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Haziqa Sajid
Guest Writer (Data Scientist)
27 Apr, 2023 · 5 min read

Top reasons why you need to automate your data pipeline in 2023


Automating data pipeline workflows has emerged as a critical method for organizations to manage and process large amounts of data. Businesses can take this step to save developers time, increase reliability, reduce errors, and gain valuable real-time insights.

This article will explore the benefits for an organization of having an automated data pipeline and discuss how it can be leveraged to boost productivity and data-driven decision-making.

What is an automated data pipeline?

An automated data pipeline is a data pipeline that uses pre-scheduled triggers to automatically manage data flow in a streamlined and repeatable way. Triggers are signals that initiate a data pipeline process without manual intervention. These can range from simple data movements (from the source to the warehouse, for example) to complex data aggregations and transformations from multiple sources.

An automated data pipeline can entail the scheduled flow of the following processes:

  • Data ingestion

  • Data processing and transformation

  • Data storage update

  • Data visualization and reporting

Data pipeline automation (also known as data orchestration) is critical to building highly scalable and flexible data systems that can ensure an accurate and consistent data flow in the face of fluctuating business requirements.

4 reasons why you need to automate your data pipeline

To access and analyze data faster

Pain point: Manually scripting and preparing data pipelines is a tedious task. It can take months before data is accessible for deriving insights.

Solution: Automated data pipelines remove the need for a manual workload, which allows you to set up pre-scheduled data systems in a no-code interface. You’ll be able to identify the trends and insights that inform decision-making quickly because the time it takes to access a complete and up-to-date view of the data is reduced from months to just hours.

To utilize “dark” data in real time

Pain point: Many companies still struggle to effectively utilize their data to gain valuable insights and real-time visibility. It can be especially challenging when old databases with outdated systems and architectures are involved as these require expensive engineering talent to transfer and prepare data for integration and analysis. The valuable data flowing into these legacy systems becomes dark data.

Solution: When set up on a schedule, setting up a connection to dark databases just once enables a raft of new, real-time business intelligence analyses that were previously unavailable. The initial setup would be possible via a no-code product or a data consultancy service, and subsequent orchestration would be via a platform such as Airflow or Y42.

To derive maximum value from data

Pain point: Rising data volume, manual coding, and cumbersome rewrites with the addition of every new data source are significant hurdles to overcome when building pipelines that leverage the full potential of organizational data.

Solution: Data pipeline automation can enable you to sidestep these hurdles and build high-quality data pipelines that can withstand the increasing number of data sources without any latency issues. As a result, your organization can build pipelines with an enhanced data processing throughput, enabling you to make the most of your organizational data.

To increase data engineers’ productivity

Pain point: Data engineers can find themselves stuck in a continuous loop of maintaining API calls and manually tracking data changes to maintain reliable integrations and data quality. This shifts their focus away from work that adds value to the organization.

Solution: An automated data pipeline streamlines the processes of data extraction, loading data into a warehouse, and data transformation, reducing the risk of errors. This helps data engineers maintain data quality and accuracy in accordance with constant data changes and means that they won’t have to continuously maintain the pipeline. Instead, they can focus on scripting appropriate queries and providing valuable insights to data analysts.

Benefits of having an automated data pipeline

The quality of your data operations significantly impacts your pipeline performance. Here’s how data pipeline automation can help you achieve efficient data outcomes:

  • Pipeline agility and scalability: Automating a data pipeline ensures consistent data processing with optimized resources. As data volumes increase, these pipelines quickly scale up to accommodate the load. They can also be updated and modified quickly, allowing for greater flexibility and adaptability to changing data requirements.

  • Advanced data analytics: Automated data pipelines can be used to ingest data in real time to ensure that it’s always fresh for prompt analytics. They also enable seamless data integration from multiple sources. By eliminating manual coding, data can flow easily between applications, leading to improved data insights and enhanced business intelligence.

  • Reduced human errors: Human errors can occur at many points in the data processing chain, including data entry, transformation, and analysis. Automating these steps ensures consistent processing of high-quality data, reducing the risk of costly errors and inaccuracies.

  • Reduced cost: By automating routine tasks in the data pipeline workflows, organizations can reduce the time and effort required to process data, enabling employees to focus on more value-adding tasks.

Common mistakes to avoid when automating your pipeline

Forrester finds that a lack of an overall automation strategy and gaps in organizational readiness are the key reasons why automation projects fail. Therefore, when you are considering orchestrating your pipeline operations, you must stay mindful of the pitfalls that can hinder the success of your pipeline automation. Some of these pitfalls include:

  • Failing to define clear objectives of pipeline automation and processes to measure its success

  • Failing to understand the data you are working with, where it’s coming from, and what you require from it

  • Failing to involve the right stakeholders at the beginning of the automation process — you must keep data and business teams in the loop to develop pipelines that cater to everyone’s data requirements

  • Not being prepared for the complexities of migrating from a manual data pipeline to an automated one — for example, the challenge of addressing the unique processing needs of data coming from different sources in different formats or of training your teams to adjust to an automated system

  • Defining a complex pipeline structure with an overloaded stack of automation technologies

  • Relying entirely on automation and failing to regularly monitor pipeline performance for speed, accuracy, and data quality assessment

Case study: Growing from seed stage to Series B by automating data operations

Red Sift, a Y42 customer, is a cybersecurity software company that works to make it more challenging for impersonators to compromise secure domains. Their goal is to make the Internet safer for all.

As a growing cybersecurity startup, Red Sift faced the challenge of scaling its data operations without having a dedicated data team, which was resulting in a high volume of data requests and pressure to do more with less. To keep up with their data needs, Red Sift turned to Y42 to automate their data infrastructure from scratch.

With Y42’s orchestration solution, Red Sift generated complex pipelines to manage communications between its complex web of tools. Y42 enabled Red Sift to cater to their increasing data needs and led them on their journey from a seed startup to a Series B venture.

Automate your data pipeline with Y42

Automation is the key to scaling your data operations and enabling you to work with larger and more complex datasets. By reducing manual intervention and enabling real-time processing, data pipeline automation can help your organization make faster and better-informed decisions while freeing up resources to focus on higher-value tasks.

If the idea of data pipeline automation fits with your data requirements, then you need a data orchestration solution like the one offered by Y42. With Y42, you can automatically connect and schedule all necessary operations for your data pipeline in just a few clicks and get the correct data to the right place at the right time. All you need to do is integrate your data, model it, and set up data exports. Y42’s data orchestration engine will handle the rest.

If you want to learn more about how orchestrating your workflows can improve the way your organization works with data, book a call with our data experts today.

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About the author:

Haziqa is a data scientist with extensive experience in writing technical content.

About Y42

Y42 is an Integrated Data Development Environment (IDDE) purpose-built for analytics engineers. It helps companies easily design production-ready data pipelines (integrate, model, orchestrate) on top of their Google BigQuery or Snowflake cloud data warehouse. Next to interactive, end-to-end lineage and embedded, dynamic documentation, DataOps best practices such as Virtual Data Builds are baked in to ensure true pipeline scalability.

It's the perfect choice for experienced data professionals that want to reduce their tooling overhead, collaborate with junior data staff, or (re)think their data stack from scratch.

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