchestrator for the whole development lifecycle, with integrated lineage and observability and a declarative programming model.
  • Prefect – An orchestration tool for coordinating all data tools. Available in both open-source and premium versions.
  • Iguazio – Iguazio’s feature store graphs can take sources from data lakes and store them into a file destination while also enriching the data and automating the feature engineering.