Deploying analytics data to the cloud is top of mind for many organizations. But making the transition away from an on-premises model is about more than transferring data. More often, factors like current data management processes, IT skills, and access to critical business logic will determine whether your move to the cloud will be easy or more difficult.  

There are three common challenges that organizations often face when deploying analytics data to the cloud that can impede success. They are:

  1. Getting your on-premises data into the cloud
  2. Adapting quickly to rapidly changing technology
  3. Delivering modifications rapidly and reliably without error

Let’s look at these challenges and examine what an organization can do to overcome them.

Challenge Number One: Getting Your On-Premises Data into the Cloud

The reality is that most companies still have some level of on-premises data, and some have very high volumes of data residing behind their private networks. Moving this data over a limited bandwidth internet connection can present a significant challenge.

Generally, organizations maintain a highly secure environment. This means that getting in to access the data can require setting up point-to-site VPNs or other complex networking systems.

Organizations not only need access to this data stored in their private networks, they also need to extract analytics data and move it into the cloud fairly quickly. Since it’s both inexpensive and infinitely scalable, Azure Data Lake provides an excellent storage destination for analytics data in the cloud.  However, adapting to data lake concepts like operationalizing the file structures and delta loads are complex, time consuming and require continued use of expensive skills.

So how do you address these challenges and get your data into a data lake quickly and reliably?

One way is by leveraging the power of automation to simplify the process and addresses common issues. Technically, here’s how it works:

  • Direct integration with Azure Data Factory (ADF) integration runtimes allow ADF to push the data to Azure (rather than pull), eliminating the need for complex networking solutions and security loopholes.
  • This type of technology then generates a scalable and fully dynamic ADF pipeline that learns and grows as your data sources change, enabling continued incremental extraction of data, even though database schema changes.
  • In addition, automation technology can create and maintain the folder structure in the data lake as well as maintain the incremental load and organization of optimized parquet files. This dramatically reduces both the complexity and time requirements of the typical data lake setup and management.

Challenge Number Two: Adapting to Rapidly Changing Technology

Managing analytics data in the cloud requires organizations to accept that they must adapt to rapidly changing technology. We all know that both the volume and velocity of data pouring into an organization is accelerating, and companies are struggling to keep up unless they have the data architecture in place to do so. For some businesses, this means that platforms used to manage this data are forced to evolve with it in a reactive fashion.

Organizations with legacy data warehouses are being forced to move to cloud solutions that can scale just to keep up with the growing volume of analytics data. And even these cloud solutions are evolving. Keeping up with these changes presents a massive challenge for organizations. Organizations often find that data professionals need to update their skills regularly and IT teams need to rebuild analytics infrastructure every few years.

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