Scramjet Transform Hub (STH) is designed to drastically improve and simplify what has been possible with the Scramjet Framework, giving developers even more tools and freedom in the Cloud. Unlike the current Serverless systems, Scramjet Transform Hub gives you an opportunity to process data from different sources and in multiple scenarios. You can not only separately process small events, but also lists of local files, large files, pull streams from streaming APIs and so on. The choice is yours and STH adapts to your use case natively.
We looked at the competition and compared their solutions to our newly launched STH. This is a lot to process, but most importantly STH offers best-in-class performance metrics at an unrivalled cost while being simple to deploy, in just 4 steps! If Scramjet Transform Hub sounds interesting to you, go and check it out – we believe you’ll be able to unlock great new use cases with STH.
See how to get started on the Scramjet GitHub Repository.
|Mertic||Lambda, Azure Fn, GCF||OpenFaaS, Nuclio||Kafka, Kinesis, Azure EH||Scramjet Transform Hub|
|Data size limit||6 MB||RAM||1 MB||Unlimited|
|Inter-step processing latency (95th)||100 ms||100 ms||N/A||~ 10 μs|
|Maximum processing time||15 min||1 h||N/A||Unlimited|
End-to-end latency level (w/o net). How much library (frameworks, caches, buffers) memory is needed to sustain operations of an 8 step workflow processing 8 concurrent items (compared to 1x).
|64 x||64 x||8 x||1 x|
8 x 8 step external orchestrators. How many additional running systems are needed to sustain operations of an 8 step workflow processing 8 concurrent items.
|9 x||9 x||9 x||1 x|
8 x 8 step serialization operations. How many times the data needs to be serialized and deserialized in an 8 by 8 scenario.
|128 x||128 x||128 x||16 x|
|Metric||Lambda, Azure Fn, GCF||OpenFaaS, Nuclio||Kafka, Kinesis, Azure EH||Scramjet Transform Hub|
|Unit of scale||Event||Data||Mixed||N/A|
|Timescale||seconds||seconds||hrs / days||N/A|
|Pricing model complexity||V-Complex||Open Source||Complex||Open Source|
Scenario low throughput monthly: 0.5 requests per second + 500 ms processing + 512 MB RAM + 10 kb output on average
|$1,11||Servers cost = $ 300||N/A||Free + servers cost = $100|
Scenario generating data for ML 10 GB of items: 500 ms processing + 512 MB RAM + 100 kb output on average
Scenario large data output: 0.5 requests per second + 1000 ms processing + 512 MB RAM + 1 MB output on average
Scenario full month of ops: 1 requests per second + 1000 ms processing + 512 MB RAM
Scenario 50% baseline + 8x scale: Like full month but with 8x peak for 4 hours a day
Scenario 1 TB of i/o ASAP: Best case for processing 1TB of input and 1TB of output data (with additional services when needed)