What is our primary use case?
We use CData AWS Glue Data Connectors for our machine learning pipelines on AWS, and we have attempted to use it for building data lakes as well.
CData AWS Glue Data Connectors has been instrumental in providing us with pre-built and supported connectors instead of requiring us to build and maintain integrations ourselves. We specifically use it for rate limit management on machine learning pipelines.
Beyond our primary use case, due to CData AWS Glue Data Connectors's wide integration capabilities, we have also integrated it with our Jira boards. Alongside our machine learning pipelines, it is integrated with our Jira boards as well.
What is most valuable?
One of the best features CData AWS Glue Data Connectors offers is its reduced data duplication. It provides us with near real-time analytics, features a simple architecture, and aids us in onboarding new data sources faster. CData AWS Glue Data Connectors extends the capability of AWS Glue ETL as it currently exists.
Out of those features, the near real-time analytics is what I find myself relying on the most, and this really helps with troubleshooting since we typically need to onboard new data sources fairly regularly, and CData AWS Glue Data Connectors makes that very streamlined and easy.
CData AWS Glue Data Connectors has made the job of data engineers easier. The connectors that CData AWS Glue Data Connectors provides help with easier table discovery, relationship mapping, and schema discovery. This makes the work more streamlined and easier, with less time spent on tasks overall.
What needs improvement?
I think the biggest drawback for CData AWS Glue Data Connectors is the cost. For a small organization, the licensing fee may exceed the cost of just building a few custom integrations. If you do not have the need for a lot of custom integrations, it might not be a good fit.
Regarding everything else about CData AWS Glue Data Connectors, it is good. CData AWS Glue Data Connectors is not a full ETL replacement because it is focused mainly on connectivity. It is not intended to replace AWS Glue transformations, Spark jobs, DBT, or data quality frameworks, but for what it does, it is fairly good at that.
What kept my rating from being higher than a seven is that CData AWS Glue Data Connectors is really good at what it does. The rating is not higher than that mainly because of the cost. The licensing fees are quite expensive, and in order to justify the use, you need to have a large number of integrated systems to justify that cost. Otherwise, it does not work financially.
For how long have I used the solution?
I have been using CData AWS Glue Data Connectors for about a year now.
What do I think about the stability of the solution?
So far, CData AWS Glue Data Connectors has been stable. There have been a few glitches here and there, but I think that is expected. However, nothing has been out of the ordinary or substantial enough to classify it as unstable.
What do I think about the scalability of the solution?
For what it is, CData AWS Glue Data Connectors is fairly scalable. It ranks very highly in terms of scalability.
How are customer service and support?
Customer support for CData AWS Glue Data Connectors is really good. I have had good experiences with their customer support so far.
Which solution did I use previously and why did I switch?
CData AWS Glue Data Connectors was our first choice, and we found that it really worked with our use case, so we just stuck with it.
How was the initial setup?
Setting up CData AWS Glue Data Connectors is a breeze. It is not difficult to set up, but my main concern is with the licensing fees. The costs are quite on the high side, and if you do not have a large number of custom integrations and lots of business applications, the cost might not really be worth it. The savings might not outweigh the cost.
What was our ROI?
In terms of savings, CData AWS Glue Data Connectors has quite helped us with operational savings because we have quite a number of business applications and a large analytic environment, and the cost of having to set up and manage custom integrations is reduced with the use of CData AWS Glue Data Connectors.
What's my experience with pricing, setup cost, and licensing?
Since we started using CData AWS Glue Data Connectors, we have seen about a 20% increase in product delivery speed. Usually the time spent on creating connectors and trying to fashion custom-made connectors can be directed to the main application development itself. I think it saves about 20% in time for new production deployments.
What other advice do I have?
I am not certain about the AI part of CData AWS Glue Data Connectors, but I do know that its governance and security is top-notch. As far as I have seen, I have not had any security or breach issues with it so far.
I have not really used the AI capability of CData AWS Glue Data Connectors, so I cannot really speak to the accuracy or reliability of its output. From what I have heard from others, it sounds as though it is about standard for the market as it currently exists.
You should consider your use cases first regarding using CData AWS Glue Data Connectors. If you do not have large analytic environments or a lot of business applications, it might not be a good fit for you based on the licensing cost. CData AWS Glue Data Connectors really works well with organizations that have large analytic environments and dozens or a large number of business applications. CData AWS Glue Data Connectors really shines in those environments and would really help with operational savings in that regard. I would rate this product a seven out of ten.
Which deployment model are you using for this solution?
Hybrid Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
