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Why Now Is the Time to Automate Data Pipelines

Easy access to data afforded by automating data pipelines gives lines of business and data analysts quick access to trusted data.

For decades, business operations involving data were relatively straightforward. Organizations extracted structured and relational data maintained in enterprise systems and performed their data analytics, reporting, and querying on that data. Data was limited to a handful of sources. And the data was typically clean and easily understood.

Modern organizations are dealing with vastly different conditions. Many new types of data are being used across industries. That includes data from smart sensors, IoT devices, customers’ mobile phones, and more. And thanks to APIs and other technology, many organizations frequently incorporate third-party data into their analytics, querying, and reporting processes. Such data often needs more massaging to be put into a useful and trustworthy form.

Unfortunately, as the demand grows for access to the growing volume and types of data from business users and lines of business, IT departments and data engineers cannot keep up. Most organizations find they simply do not have enough time or resources to meet demand.

Implementing automated data pipelines is one way to make data a more natural part of analytics and reporting workflows.

Why the status quo will not do

Today, IT departments and data engineers must satisfy multiple masters when providing data throughout the organization. They must provide fast access to high-quality data that lines of business and data analysts need to do their jobs. And they must meet the requirements of those responsible for the data’s governance and safety.

Traditionally, they would manually build data pipelines on a one-off basis as new projects emerged. That was fine when only a handful of efforts needed data. But today, essentially every business unit and every manager needs data. Old methods are unsustainable.

Some compare the situation to that of the early days of the telephone. When a limited number of calls were being placed, operators manually connected the caller to the party they were trying to reach. As the volume of calls grew, the operation of connecting a call to a party had to be automated.

Similarly, traditional approaches to manually making data accessible and building data pipelines fail due to the rise in demand. Manual approaches simply cannot meet the need for speed. Data analysts and business units cannot wait weeks for a team to build a pipeline.

Organizations want to use their skilled staff on projects that deliver high value to the operation. As noted, analytics, BI, AI, and more are expanding into many more domains. That drives up the need for data access. Again, a manual approach cannot keep pace.

Once a pipeline is built, there are additional issues. A manual approach cannot keep pace with these groups’ frequent and varied needs. Organizations do not just put a pipeline in place, and that’s it. Users constantly want access to new data sources, and they want that data made available to new systems and cloud services to apply new and different analyses.

See also: Challenges Data Pipeline Engineers Need To Overcome

How data pipeline automation helps

In general, businesses are embracing many types of automation to improve productivity, speed innovation, enhance the customer experience, and more. Some common efforts include using robotic process automation (RPA) to automate repetitive tasks and processes; employing Kubernetes to automate, scale, and manage containerized applications; or using React to simplify how web servers, apps, and browsers communicate.

In each case, the technology unlocked productivity and scalability that was not previously possible. And that, in turn, led to innovation.

Automation of data pipelines gives data-driven organizations comparable capabilities. Automation can be applied to common tasks like data access using connectors, data transformation (ETL and ELT), data joins and enrichment, and more.

One way to think about automating data pipelines is to look at the main functions that must be supported. Any solution or effort must focus on some basic elements like data ingestion, transformation, and orchestration.

Advanced solutions offer more. For example, a critical differentiator is a solution that provides observability. A solution should be able to provide insights into current conditions, perform error detection and notification, and make logs easily digestible via visualization. 

These are all areas where can help. Its Data Pipeline Automation Platform lets businesses easily build intelligent data pipelines. The platform checks data end-to-end from a single console, detects changes, and propagates changes.

There are many benefits to using such a platform. Automation ensures that data movement and transformation are carried out efficiently and consistently, and these processes easily scale.

Such automation ensures that the data processing steps are standardized and repeatable. Automated pipelines can incorporate error handling, retries, and logging. Once an issue is detected, an automated data pipeline can resolve the issue without human intervention.

A final word

Businesses are turning to different types of automation to increase efficiency, improve operations, and more. Like other automation technologies like RPA, React, and Kubernetes, the Data Pipelines Automation Platform eliminates manual tasks, freeing time for skilled IT staff and data engineers to work on more business-critical projects.

The easy access to data afforded by automating data pipelines gives the data consumers, the lines of business and data analysts, quick access to trusted data. That ease of access on their timeframes allows these groups to try different things, experiment, and ultimately accelerate innovation.

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