Terality is perfect for interactively exploring big datasets in a Jupyter notebook or a Python script. Still, when you began a session, we used to need a few seconds to provision infrastructure for you. This behavior could make Terality feels a bit unresponsive when you start working with a dataset.
Terality can now provision infrastructure in less than 500 milliseconds, allowing you to get started right away with much less latency. Of course, you still don’t have to concern yourself with said infrastructure: everything is done transparently behind the scenes.
With this change, associated with other optimizations in various operations, Terality can run many operations with less latency. Simple functions (such as DataFrame.head) reliably return a result faster, and complex operations (such as merges or joins) also benefit from these improvements.
To check how Terality compares to the best solutions on the market, we picked the most scientific, unbiased and well-known benchmark for pandas alternatives: the h2o benchmark. It consists of a list of timed simulations on different database-like operations like: join, merge, and groupby, run on different dataset sizes: 0.5, 5 and 50GB. You can check the final section where we give more detail on the experiments and how to reproduce the results for Terality.
After weeks of preparation, we’re proud to finally announce Terality hosted demo notebook - the fastest way to take Terality for a test ride, completely free of charge. We wanted to lower the time needed for you to realize what Terality is all about to 1 click! There’s no better way than running a pre-written tutorial on our infrastructure to experience our pandas lightning-fast serverless data processing