It was used for some basic purposes because all I did was use Jupyter notebooks to run codes on the Snowflake environment. This was done by me at my previous job. I had just started using Jupyter notebooks to run Python codes on Snowflake data warehouses.
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Though the tool provides exclusive features to run Python codes in a certain environment, it needs to improve support
What is our primary use case?
How has it helped my organization?
I was really more on the engineering side of things. The data scientists built certain models around it. So that, I guess, was a very big improvement.
What is most valuable?
For me, the valuable feature of the solution is the one that I used, which was Jupyter notebooks. Also, I think the data scientists used it to train their models. I am not the biggest user of it.
What needs improvement?
I think maybe the support is an area where it lacks. From my talks with the administrator, I think the support could be improved. My usage of it was limited to Jupyter notebooks.
For how long have I used the solution?
I have experience with IBM Watson Studio for about a couple of months, like three months. Also, I don't remember the version of the solution I was using.
What do I think about the stability of the solution?
Stability-wise, I rate the solution a seven out of ten.
What do I think about the scalability of the solution?
Scalability-wise, I rate the solution a six out of ten. However, I am not the best person to judge the solution based on scalability.
How was the initial setup?
The solution is deployed on the cloud.
What was our ROI?
I would think that my organization had seen a return on investment. However, I am not the best person to speak on its ROI.
What other advice do I have?
To those evaluating the product, I would say it's a good product to use from what I know, and it's not bad. Right now, we're just really making comparisons. I rate the overall solution a seven out of ten.
Review for DataStax
What do you like best about the product?
Our data was decentralized. It has therefore become indispensable to implement a customer master data management solution to deploy a consolidated vision of our clients in one single point.
What do you dislike about the product?
Beneath the covers, the Cassandra data storage layer is basically a key-value storage system. This means that you must "model" your data around the queries you want to surface, rather than around the structure of the data itself. This can lead to storing the data multiple times in different ways to be able to satisfy the requirements of your application.
What problems is the product solving and how is that benefiting you?
The write volume it can handle. It is able to handle such a large volume of writes by first writing to an in-memory data structure, then to an append-only log.
DSE an acceptible product with high maintenance overhead
What do you like best about the product?
The all in one integration between Cassandra, Solr, and Spark makes it simple to use together. Once installed, configuring them for basic out of the box functionality is completed by just enabling each one in a file. Once you get into the Ops Center and maintaining a production cluster, it starts to get more complicated. However, the DataStax academy provides the information needed to start more advanced configurations and implementations.
What do you dislike about the product?
Maintaining a production cluster is very time consuming, requiring a full time resource just to manage this application for a larger system. The cloud resources needed to support this are pretty large. It's almost a wash compared to cloud native services. Beyond the generic training videos and programs, the extended support and training is a lot of extra effort to get spun up and covered.
What problems is the product solving and how is that benefiting you?
DataStax Enterprise solution provides a great backend data layer to our microservices in the cloud. Inventory search and the speed at which results are returned means this can be used for bot functions and other AI capabilities.
Recommendations to others considering the product:
Make sure you have the experience on staff to support this application moving forward. Without at least one experienced resource who has some combination of NoSQL solutions, search understanding, and basic AI capabilities, you will likely have to train a resource 6-8 months just to be comfortable with the product.
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