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Top Cloud Data Migration Challenges in 2022 and How to Fix Them

We recently sat down with Sandeep Uttamchandani, Chief Product Officer at Unravel, to discuss the top cloud data migration challenges in 2022. No question, the pace of data pipelines moving to the cloud is accelerating. But as we see more enterprises moving to the cloud, we also hear more stories about how migrations went off the rails. One report says that 90% of CIOs experience failure or disruption of data migration projects due to the complexity of moving from on-prem to the cloud. Here are Dr.

A Better Approach to Controlling Modern Data Cloud Costs

As anyone running modern data applications in the cloud knows, costs can mushroom out of control very quickly and easily. Getting these costs under control is really all about not spending more than you have to. Unfortunately, the common approach to managing these expenses—which looks at things only at an aggregated infrastructure level—helps control only about 5% of your cloud spend.

Big Data Meets the Cloud

With interest in big data and cloud increasing around the same time, it wasn’t long until big data began being deployed in the cloud. Big data comes with some challenges when deployed in traditional, on-premises settings. There’s significant operational complexity, and, worst of all, scaling deployments to meet the continued exponential growth of data is difficult, time-consuming, and costly.

Managing Costs for Spark on Amazon EMR

Are you looking to optimize costs and resource usage for your Spark jobs on Amazon EMR? Then this is the webinar for you. Overallocating resources, such as memory, is a common fault when setting up Spark jobs. And for Spark jobs running on EMR, adding resources is a click away - but it’s an expensive click, so cost management is critical. Unravel Data is our AI-enabled observability platform for Spark jobs on Amazon EMR and other Big Data technologies. Unravel helps you right-size memory allocations, choose the right number of workers, and map your cluster needs to available instance types.

Managing Costs for Spark on Databricks Webinar

Are you looking to optimize costs and resource usage for your Spark jobs on Databricks? Then this is the webinar for you. Overallocating resources, such as memory, is a common fault when setting up Spark jobs. And for Spark jobs running on Databricks, adding resources is a click away - but it’s an expensive click, so cost management is critical.

A Primer on Hybrid Cloud and Edge Infrastructure

Thank you for your interest in the 451 Research Report, Living on the edge: A primer on hybrid cloud and edge infrastructure. You can download it here. 451 Research: Living on the edge: A primer on hybrid cloud and edge infrastructure Published Date: October 11, 2021 Introduction Without the internet, the cloud is nothing. But few of us really understand what is inside the internet. What is the so-called ‘edge’ of the internet, and why does it matter?

Twelve Best Cloud & DataOps Articles

Interested in learning about different technologies and methodologies, such as Databricks, Amazon EMR, cloud computing and DataOps? A good place to start is reading articles that give tips, tricks, and best practices for working with these technologies. Here are some of our favorite articles from experts on cloud migration, cloud management, Spark, Databricks, Amazon EMR, and DataOps!

Managing Cost & Resources Usage for Spark

Spark jobs require resources - and those resources? They can be pricey. If you're looking to speed up completion times, optimize costs, and reduce resource usage for your Spark jobs, this is the webinar for you.For Spark jobs running on-premises, optimizing resource usage is key. For Spark jobs running in the cloud, for example on Amazon EMR or Databricks, adding resources is a click away - but it’s an expensive click, so cost management is critical.

Troubleshooting Databricks

The popularity of Databricks is rocketing skyward, and it is now the leading multi-cloud platform for Spark and analytics workloads, offering fully managed Spark clusters in the cloud. Databricks is fast and organizations generally refactor their applications when moving them to Databricks. The result is strong performance. However, as usage of Databricks grows, so does the importance of reliability for Databricks jobs - especially big data jobs such as Spark workloads. But information you need for troubleshooting is scattered across multiple, voluminous log files.