We use the product to extract data and present it for warehouse reporting.
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We use the product to extract data and present it for warehouse reporting.
The platform provides valuable scalability, stability, and robustness benefits.
The product's pricing needs improvement.
We have been using SQL Azure for 3 years.
I rate the platform's stability an 8 out of 10.
I rate the platform's scalability as 10 out of 10.
The initial setup can be straightforward if you properly configure the product and utilize scripts effectively. Once the script is ready for cloud deployment, executing it is next. The solutions architect sets up the basic infrastructure using infrastructure as code principles. It involves carefully selecting resources, such as VMs and app services, and configuring them according to your requirements. Additionally, one needs to manage security aspects like opening and closing firewalls, which can introduce complexities to the setup process.
I recommend considering a pay-as-you-go pricing model initially.
The automated scalability feature of SQL Azure has proven to be highly beneficial, particularly when deployed in the cloud. It can be done remotely, eliminating the need for manual intervention. However, it may become more challenging if the infrastructure reaches its limits, especially in vertical scaling scenarios.
Whether opting for AWS or Azure, the service offers guaranteed uptime, often around 99.9%. Additionally, it supports deployment across multiple zones and regions.
The transition from on-premises to SQL Azure can impact operational costs depending on the setup. It offers options like pay-as-you-use or dedicated services running 24/7. However, it's essential to evaluate your business case thoroughly before deciding.
I rate it an 8 out of 10.
We restore our golden data from various sources and then push it to MongoDB. We make our CDP from MongoDB, which serves as a device-centric system.
There is a built-in feature called Autoscaling In MongoDB Atlas. This feature automatically adjusts the configuration of MongoDB based on the volume of users we ingest daily. Autoscaling dynamically scales the resources to accommodate the load when our data flow increases.
The real-time data visible within MongoDB Atlas is not accurate. If they can improve the UI that monitors real-time data. It's more impressive and more attractive. It could be more user-friendly.
I have been using MongoDB Atlas for two years.
The product is pretty stable.
The solution is scalable. Autoscaling supports it.
50 users are using this solution
Whenever we have doubts during configuration, we reach out for assistance. We must upgrade certain parameters in our MongoDB setup, prompting us to contact their support team. They resolve such issues within four to five hours.
The initial setup is not very complex. It is easy to use. It's easy to deploy on MongoDB. We push from GitHub. From there, we specify where the data is restored in MongoDB. We continue to connect. It puts the data and delivers it to Argo City.
The product has a yearly subscription.
We have assigned DevOps for security.
The overview and monitoring part will address this issue, and then we will use it to observe any increasing traffic on our website. We also monitor the rising number of connections due to this traffic. It's quite easy to oversee everything in one place. However, the UI isn't particularly user-friendly.
I've also used it in my previous company and found it handy and easy to configure, including easy capabilities.
We are establishing SLAs that are directly tied to MongoDB. All are interconnected with MongoDB. If MongoDB experiences downtime or RAM or CPU usage spikes significantly, users may encounter difficulties logging in. This reliance on MongoDB can pose challenges for user accessibility, particularly when considering the conferencing tools we use.
Overall, I rate the solution an eight out of ten.