Google's BigQuery is part of the Google Cloud Platform, a database-as-a-service (DBaaS) supporting the querying and rapid analysis of enterprise data.
$4
per 100 slots
MongoDB Atlas
Score 8.7 out of 10
N/A
MongoDB Atlas is the company's automated managed cloud service, supplying automated deployment, provisioning and patching, and other features supporting database monitoring and optimization.
One of the most important aspects while working with data warehousing solutions and analytics is the ability to handle large datasets. Google BigQuery is the best in business for that particular aspect. It is ridiculously fast while handling large data sets. Another aspect where it is well suited is the ability to integrate it with data visualization tools like Data Studio. It is fast, easy to use, and very reliable. The only aspect where I feel it is less appropriate where you have to pay more of inefficient scripts and that can hamper the growth of the company a bit.
It is suitable for database administrative operations that require a high level of speed, since by not working with conventional SQL it is possible to create data records faster, and therefore better manage work time. It is not necessary to create backend connections for the databases, since these backends of different web pages and applications automatically centralize the development data in a single place, which is useful when managing many services in parallel. It is much easier and faster to get a specific document based on special characteristics of your data, through the filtering system of MongoDB Atlas, since it manages large numbers of servers, and has a high capacity of data mimicry. The cluster shows a history of all the operations that have been performed during the day, so that it is easy to make job regressions in case you need to identify an error that occurred at some point during the day. All data from the MongoDB web application is automatically stored in the Atlas cloud.
One issue with Google Cloud Storage is its price. For one to have that premium Google Cloud Storage, for the purpose of massive storage, he/she must have adequate cash. Otherwise, Google Cloud Storage is a safe and perfect online storage platform.
The only thing that can come to mind that would be annoying with this software was that sometimes when trying to share files on the Cloud with coworkers, it would just not share at all, or there would be a massive delay in when I shared them and when they received them. Other than that though, everything is perfect with this.
web UI is easy and convenient. Many RDBMS clients such as aqua data studio, Dbeaver data grid, and others connect. Range of well-documented APIs available. The range of features keeps expanding, increasing similar features to traditional RDBMS such as Oracle and DB2
It’s Google, they’re big and well organized, the documentation is abundant and the scalability is amazing. The UX is good too, considering it’s a professional tool expected to be used by people with a specific technical background. Overall, it makes me feels good and secure that we know where to store the data, how to use that data and that the data is handled with utmost security and performance practices.
We love MongoDB support and have great relationship with them. When we decided to go with MongoDB Atlas, they sent a team of 5 to our company to discuss the process of setting up a Mongo cluster and walked us through. when we have questions, we create a ticket and they will respond very quickly
Spinning up, provisioning, maintaining and debugging a Hadoop solution can be non-trivial, painful. I'm talking about both GCE based or HDInsight clusters. It requires expertise (+ employee hire, costs). With BigQuery if someone has a good SQL knowledge (and maybe a little programming), can already start to test and develop. All of the infrastructure and platform services are taken care of. Google BigQuery is a magnitudes simpler to use than Hadoop, but you have to evaluate the costs. BigQuery billing is dependent on your data size and how much data your query touches.
When choosing a NoSQL, open source database, MongoDB is the clear winner from an implementation standpoint. For databases that are better suited for highly-organized data, a traditional database engine like MySQL, PostgreSQL, or Oracle's RDBMS may be a better choice. When the requirement is for a NoSQL production database, MongoDB and Atlas are the clear winners from an implementation and management perspective and is very cost-competitive with offerings from Azure Cosmos and Amazon Document DB.
Google Support has kindly provide individual support and consultants to assist with the integration work. In the circumstance where the consultants are not present to support with the work, Google Support Helpline will always be available to answer to the queries without having to wait for more than 3 days.
Google BigQuery has had enormous impact in terms of ROI to our business, as it has allowed us to ease our dependence on our physical servers, which we pay for monthly from another hosting service. We have been able to run multiple enterprise scale data processing applications with almost no investment
Since our business is highly client focused, Google Cloud Platform, and BigQuery specifically, has allowed us to get very granular in how our usage should be attributed to different projects, clients, and teams.
Plain and simple, I believe the meager investments that we have made in Google BigQuery have paid themselves back hundreds of times over.
Since we can cover a much more exhaustive database management with MongoDB Atlas, we can say that we have partly increased our profit intake in relation to the time we have been using the software.
MongoDB Atlas has improved our earnings by 400%, which leaves our current ROI at a percentage of 5, having achieved our first 200% in the first half year of using the software.
