The solution is really useful. It’s an easy way to get information. I use it as a reference for analytics, sourcing information, and research.
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The solution is really useful. It’s an easy way to get information. I use it as a reference for analytics, sourcing information, and research.
The APIs are valuable. I also use the open-source things available in the solution.
The product must be made more user-friendly. Sometimes, we have to go a roundabout way and read a lot of instruction that isn't necessary. Generally, if people use the information, they have some knowledge about it. If they don't, there should be an introduction section specifically for it. The rest of it should skip all the instructions.
I have been using the solution since it was started.
The tool’s stability is pretty good. I rate the stability an eight out of ten.
I’m the only user. I use AI a lot. The tool’s scalability depends on who we have contracts with.
We can't talk to anybody. It's all AI-based. It would be good if the vendor kept up on their models. If the AI hasn’t been trained, it tends to go in circles. When we ask to speak to customer support, the AI goes round and round to keep us from doing it.
Now that I am a partner, I have a higher level of support. I can get a hold of administrators directly. However, in the beginning, the usability was bad. The AI sent us in circles when we asked for customer support because it didn't want us to talk to support.
Negative
The initial setup was straightforward. I wish the websites would ask us in the beginning whether or not we know how to use the tool. It could skip all the demos and introductions if we know it. The deployment time depends on how fast we enter the information. If we use templates, it is easy. Depending on our speed, we could set it up in minutes or hours.
I can do the deployment myself. We need to do regular upkeep. We can opt-in for automatic updates from Google. It depends on what cloud we use.
I use the free API versions. The product is cheap. I rate the pricing a two out of ten.
I use the products from Microsoft, Google, and IBM. I use many open-source tools, copy their APIs, and read their documents. Microsoft is easier for API and copying information because we can go over to the documents and click copy.
In Google, we must enter our payment methods even if we're not paying for a service and want to use the free service. It is annoying. If we don't use Google-specific things, it won’t allow us to connect to other cloud services without paying for the subscription. Microsoft is 99% free.
I use a lot of Atlassian and Jira products. I do a lot of code for a lot of bigger-name websites. I apply it through Jira, which is connected to my Google Cloud, which is also connected to my Azure. The tool’s usability is pretty good. Overall, I rate the product an eight out of ten.
Our main use cases involve transferring workloads from AWS and Univision to Google Cloud Datalab. Before coming to the setting we utilised Google Datalab for looker and handling separated tables for research and development scenarios. Currently in association with AWS and Univision, we are focusing and migrating, defining data set tables and moving data to the Google hosted platform.
Since we are working extensively with the solution in managing data sets and regional databases, we focus a lot on handling different table segmenting data based on ids and customising the architecture for unit projects. There was a challenge which included and rose from the rapid and regressive pace of development since we transitioned from AWS to GCP. Adapting to the logical data structure is a complex process because it is fast paced and dynamic due to the project nature. Google Cloud Datalab helps us to incorporate different components and utilise univision simultaneously. It is very customizable.
We have also encountered challenges during our transition period in terms of data control and segmentation. The management of each channel and data structure as it has its own unique characteristics requires very detailed and precise control. The allocation should be appropriate and the complexity increases due to the different time zones and geographic locations of our clients. The process usually involves migrating the existing database sets to gcp and ensure data integrity is maintained. This is the only challenge that we faced while navigating the integers of the solution and honestly, it was an interesting and unique experience.
I have been using Google Cloud Datalab for quite some time now.
Regarding the technical support, I have worked with the various vendors that are Microsoft, AWS and Google while engaging in architecting projects. My approach always involved maintaining an open mind towards technology and considering various factors such as feasibility as specific use cases. In my opinion different vendors have specialised expertise and some collaborations can be challenging as they focus on specific areas without holistic understanding of the full cycle. This specialisation has led to issues even in Google Cloud data lab and their support.
I believe that rating depends on the individual experience and this is a very subjective matter. Understanding factors like collaboration and approach, plays a very important role. In my opinion, Google might have missed some opportunities and is facing challenges compared to competitors like AWS and Azure. However, based on the open source and their association with Docker I will rate them around 6.5 to 7.
