Data Pipeline Automation: Harness the Power of Your Most Critical Business Asset

Businesses today generate unspeakably vast amounts of data sourced from a variety of sources like websites, physical sensors, call centers, mobile apps, sales data, social channels, geolocation tools and more. That data contains worlds of useful information, full of potential value for both immediate decision making and long-term strategic planning. In order to extract its full value, that data must be directed into a data pipeline, a collection of technologies that work in conjunction to aggregate data from multiple sources for scrubbing, storage, translation, presentation and consumption by business decisionmakers.

But a data pipeline can’t be managed manually as the data comes in too quickly, the volume is too high, and the process is too dynamic. Given the speed and demands of business, today’s data pipeline must rely on automating technologies to orchestrate connections between the various data sources to make data meaningful and available anytime to the stakeholders who need it.

This makes pipeline automation essential for any business that aspires to be data-driven. Data pipeline automation allows the business to extract data at its source, transform it, integrate it with other sources and continually fuel business applications and data analytics tools.

Types of Data Pipelines

Depending upon the nature of the business, it may use various pipeline structures, such as those listed below:

• Real-time or streaming. Such pipelines are optimized to process data as it is generated. Real-time processing is necessary when you’re relying on data from streaming sources, such as IoT (internet of things), medical telemetry or financial market data.
• Batch. This type of data can be stored for when it’s needed. These types of pipelines are used when you need to move sizable chunks of data at regular intervals and don’t necessarily need insight in real time. For instance, marketing or sales data might be transferred weekly, monthly or quarterly into a data warehouse connected to the pipeline for analysis at some later point.
• Cloud-native. These pipelines are designed to work with data that’s generated in and remains in the cloud through its life cycle. These can work particularly well for highly complex analysis scenarios or system migrations. These days, most organizations are running at least some of their workloads in the cloud, and cloud storage often provides the backbone for data analytics pipelines.
• Change Data Capture (CDC). These pipelines are automated to identify and transfer data that has been added or changed since the last update. Like a manifest, it compares and isolates only what’s changed, moving the data through workflows as predefined events occur.

Regardless of the type of pipeline, automation is key to helping you get the most out of the data it contains. Central to your automation strategy, ideally, is some sort of orchestration platform that enables the systems within the pipeline to communicate with one another via an API or some other proprietary connector. This provides a single point of control and can make your automation efforts much more effective.

What’s Behind the Pipeline Automation Imperative?

Business users, whose reporting needs are greater than ever, demand speed, scale and repeatability from data they use for both historical and predictive analysis. Data pipeline automation reduces manual tasks. It enables IT Teams — which often lack skilled data workers — to extend their limited resources and quickly help the business ingest, combine, normalize, analyze and present data continually with much less effort. This automation creates a big impact on the business with only limited resources. 

The Many Benefits of an Automated Data Pipeline

While there is some effort and investment involved in putting an automation strategy in place, the benefits can be enormous. Below is a list of ways an automated data pipeline can help your business:  

• Allows you to bridge gaps between disparate systems and more easily extract, integrate, normalize and analyze data from countless sources
• Improves security by reducing the need for high-touch custom code and manual intervention, and by making it easier to enforce integration standards and encryption protocols
• Enables repeatable, scalable processes that are feasible to sustain over time
• Enhances visibility into data and its lineage, making it easier to trace its origin, movements and potential errors that may occur in workflows
• Makes it faster and easier to introduce new data sources and to build, test and deploy new workflows
• Provides insulation from costly and time-consuming human errors that may be introduced
• Makes it easier for IT teams to identify and fix root-cause problems with the data or with surrounding processes and tools
• Allows you to illuminate and use dark data, while also managing the risk often associated with it, such as dark data that contains sensitive information
• Frees up staff, allowing data analysts and other roles to focus on higher-yielding tasks rather than on tedious, manual data management
• Reduces the need for manual integrations by automating point-to-point integrations
• Improves accuracy of management reporting and enables real-time decision making by getting data into the hands of business leaders swiftly and consistently

Your Key to Creating Business Advantage

Data pipeline automation is no longer an exclusive capability reserved only for a handful of companies with teams of brilliant data scientists. The rapid proliferation of enabling technologies, such as pipeline orchestration platforms and intuitive BI tools, make it an exciting time to adopt pipeline automation and to hone your team’s skills in these technologies.

Armed with the right strategy, technologies and training, any business can unlock the value of its most valuable asset: the data it creates day in and day out.

There are countless tools and learning opportunities IT and business professionals can take advantage of the benefits of data pipeline automation. ExitCertified is partnered with all of the major cloud providers, so if you’re looking for AWS Data Analytics, Microsoft Azure Data and AI, or Google Cloud Data Analysis training, we have you covered. ExitCertified also has Business Intelligence training from various tool providers as well as data automation training from vendors like Databricks, Snowflake, IBM and SAP.