Azure Analysis Services delivers enterprise-grade BI semantic modeling capabilities with the scale, flexibility, and management benefits of the cloud. Azure Analysis Services helps transform complex data into actionable insights. Azure Analysis Services is built on the analytics engine in Microsoft SQL Server Analysis Services.
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Google BigQuery
Score 8.6 out of 10
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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.
$6.25
per TiB (after the 1st 1 TiB per month, which is free)
We would have many technical issues and glitches with previous similar providers but found that Azure Analysis Services can simply handle our workload and memory better. I remember we lost an account due to cloud issues not fully saving or corrupting some files. Granted, this is rare with any cloud but haven't had that issue with the same load of memory with Azure Analysis Services.
Google BigQuery really shines in scenarios requiring real-time analytics on large data streams and predictive analytics with its machine learning integration. Teams have been using it extensively all over. However, it may not be the best fit for organizations dealing with small datasets because of the higher costs. And also, it might not be the best fit for highly complex data transformations, where simpler or more specialized solutions could be more appropriate.
Providing role based access or we can say privilege based on the role to the user if it is integrated with Azure active directory and hence securing the access to sensitive data.
We use to run different type of analytics services to get the better result which is hectic if done manually or with human efforts.
We also use to collect bulk of data with the help of this tool and run customized test cases for better efficiency of result and better decision making. The result are very crucial and helps in taking big decision.
It supports different or we can say heterogeneous database vendors like the Oracle, SQL, and hence make the task easy.
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.
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.
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.
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.
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
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.
The best thing about it is the ability to create a query and drill down the data to a more granular level when needed. The best thing about Azure Analysis Services is. It provides secured access anytime from anywhere, it also provides REST API for this, which is very easy to use, and it provides video tutorials for beginners means you get the whole package of learning and implementation!!
I have used Snowflake and DataGrip for data retrieval as well as Google BigQuery and can say that all these tools compete for head to head. It is very difficult to say which is better than the other but some features provided by Google BigQuery give it an edge over the others. For example, the reliability of Google is unmatchable by others. One thing that I really like is the ability to integrate Data Studio so easily with Google BigQuery.
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.
Pricing has been very reasonable for us. The first 10 GB of storage is free each month and costs start at 2 cents per GB per month after that. For example, if you store 1 terabyte (TB) for a month, then the cost would be $20. Streaming data inserts start at 1 cent per 200 megabytes (MBs). The first 1 TB of queries is free, with additional analysis at $5 per TB thereafter. Meta data operations are free.
Big Query helps reduce the bar for data analytics, ML and AI. BQ takes care of mundane tasks and streamlines for easy data processing, consumption. The most impressive thing is the ML and AI integration as SQL functions, so the need for moving data around is minimized.
The visuals of ML models is very helpful to fine tune training, model building and prediction, etc.
