In our previous blog post in this series, we explored the benefits of using GPUs for data science workflows, and demonstrated how to set up sessions in Cloudera Machine Learning (CML) to access NVIDIA GPUs for accelerating Machine Learning Projects.
We've moved from desktop to SaaS, to a real UX focus. Now we're seeing new vendors that are analytics-first. They’re creating new applications that are challenging the established players. Historically, applications were transaction-first; you build your software thinking about your workflow or the transactions that you want people to do.
Custom code to connect with data APIs is quickly becoming a thing of the past.
Facebook recently announced that it will effectively discontinue Facebook Analytics on June 30, 2021. The announcement was not particularly informative and was limited to pointing out ways of retaining the tool’s users by means of diverting business to other features that Facebook already offers. However, the reasons behind this decision were not addressed by Facebook and it brings up the question of what this means for the industry.
With its low-code and no-code features, Xplenty brings the power of ETL and data integration to the masses. But even with Xplenty’s tremendously user-friendly interface, it’s possible that the transformations you design don’t work exactly as you intended—which means you need to debug and resolve the issue fast. Fortunately, there are multiple debugging options in Xplenty for exactly this reason.
The faster you can extract, transform, and load data from MongoDB, the better it is for your business processes and business intelligence systems. The problem is, most ETL solutions struggle to manage MongoDB’s dynamic schemas, NoSQL support, and JSON data types. That’s not the case with Xplenty – which was optimized for easy, no-fuss MongoDB integrations with ease: no custom code, no delays, no confusion.
