People in the GPU space and slow storage systems like hadoop and S3)
To caveat, I saw slow compared to something like storing your queryable data in system memory. The answer is that your clients are not normally GPU people. They are aware of GPUs and know they can be used for certain things but real clients normally have lots of data and they don't fire up a million instances to let it sit in a $10/GB storage solution. So we are able to help people that are already leveraging these kinds of storage technologies out of necessity.
Other playrers taking the whole stack)
When you say other players are trying to run the whole stack themselves it depends on what you mean. From my understanding Kinetica is an in memory solution for example. How do you query a 100TB data set? Or a 1PB dataset? They are instead going after a specific kind of problem so I wouldn't consider any of us to be trying to run the whole stack. This is usually left up to behemoths and none of us are that in the GPGPU database space yet.
As far as where our focus is. We want you to be able to use blazing to accelerate query workloads more and more on data where it lies how it lies, You can ingest into blazing for maximum performance but the big difference between us and some of the other gpu players is that our focus is on working on datasets that leverage multiple understores (e.g. hdfs, s3, azure file share) and use multiple file formats. Right now the only two file formats we operate on natively are Simpatico (our own file format) and Parquet. Others have to be ingester but we are working on adding more file formats tha we can interact with efficiently in place without the need for ingestion.
As far as trying to take over the whole stack. Blazing wants to be a lean but powerful development shop. We want to focus on our core competencies and leverage other brillliant technologies when possible to avoid having to manage everyrhing. We don't want to build new file systems. And we think that there is a really big value proposition in helping people analyze data where it already lies.
As per your last question.
This is not our first pajama party when it comes to startups and something we have seen is that early on it feels very similar to consulting when you are establishing product market fit. You adjust your product to the immediate needs of the people you are interacting with. That road can be a troublesome one if you end up spending all of your time configuring other toosl and products and not developing your own. Keeping a tight handle on the funnel early on will help ensure that you have enough resources to work on your main value proposition and also increase the likelihood that the few engagements you do take lead to long term revenue and licenses (if that is how you make your money).
In the long run my main strategy is try to do as little as possible. Try to do it really well.