Using Automation to Optimise Workflow in Big Data Projects

 

big data automation

 

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4 MINUTE READ

 

Companies are now beginning to focus in on finding ways to better leverage their data. However, the vast scale of today’s big data projects means they can easily become enormous time-sinks if handled inefficiently. Unsurprisingly, investments in tools that extract actionable information from big data sets are rising sharply.

 

The International Data Corporation estimated the big data and analytics market jumped from $112 billion in 2015 to $150 billion in 2017, and projected that number to grow to $203 billion in 2020.

 

The problem is, the investment isn’t paying off for everybody. Many organisations are now realising that simply investing in the right big data tools and applications does not lead to positive results on its own.

 

Companies have to be able to set up tools and applications in a way that makes them work together seamlessly, whilst also getting the right data to the right people in a timely manner. This requires the ability to analyse and process data at lightning-quick speeds.

 

This is where automation comes into play. It offers an increasingly viable way of avoiding many of the pitfalls commonly associated with big data projects.

 

Automation for software integration

 

Automation is defined as the linking of disparate systems and software  (including big data tools and applications) to make them self-acting. This capability has become even more important as companies deploy a growing number of big data tools and applications that create a high degree of structural complexity.

 

When big data tools are not integrated into the larger IT architecture, these tools and applications are rendered practically useless outside of their immediate silo. However, through automation, companies can start tearing down these silos, enabling them to leverage data through one coordinated system.

 

Traditionally, companies use scripting to connect applications and systems that were not designed to work together. This is far from ideal as it creates a constant need for the repetitive and error-prone process of manual scripting. Automation provides pre-built and tested logic so companies can focus on functionality, rather than writing code and testing workflows.

 

Establishing such automated systems has enabled many companies to use their data in a more impactful and tangible way.

 

 

Transforming data into insights

 

At the end of the day, data is only as valuable as the insights it produces. Take the Hadoop ecosystem, for example. Companies often struggle to make the data produced from Hadoop applications available to other departments and applications in a timely manner.

 

 

Automation can have an enormous impact when paired with programmes such as Hadoop. It helps organisations by simplifying the frameworks required to incorporate data from Hadoop into workflows. This means less time is spent preparing and integrating data, giving more time for the worthwhile (and often profitable) process of analysing it. Organisations can thereby achieve deeper insights from data while also leveraging it more quickly.

 

 

Without effective orchestration, data won’t get to the right people in time, reducing its usefulness. The real benefits of big data are realised when better decisions can be made for the company and customer in real time. To make this a reality, it’s increasingly important that organisations fully capitalise on their big data tools and applications, as well as the data generated within them.

 

There are already a number of Hadoop automation tools on the market. For example, Automic claims their software enables “non-technical end users like Data Scientists to quickly build complex Hadoop workflows, reducing development effort and increasing business agility”. The market for such tools will undoubtedly grow in tandem with increased investment in big data technology itself.  

 

 

While automation is only one piece of the puzzle in extracting value from data, it will become increasingly important over the coming years as big data projects grow in complexity.

 

 

 

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