By Virupaksh Reddy
Big data platforms, such as Microsoft Azure, provide much of the power for business analytics. But as more data is generated from more sources — think the Internet of Things (IoT), edge computing, and a wide and diverse variety of devices and apps — it becomes more difficult to unlock the platform’s true value.
At Innovizant, we believe that automated data pipelines are the answer. A data pipeline moves data (typically customer data) through these complex analytical ecosystems, helping organizations attain results faster, more efficiently and more cost-effectively.
Big Data, Big Challenges
But extracting information from various sources and delivering it to a big data platform for analytical processing is an ongoing challenge for many companies.
Part of this is due to the sheer volume of data being generated. Ninety percent of the data that exists today was generated within the last two years, and that pace of growth shows no signs of slowing down. When we talk about “Big Data,” it isn’t an exaggeration!
Another challenge is that all this data comes in multiple formats. As SaaS adoption has increased, so has the amount of unstructured data being generated and collected. And for analytics in a big data platform, data must be converted into a common format.
But dealing with a combination of structured and unstructured data, with XML, flat files, text files, video and more can burn up your staff’s limited time. And the more time it takes, the more it costs.
Shifting From ETL To Agile
The traditional practice of extract, transform, load (ETL) is resource-intensive and inflexible. It requires a team of engineers and data scientists to spend months developing workflows that are stagnant and lack efficiency in managing multiple, disparate data sources that, these days, are changing at the speed of business. It also requires layers of customized technology that focus on lowering storage costs by limiting data.
In an environment where data changes daily and sometimes in just seconds, lengthy release cycles that require major system changes and specialized experts. This old school waterfall approach to development stunts the ability of businesses to respond rapidly to market changes.
Today, businesses need processes focused on data discovery and exploration. This requires real-time access to multichannel data and easy deployment to stakeholders across the organization.
Focused on agility, efficiency and innovation, agile development revolves around continuous improvement in response to dynamic change. As automation changes the way business is run, agile processes have gained momentum.
Integration Drives Agility
In our practice, we’ve found that data integration is a critical component of agility — and therefore integral to building a robust automated data pipeline. Boomi’s unified platform as a service (iPaaS) offers all the tools customers need to succeed.
Boomi’s cloud-native integration platform as a service (iPaaS) helps customers ingest customer data from multiple sources — fast. The low-code platform’s drag-and-drop functionality makes it easy to build and deploy integrations in a fraction of the time it would take using other tools. And that agility allows customers to easily connect or disconnect data sources as needed.
Boomi’s visual development tools also make it possible for citizen integrators — business analysts or other non-technical staff — to develop, manage and maintain integrations, reducing the load on IT.
But the Boomi platform doesn’t just deliver integration. It also ties in components like API design and management, workflow automation and more, that power more robust development projects and streamline deployment.
Find out how the Boomi platform can help you – watch our product demo.