Azure Analysis Services vs. Google BigQuery

Microsoft Azure Analysis Services is best tool which is well suited for many type of scenarios. Like if the organization is dealing with a lot of critical data and need some better analysis and insights for that data then tool serves the best. It helps in depth analysis and getting the desired result which helps in making big decision for any organization. We can create role based access for sensitive data hence it is very helpful for security point of view. Helps in making the business more productive and taking decision based on facts. It is less appropriate for scenarios like where data amount is less and the solution is very costly and someone can get a cheaper solution. Also not suited for environment where user directory do not exist because without the help of user directory role could not be created hence proper utilization of this tool will not be possible.

Incentivized

Google BigQuery is great for being the central datastore and entry point of data if you're on GCP. It seamlessly integrates with other Google products, meaning you can ingest data from other Google products with ease and little technical knowledge, and all of it is near real-time. Being serverless, BigQuery will scale with you, which means you don't have to worry about contention or spikes in demand/storage. This can, however, mean your costs can run away quickly or mount up at short notice.

Incentivized

• You can import data from various sources (i.e. Oracle, Azure SQL...)
• You can store data models
• You can elaborate data and create insights
• Perfect integration with Excel and Power BI

Incentivized

• 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

• Microsoft Azure Analysis Services is very costly solution and in that price we can get some better business intelligence tool with lot more of capabilities
• The dashboard or we can say user interface is complex and need time to understand and gain expertise in order for proper working.
• It needs continuation monitoring which is sometime a big task.
• Sometime, the tool shows unusual behavior and become unstable, so we need to clear temp files for proper functioning.

Incentivized

• It is challenging to predict costs due to BigQuery's pay-per-query pricing model. User-friendly cost estimation tools, along with improved budget alerting features, could help users better manage and predict expenses.
• The BigQuery interface is less intuitive. A more user-friendly interface, enhanced documentation, and built-in tutorial systems could make BigQuery more accessible to a broader audience.

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

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

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

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

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 have used the IBM cloud which was truly a specific nightmare for our team. User experience, layout, and design is big for us as it understandably is with many people. Even if any type of program can technically do all that we need it to, we still found our team will not be as motivated or satisfied using it compared to something more visually appealing and smooth.

Incentivized

Google BigQuery of course collects a much much larger array of raw data and can handle (practically) an unlimited amount of data. For a large enterprise like ours that relies on large-scale analytics, this is absolutely imperative. Google BigQuery can also combine GA4 data with external sources (like CRM tools), so our analytics can be unified. Due to our heavy reliance on GA4, Google BigQuery is the natural choice since it is a Google product and has better integration.

Incentivized

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

• Website analytics; We have realized so many benefits like traffic analysis, CRM, click-through funnels, and many more.
• Aas allows us to do complex calculations and aggregations at slight efforts. It accommodates DAX expression, which in return is simple to apply.

Incentivized

• In some places, Google BigQuery has helped us save some money by avoiding the need for expensive infrastructure and reducing some of the operational costs.
• Scalability is up-to-date and really helpful in multiple places.
• Knowledge transfer is easy as it is very user-friendly, so the learning curve has been reduced.
• Also, it gives us more insights from our data, helping us make smarter decisions for our business.

Incentivized