Amazon Web Services vs. Google BigQuery vs. Microsoft Azure

This is something that is actually common across most cloud providers. A comprehensive understanding of one's use cases, constraints and future directions is key to determining if you even need a cloud solution. If you are a 2-person startup developing something with a best-scenario audience of 1k DAU in a year, you would very likely best served by a dirt-cheap dedicated Linux server somewhere (and your options to graduate to a cloud solution will still be open). If, however, you are a bigger fish, and/or you are actively considering build-vs-buy decisions for complicated, highly-loaded, six-figure requests per minute systems, global loadbalancing, extreme growth projections - then MAYBE you solve all or part of it with a cloud provider. And depending on your taste for risk, reliability, flexibility, track record - it might be AWS.

Incentivized

Event-based data can be captured seamlessly from our data layers (and exported to Google BigQuery). When events like page-views, clicks, add-to-cart are tracked, Google BigQuery can help efficiently with running queries to observe patterns in user behaviour. That intermediate step of trying to "untangle" event data is resolved by Google BigQuery. A scenario where it could possibly be less appropriate is when analysing "granular" details (like small changes to a database happening very frequently).

Incentivized

Azure is particularly well suited for enterprise environments with existing Microsoft investments, those that require robust compliance features, and organizations that need hybrid cloud capabilities that bridge on-premises and cloud infrastructure. In my opinion, Azure is less appropriate for cost-sensitive startups or small businesses without dedicated cloud expertise and scenarios requiring edge computing use cases with limited connectivity. Azure offers comprehensive solutions for most business needs but can feel like there is a higher learning curve than other cloud-based providers, depending on the product and use case.

• During the month-end, we experience high resource utilization; however, with AWS's scalability, we can effectively tackle the peak load.
• With AWS IAM, we don't need to set up complete infrastructure for identity and access management, as AWS provides end-to-end IAM services.
• With AWS, development has become very easy as it's very quick to spin up and destroy the environment, which saves costs.

Incentivized

• Realtime integration with Google Sheets.
• GSheet data can be linked to a BigQuery table and the data in that sheet is ingested in realtime into BigQuery. It's a live 'sync' which means it supports insertions, deletions, and alterations. The only limitation here is the schema'; this remains static once the table is created.
• Seamless integration with other GCP products.
• A simple pipeline might look like this:-
• GForms -> GSheets -> BigQuery -> Looker
• It all links up really well and with ease.
• One instance holds many projects.
• Separating data into datamarts or datameshes is really easy in BigQuery, since one BigQuery instance can hold multiple projects; which are isolated collections of datasets.

Incentivized

• Microsoft Azure is highly scalable and flexible. You can quickly scale up or down additional resources and computing power.
• You have no longer upfront investments for hardware. You only pay for the use of your computing power, storage space, or services.
• The uptime that can be achieved and guaranteed is very important for our company. This includes the rapid maintenance for security updates that are mostly carried out by Microsoft.
• The wide range of capabilities of services that are possible in Microsoft Azure. You can practically put or create anything in Microsoft Azure.

Incentivized

• When there is any misconfiguration of EC2 related to SSM Connect. It doesn't clearly states that what particular configuration is missing.
• Debugging networking related issues could be improved.
• From the security group page, it's difficult to determine which resource a security group is associated with.

Incentivized

• Please expand the availability of documentation, tutorials, and community forums to provide developers with comprehensive support and guidance on using Google BigQuery effectively for their projects.
• If possible, simplify the pricing model and provide clearer cost breakdowns to help users understand and plan for expenses when using Google BigQuery. Also, some cost reduction is welcome.
• It still misses the process of importing data into Google BigQuery. Probably, by improving compatibility with different data formats and sources and reducing the complexity of data ingestion workflows, it can be made to work.

Incentivized

• The cost of resources is difficult to determine, technical documentation is frequently out of date, and documentation and mapping capabilities are lacking.
• The documentation needs to be improved, and some advanced configuration options require research and experimentation.
• Microsoft's licensing scheme is too complex for the average user, and Azure SQL syntax is too different from traditional SQL.

We are almost entirely satisfied with the service. In order to move off it, we'd have to build for ourselves many of the services that AWS provides and the cost would be prohibitive. Although there are cost savings and security benefits to returning to the colo facility, we could never afford to do it, and we'd hate to give up the innovation and constant cycle of new features that AWS gives us.

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.

AWS offers a wide range of powerful services that cater to various business needs which is significant strength. The ability to scale resources on-demand is a major advantage making it suitable for businesses of all sizes. The sheer volume of options and configurations can be overwhelming for new users leading to a steep learning curve. While functional the AWS management console can feel cluttered and less intuitive compared to some competitors which can hinder navigation. Although some documentation lacks clarity and practical examples which can frustrate users trying to implement specific solutions.

Incentivized

I think overall it is easy to use. I haven't done anything from the development side but an more of an end user of reporting tables built in Google BigQuery. I connect data visualization tools like Tableau or Power BI to the BigQuery reporting tables to analyze trends and create complex dashboards.

Incentivized

As Microsoft Azure is [doing a] really good with PaaS. The need of a market is to have [a] combo of PaaS and IaaS. While AWS is making [an] exceptionally well blend of both of them, Azure needs to work more on DevOps and Automation stuff. Apart from that, I would recommend Azure as a great platform for cloud services as scale.

Incentivized

Availability is very good, with the exception of occasional spectacular outages.

I have never had any significant issues with Google Big Query. It always seems to be up and running properly when I need it. I cannot recall any times where I received any kind of application errors or unplanned outages. If there were any they were resolved quickly by my IT team so I didn't notice them.

Incentivized

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

Incentivized

AWS does not provide the raw performance that you can get by building your own custom infrastructure. However, it is often the case that the benefits of specialized, high-performance hardware do not necessarily outweigh the significant extra cost and risk. Performance as perceived by the user is very different from raw throughput.

I think Google Big Query's performance is in the acceptable range. Sometimes larger datasets are somewhat sluggish to load but for most of our applications it performs at a reasonable speed. We do have some reports that include a lot of complex calculations and others that run on granular store level data that so sometimes take a bit longer to load which can be frustrating.

Incentivized

The customer support of Amazon Web Services are quick in their responses. I appreciate its entire team, which works amazingly, and provides professional support. AWS is a great tool, indeed, to provide customers a suitable way to
immediately search for their compatible software's and also to guide them in a
good direction. Moreover, this product is a good suggestion for every type of
company because of its affordability and ease of use.

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

We were running Windows Server and Active Directory, so [Microsoft] Azure was a seamless transition. We ran into a few, if any support issues, however, the availability of Microsoft Azure's support team was more than willing and able to guide us through the process. They even proposed solutions to issues we had not even thought of!

Incentivized

The API's were very well documented and was Janova's main point of entry into the services.

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.

Amazon Web Services fits best for all levels of organisations like startup, mid level or enterprise. The services are easy to use and doesn't require a high level of understanding as you can learn via blogs or youtube videos. AWS is Reasonable in cost as the plan is pay as you use.

Incentivized

PowerBI can connect to GA4 for example but the data processing is more complicated and it takes longer to create dashboards. Azure is great once the data import has been configured but it's not an easy task for small businesses as it is with BigQuery.

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.

We have continued to expand out use of Google Big Query over the years. I'd say its flexibility and scalability is actually quite good. It also integrates well with other tools like Tableau and Power BI. It has served the needs of multiple data sources across multiple departments within my company.

Incentivized

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.

• Using Amazon Web Services has allowed us to develop and deploy new SAAS solutions quicker than we did when we used traditional web hosting. This has allowed us to grow our service offerings to clients and also add more value to our existing services.
• Having AWS deployed has also allowed our development team to focus on delivering high-quality software without worrying about whether our servers will be able to handle the demand. Since AWS allows you to adjust your server needs based on demand, we can easily assign a faster server instance to ease and improve service without the client even knowing what we did.

Incentivized

• Previously, running complex queries on our on-premise data warehouse could take hours. Google BigQuery processes the same queries in minutes. We estimate it saves our team at least 25% of their time.
• We can target our marketing campaigns very easily and understand our customer behaviour. It lets us personalize marketing campaigns and product recommendations and experience at least a 20% improvement in overall campaign performance.
• Now, we only pay for the resources we use. Saved $1 million annually on data infrastructure and data storage costs compared to our previous solution.

Incentivized

• For about 2 years we didn't have to do anything with our production VMs, the system ran without a hitch, which meant our engineers could focus on features rather than infrastructure.
• DNS management was very easy in Azure, which made it easy to upgrade our cluster with zero downtime.
• Azure Web UI was easy to work with and navigate, which meant our senior engineers and DevOps team could work with Azure without formal training.

Incentivized