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Amazon Kinesis Video Streams vs Ziggeo: What are the differences?
What is Amazon Kinesis Video Streams? Capture, process, and store video streams for analytics and machine learning. Amazon Kinesis Video Streams makes it easy to securely stream video from connected devices to AWS for analytics, machine learning (ML), and other processing. Kinesis Video Streams automatically provisions and elastically scales all the infrastructure needed to ingest streaming video data from millions of devices. It also durably stores, encrypts, and indexes video data in your streams, and allows you to access your data through easy-to-use APIs.
What is Ziggeo? An asynchronous interface to record and playback videos on websites. It is a cloud-based video technology SaaS (Software as a Service) company that provides asynchronous video APIs, mobile SDKs and tools to deliver enterprise-grade WebRTC capabilities.
Amazon Kinesis Video Streams and Ziggeo can be primarily classified as "Video Streaming" tools.
Some of the features offered by Amazon Kinesis Video Streams are:
- Device SDKs to securely stream data from devices
- Durable storage
- APIs for real-time and batch data retrieval
On the other hand, Ziggeo provides the following key features:
- Video recording
- Video playback
- Transcoding
We would like to connect a number of (about 25) video streams, from an Amazon S3 bucket containing video data to endpoints accessible to a Docker image, which, when run, will process the input video streams and emit some JSON statistics.
The 25 video streams should be synchronized. Could people share their experiences with a similar scenario and perhaps offer advice about which is better (Wowza, Amazon Kinesis Video Streams) for this kind of problem, or why they chose one technology over the other?
The video stream duration will be quite long (about 8 hours each x 25 camera sources). The 25 video streams will have no audio component. If you worked with a similar problem, what was your experience with scaling, latency, resource requirements, config, etc.?
I have different experience with processing video files that I'll describe below. It might be helpful or at least make you think a bit diffferent about the problem. What I did (part of it is a mistake): To increase the level of parallelism at the time consuming step which was the video upload, using a custom cmd tool written in Python, I splitted the input videos to much smaller chunks (without losing their ordering - just file name labeling with timestamp) . It then uploaded the chunks to S3. That triggered a few Lambdas that each first pulled a chunked video, did the processing with ffmpeg (the Lambdas were the mistake - at that time the local Lambda storage was up to 512MB so lots of chunks and lots of Lambdas had to be in place, also Lambda are hell to debug), later called Rekognition and later using AWS Elemental MediaConvert to rebuild the full length video. I would use some sort of ECS deployment where processing is triggered by S3 event, and scale the number of Fargate nodes dependent on the number of chucks/videos. Then each processor pulls its video (not stream) to its local storage (local EBS drive) and works. I failed to understand why are you trying to stream videos that are basically static, as a file, or that putting the files on S3 is a current limitation (while your input videos are 'live' and streaming) that you're trying to remove ?
