As enterprises seek to migrate and manage their production analytic workloads in the public cloud, we increasingly hear teams considering PaaS offerings (such as AWS EMR, Redshift, Azure HDI) as the first stop for implementation. But many don’t realize that only provides a foundational analytic capability in the cloud. Significant additional work is needed to migrate and manage analytics in production.
A telecommunications company called Cazena to learn about our Data Lake as a Service, hoping that it would help them deliver an at-risk project. It definitely would, it’s a cloud use case we know well, with a solution designed accordingly.
As part of our ongoing series on Productionizing Hadoop and Spark in the cloud, we explore performance optimization, and how companies scale and tune for the best performance. We also discuss what’s required for production-grade deployments, often an underestimated part of the process.
There is an interesting theme mentioned by the leaders of data science and advanced analytics groups: All are focused on how to make their team as productive as possible. The resources for these teams are notoriously hard to find. So, naturally, team leaders want to ensure that these scarce, highly-skilled workers have everything they need to be efficient. Here are the most common pitfalls we hear about. Do you agree?
Over the past few years, I have observed a deepening organizational divide in large data-driven companies. On one hand, IT and data owners have their hands full managing their current data infrastructure and platforms.
Japanese rock gardens, or zen gardens, were first constructed centuries ago at temples as aids to meditation. Also called “dry landscapes,” zen gardens are designed as miniature models of natural landscapes. This practice of artfully modeling the world in miniature seemed like a beautiful analogy to launch our new Data Science Sandbox as a Service…