When it comes to the competitive landscape, we don’t see direct competitors, we prefer to see opportunities. With this spirit in mind, we are always working to understand what other opportunities there are for delivering data and content streams so that we can better understand the landscape, as well as connect, augment, and work with existing solutions whenever possible. One solution we’ve been keeping an eye on when it comes to augmenting with Stremdata.io is Amazon Kinesis. We are taking a moment and understanding Amazon Kinesis and what they bring to the table when it comes to streaming.
According to their tagline, Amazon Kinesis allows you to “easily collect, process, and analyze video and data streams in real time”. One of the primary features they offer centers around establishing video streams, something we don’t do, but we did want to understand the core features they offer when it comes to data and content streaming:
– Kinesis Data Streams – capture, process, and store data streams – Amazon Kinesis Data Streams enables you to build custom, real-time applications that process data streams using popular stream processing frameworks.
– Kinesis Data Firehose – load data streams into AWS data stores – Amazon Kinesis Data Firehose is the easiest way to capture, transform, and load data streams into AWS data stores for near real-time analytics with existing business intelligence tools.
– Kinesis Data Analytics – analyze data streams with standard SQL – Amazon Kinesis Data Analytics is the easiest way to process data streams in real time with SQL without having to learn new programming languages or processing frameworks.
Amazon Kinesis is pretty focused on integration with other AWS services, so we see an opportunity around helping connect to Kinesis data stream using the AWS Kinesis API, and providing a last mile of stream to the browser, ML models, and other applications. Using the AWS Kinesis API GetRecords method you could easily proxy each Kinesis stream using Streamdata.io, and provide a simpler, easier to handle Server-Sent Events (SSE) stream that could be easily streamed into the browser, or other applications.
We can see the potential for using Amazon Kinesis. Applying the data analytics to large volume streams using AWS infrastructure for storage and processing makes a lot of sense. We aren’t interested in competing with everything they do. We think Streamdata.io makes sense when it comes to delivering last mile streams after you have used Kinesis to analyze data, and you need to then provide simple, easy to use HTTP streams from Kinesis streams that have been setup around different topics, and other segmentation approaches. Much like we already do for web APIs, Kafka, and other existing data delivery solutions. We will keep playing with Kinesis to understand what is possible, and how we can augment and improve upon what they already do using Streamdata.io. Let us know if you are using AWS Kinesis, or are using AWS infrastructure for your data storage needs, and looking to understand how you can stream data from AWS using their existing APIs.
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