Databricks has become a popular computing framework for big data as organizations increase their investments of moving data applications to the cloud. With that journey comes the promise of better collaboration, processing, and scaling of applications to the Cloud. However, customers are finding unexpected costs eating into their cloud budget as monitoring/observability tools like Ganglia, Grafana, the Databricks console only telling part of the story for charge/showback reports.

The Cloud brings many opportunities to help implement big data across your enterprise and organizations are taking advantage of migrating big data workloads to the cloud by utilizing best of breed technologies like Databricks, Cloudera, Amazon EMR and Azure HDI to name a few. However, as powerful as these technologies are, most organizations that attempt to use them fail. Join Chris Santiago, Director of Solution Engineering as he shares the top reasons why your big data cloud migration fails and ways to overcome it.

Make your data platform faster, better & cheaper with Unravel by joining Chris Santiago, Director of Solution Engineering to learn how to reduce the time troubleshooting and the costs involved in operating your data platform. Instantly understand why technologies such as Spark applications, Kafka jobs, and Impala underperform or even fail! Define and meet enterprise service levels through proactive reporting and alerting.

The movement to utilize data to drive more effective business outcomes continues to accelerate. But with this acceleration comes an explosion of complex platforms to collect, process, store, and analyze this data. Ensuring these platforms are utilized optimally is a tremendous challenge for businesses. Join Mick Nolen at Senior Solutions Engineer at Unravel Data, as he takes you through Unravel’s approach to migrating big data workloads to the Cloud. Whether you’re migrating from