Google BigQuery vs. Microsoft Azure

For organizations looking to avoid the overhead of managing infrastructure, BigQuery's server-less architecture allows teams to focus on analyzing data without worrying about server maintenance or capacity planning. Small projects or startups with limited data analysis needs and tight budgets might find other solutions more cost-effective. Also, it is not suitable for OLTP systems.

Incentivized

In terms of cloud computing, Microsoft Azure is the only comprehensive result the company offers. Regardless of how big or small an organization is, it can make use of this system. As a cyber-security professional, this is your best option for data management. A business that wants to minimize capital expenditures can use Microsoft Azure. Many Microsoft services accept it. People with little or no knowledge of cloud computing may find it impossible. It isn’t the solution for companies that don’t want to risk having only one platform and infrastructure vendor.

• Its serverless architecture and underlying Dremel technology are incredibly fast even on complex datasets. I can get answers to my questions almost instantly, without waiting hours for traditional data warehouses to churn through the data.
• Previously, our data was scattered across various databases and spreadsheets and getting a holistic view was pretty difficult. Google BigQuery acts as a central repository and consolidates everything in one place to join data sets and find hidden patterns.
• Running reports on our old systems used to take forever. Google BigQuery's crazy fast query speed lets us get insights from massive datasets in seconds.

Incentivized

• Azure simply provides end to end life cycle. Starting from the development to automated deployment, you will find [a] bunch of options. Custom hook-points allow [integration] on-premise resources as well.
• Excellent documentation around all the services make it really easy for any novice. Overall support by [the] community and Azure Technical team is exceptional.
• BOT Services, Computer Vision services, ML frameworks provide excellent results as compare to similar services provided by other giants in the same space.
• Azure data services provide excellent support to ingest data from different sources, ETL, and consumption of data for BI purpose.

Incentivized

• Can't use it out of Google's cloud platform which is a minus point if you want a local setup.
• Can be a little expensive to manage.
• A little difficult to manage someone with less technical expertise as it requires you to have SQL knowledge of joins, CTEs etc.

Incentivized

• In our experience, Azure Kubernetes Survice was difficult to set up, which is why we used Kubernetes on top of VMs.
• Azure REST API is a bit difficult to use, which made it difficult for us to automate our interactions with Azure.
• Azure's Web UI does a good job of showing metrics on individual VMs, but it would be great if there was a way to show certain metrics from multiple VMs on one dashboard. For example, hard drive usage on our database VMs.

Incentivized

We have to use this product as its a 3rd party supplier choice to utilise this product for their data side backend so will not be likely we will move away from this product in the future unless the 3rd party supplier decides to change data vendors.

Incentivized

Moving to Azure was and still is an organizational strategy and not simply changing vendors. Our product roadmap revolved around Azure as we are in the business of humanitarian relief and Azure and Microsoft play an important part in quickly and efficiently serving all of the world. Migration and investment in Azure should be considered as an overall strategy of an organization and communicated companywide.

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

Microsoft Azure's overall usability has been better than expected. Often times vendors promise the world, only to leave you with a run-down town. Not the case with our experience. From an implementation perspective, all went perfect, and from the user-facing experience we have had no technical issues, just some learning curve issues that are more about "why" than "how"

Incentivized

It has proven to be unreliable in our production environment and services become unavailable without proper notification to system administrators

Incentivized

BigQuery can be difficult to support because it is so solid as a product. Many of the issues you will see are related to your own data sets, however you may see issues importing data and managing jobs. If this occurs, it can be a challenge to get to speak to the correct person who can help you.

Incentivized

Support is easy with all the knowledge base articles available for free on the web. Plus, if you have a preferred status you can leverage their concierge support to get rapid response. Sometimes they’ll bounce you around a lot to get you to the right person, but they are quite responsive (especially when you are paying for the service). Many of the older Microsoft skills are also transferable from old-school on-prem to Azure-based virtual interfaces.

Incentivized

As I have mentioned before the issue with my Oracle Mismatch Version issues that have put a delay on moving one of my platforms will justify my 7 rating.

Google's Firebase isn't a competitor but we had to use Google's BigQuery because Google's Firebase's database is limited compared to Google's BigQuery. Linking your Firebase project to BigQuery lets you access your raw, unsampled event data along with all of your parameters and user properties. Highly recommend connecting the two if you have a mobile app.

Incentivized

As I continue to evaluate the "big three" cloud providers for our clients, I make the following distinctions, though this gap continues to close. AWS is more granular, and inherently powerful in the configuration options compared to [Microsoft] Azure. It is a "developer" platform for cloud. However, Azure PowerShell is helping close this gap. Google Cloud is the leading containerization platform, largely thanks to it building kubernetes from the ground up. Azure containerization is getting better at having the same storage/deployment options.

Incentivized

None so far. Very satisfied with the transparency on contract terms and pricing model.

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.

Incentivized

• Brings down Capex to customers.
• Some of the built-in security features of DDoS Basic protection that comes with VNET on Azure or even WAF on AGW brings huge advantages to customers.
• Hybrid benefits for those who have software assurance can save even more costs by moving to Azure.

Incentivized