There are good reasons to move big data systems to the cloud, but doing so also poses challenges for IT teams on migrating workloads and then managing clusters and system instances.
NEW YORK -- Companies are increasingly shifting big data clusters to the cloud for more flexibility and easier scalability. But IT managers who have made the move warn that getting the clusters there isn't easy, and that there are ongoing complications to contend with after you do.
The hurdles start with workload and data migration challenges, and they continue with a variety of management issues, according to speakers and attendees at the 2017 Strata Data Conference here. They pointed to things such as frequent system crashes and the need to carefully manage temporary clusters that are set up to run particular processing jobs and then shut down. In addition, they said some workloads aren't a good fit for the cloud computing model, which can require integration with systems that are left running internally.
The ability to dynamically spin up and modify big data clusters as needed in the cloud makes dealing with the downsides worthwhile for Chris Mills, who leads the big data team at The Meet Group Inc., a New Hope, Pa., company that operates a set of social networking and online dating sites.
After switching from an on-premises big data environment to one in the Amazon Web Services (AWS) cloud, clusters can be added or expanded "in minutes," Mills said. That has reduced IT overhead costs and made experimental and "deep-dive" analytics applications more feasible, he added.
But moving to the cloud "is going to cost more and take longer than you planned," Mills cautioned in a conference session. In The Meet Group's case, that was partly due to the project team identifying potential new applications during the migration process. But unexpected issues also cropped up along the way, he said. All told, it took about six months to set up the cloud-based big data architecture, and another six months to fine-tune the environment. View More
