Data integration is a challenge that keeps getting more difficult. It’s no surprise considering the explosion of cloud-based tools, the proliferation of devices that consume and produce information, and the way information is shared between systems and from systems to humans. Plus, IDC predicts that the volume of data will reach around 40 Zettabytes (1 billion Terabytes equals 1 Zettabyte) by 2020 – and that 90 percent of it will be unstructured.
This rapid growth of data is making legacy data integration technology nearly unusable.
Custom code is onerous
Let’s explore why this is so. Organizations must address four key steps during the data migration and integration process:
- Capture data that supports both the known use cases as well as future undefined use cases (think IoT data to support a future machine learning enabled use case).
- Conform inbound data to corporate standards to ensure governance, quality, consistency, regulatory compliance, and accuracy for downstream consumers.
- Refine data for its eventual downstream application and/or use cases (once it has been captured and conformed to corporate standards).
- Delivery of data needs to be broad and prepared to support future unknown destinations.
For decades, IT has handled data integration by writing volumes of custom code. This onerous task has only grown more complex with the rise in SaaS applications and the constant product releases, the surge in big data, the emergence of the Internet of Things, and the proliferation of mobile devices.
In most organizations, the IT integration backlog feels insurmountable. It should only take days to deploy a tactical or departmental data warehouse solution. Yet it often takes months. Even enterprise-wide data transformation projects which should take months often last years. Both the deployment and the costs to maintain all the integrations are overwhelming most IT organizations.
Enter modern integration platforms
Fortunately, the days are long gone when IT needed hundreds of coders to create an extract, transform, load (ETL) process and then maintain it by writing more code. Modern integration platforms eliminate the need for custom coding. In fact, they make it possible to deploy and scale data integration projects as much as ten times faster.
These platforms ease data integration pain because they’re designed for flexibility and easy deployment for any project. The best of these feature a drag-and-drop UX coupled with a powerful platform and hundreds of pre-built connectors out of the box. In fact, they largely leave data in place, and instead access and transform data where it resides, regardless of its structure (or lack thereof) and location.
Plus, because the connectors are always up-to-date, the IT organization avoids updating each integration every time its updated. This saves an incredible amount of time, money, and frustration across IT and all its projects.
Know what to look for
While integration platforms are a proven way to streamline data integration, not all these platforms are created equal.
- Some are good at simple point-to-point cloud app integrations while others are good at moving and transforming large and complex data into a data lake for advanced analytics.
- Some require extensive developer resources to hand-code APIs while others provide self-service, drag-and-drop offerings that can be used by IT and business leaders alike.
- Some are best for specific tactical projects while others are more fitting as a strategic, enterprise-wide platform for multi-year digital transformation projects.
As your organization considers its options, understand how and who needs to be empowered with data integration capabilities. Then match your requirements to the most relevant solution.
If you’re ready to migrate to Microsoft Azure for a cloud data warehouse, download our guide on how to choose the right integration approach.
