A Complete Guide to Automated Data Pipelines

Shawn Fergus -

In today's fast-paced digital landscape, modern data is the new oil. But just like crude oil needs to be refined to produce valuable products, raw data needs to be processed and moved to the right places to generate insights.

This is where data pipeline automation comes into play.

Designed to streamline the flow of real-time data between systems, automated data pipelines are a game-changer for IT operations and DevOps teams.

With the help of tools like Shipyard, these pipelines not only make data management more efficient but also offer a plethora of data-driven benefits that we'll dive into. From the basic steps involved in setting up a pipeline to the advantages it brings to your organization, this comprehensive guide covers it all.

Data pipeline automation: Overview

In the context of IT operations and DevOps, data pipeline automation is the process of automatically managing and orchestrating the data flow between systems.

Automating this process ensures data can be collected, processed, and seamlessly transferred without manual intervention, for use cases that range from fueling data analytics, business intelligence, and decision-making, to cutting-edge data science and machine learning. The most common steps of this process include:

  1. Data collection: Data ingestion, from one or many sources, is the first step of any pipeline process. And data sources that pipelines ingest new data from can vary widely, including relational databases, APIs, or customer relationship management software (CRMs).
  2. Data processing: With data ingested, pipelines will transform and/or process the data to maintain high data quality. Typically, this involves cleaning the data, aggregating it, transforming it into different formats, and running computations.
  3. Data transfer: Post-processing, the pipeline moves processed data to its destination, which could be another application, data warehouse, data lake, or other form of data store.
  4. Orchestration: All steps in an automated data pipeline must be coordinated. The term "orchestration" refers to this coordination, ensuring tasks are executed in the correct order, features are handled, and dependencies between tasks are managed.
  5. Monitoring and logging: Keeping tabs on the performance of real-time data streams is essential for automated pipelines, which includes monitoring for errors and logging specific info needed for debugging and auditing purposes.
  6. Scheduling: Not all automated data pipelines run continuously. In fact, automated pipelines are highly customizable and can be set to run hourly, daily, or at timed intervals.

Benefits of automated data pipelines

Despite their relative simplicity, DevOps teams enjoy numerous benefits when implementing automated data pipelines correctly. Here are some common advantages:

Reduced manual effort

Automation reduces (or completely removes) the need for manual intervention in the data movement process. DevOps teams bogged down by less repetitive, manual work then have more time and attention for value-added tasks, like developing new features and improving infrastructure.

Flexibility

In addition to more time spent on impactful tasks and projects, best-in-class automated data pipeline tools afford more flexibility through their ability to integrate easily with a wide range of destinations, data sources, data analysis tools, dashboards, and more. This ease of use translates to far less time teams need to invest in adapting systems and workflows to meet changing data requirements.

Improved collaboration

Data pipeline tools often provide intuitive visual interfaces as well, making it easier for cross-functional teams to stay in sync. This means data engineers, software developers, and operations teams can all work together while designing, deploying, and monitoring data workflows.

Enhanced monitoring and logging

The ability to monitor workflows itself benefits from automated pipeline tools, which can offer built-in monitoring and logging capabilities. Teams utilizing these enhanced monitoring and logging capabilities can quickly identify and address issues, ensuring smooth pipeline operations.

Efficiency and speed

Teams using automated data pipelines can accelerate the overall data movement process, which, in turn, enables faster data transfers and processing. These speed boosts are especially beneficial for business operations that rely on continuous integration and continuous deployment (CI/CD) processes, where rapid feedback loops are essential.

Consistency

Speed isn't the only improvement to the data pipeline process. The use of automated pipelines also ensures data processing and transfer tasks get executed correctly and consistently every single time they take place. This boost to consistency becomes a crucial pillar, helping maintain data integrity.

Reliability

Another process benefit involves how automated pipelines can handle errors, retries, and failures in a way that minimizes disruption. This functionality mitigates data loss and corruption, ensuring data flows are reliable.

Scalability

The need to scale up an IT environment should be a welcome task, not a problematic worry. Automated data pipelines contribute to the former, as they can scale to handle increases in load (even without significant manual intervention). This plasticity allows DevOps teams to manage large datasets without overhauling their entire system.

Enhanced security

The tools that enable automated pipelines often incorporate security features as well, ensuring data stays protected during the pipeline process through the use of access controls and encryption.

Cost savings

If (somehow) these benefits weren't valuable enough on their own, together they add up to long-term cost savings by reducing manual effort, minimizing errors, and optimizing a department's data processes.

How to create an automated data pipeline

Once you've wrapped your head around all the benefits inherent in automated data pipelines, it's tempting to dive right in and put one to work.

But, said benefits are contingent on correctly implementing these powerful IT tools. While the particulars of specific IT environments will vary, here is a general process DevOps teams can follow using tools like Shipyard to set up their first automated data pipeline:

  1. Define your objective: Resist the natural tendency to focus on technical details first. Proper pipeline implementation begins with a solid understanding of what you need your pipeline to accomplish. Do you need an efficient, reliable way to move data from one location to another? Or are you trying to uncover dark data that's been flowing through your organization's legacy system? Whatever the goal may be, make sure you clearly define it—a clear goal will then define your next steps.
  2. Identify data sources and destinations: With your goal well-defined, you'll then be able to determine your data sources (where your data will be coming from) and destinations (where you'll need that data to go). Sources and destinations typically include cloud storage, data lakes, data warehouses, APIs, or other databases.
  3. Design your data flow: Defining your data sources and destinations enables you to map out the flow of data. In doing so, you can identify potential dependencies between different data processing tasks and the order in which they will need to be executed.
  4. Data extraction: Next, you will need to identify which processes will be required to extract data from the sources you identified in step two. Typically, this includes reading files and querying databases.
  5. Data transformation: Once extracted, pipelines will typically need to transform ingested data into formats that allow it to be used. Automated data pipelines will typically clean, aggregate, and enrich data as part of the transformation process.
  6. Data loading: Once transformed, your pipeline will need a process to load transformed data into its destination (or destinations). When executed in this order, this process is referred to as ETL (extract, transform, load).
  7. Choose a good conductor: Making sure every step to this point makes music, not noise, requires orchestration. Platforms like Shipyard excel at keeping the entire data pipeline in tune, as they help you further define the order of tasks, handling errors and retries while managing all the dependencies between them.
  8. Focus on scheduling: Again, based on your goals, you should know how often your pipeline will need to run. The beauty of automated data pipelines is that they can run with minimal intervention, 24/7, 365. But that doesn't mean they should.
  9. Set up monitoring and logging: The monitoring and logging mechanisms you choose are important, as they will track your pipeline's performance, catch any errors that occur, and provide you with continuous insights into your data flow. Without these features, it's almost impossible to keep your pipeline free of errors, let alone track them down to fix them quickly when they do occur.
  10. Prioritize optimization: Even if you've planned everything perfectly, your automated data pipeline will need to be optimized as the data volumes it handles grow or as requirements change. Typically, this will involve scaling resources, integrating new tools and services into the pipeline process, and tinkering with the pipeline's transformation logic.
  11. Establish security and compliance: Any data pipeline you implement will need to adhere to data security best practices. Proper compliance involves abiding by relevant data protection regulations, enforcing access controls, and encrypting sensitive data.
  12. Shore up testing and validation: In addition to mapping out security and compliance, plan to test your pipeline before deploying it into an actual production environment. At Shipyard, we joke that it’s the same as making sure your pipeline's sea-worthy before you drop it in the water. Validate your pipeline's data output, ensuring it matches your expected results and no integrity issues present themselves.
  13. Plan to set sail: While we're having some fun with Shipyard-friendly metaphors, think of the next step, deployment, as getting ready to set sail. Once tested and validated, your automated data pipeline is ready for your production environment.
  14. Remember to trim the sails: Finally, once your pipeline's in action, it takes consistent love and attention to keep it sailing true. Plan to conduct regular reviews and maintenance. This vigilance will keep your pipeline performing as intended while adapting to ever-changing data sources and business requirements.

Choosing the right tools for the (automated) job

As we've seen, automated data pipelines are an invaluable asset for any organization that aims to make the most out of its data. They offer a multitude of benefits, from reducing manual effort and enhancing security to providing cost savings and scalability.

However, the key to unlocking these benefits lies in the correct implementation of these pipelines. With platforms like Shipyard, you can easily navigate the complexities of data pipeline automation, ensuring that your data flows as smoothly as a well-oiled machine.

So, whether you're just starting your journey or looking to optimize your existing processes, remember that a well-implemented data pipeline is your ticket to sailing smoothly in the sea of big data. Plan, implement, monitor, and keep trimming those sails to ensure that your data pipeline remains sea-worthy in the ever-changing tides of the technology landscape.Schedule a quick and free demo to learn more.

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