BigQuery vs Microsoft Analytics Platform System comparison

"The product is serverless. We only need to write SQL queries to analyze the data. We need to pay based on the number of queries. The retrieval time is very less. Even if you write large queries, the tool is able to bring back data in a few seconds."

"BigQuery is a powerful tool for managing and analyzing large datasets. The versatility of BigQuery extends to its compatibility with external data visualization tools like Power BI and Tableau. This means you not only get query results but can also seamlessly integrate and visualize your data for better insights."

"The setup is simple."

"As a cloud solution, it's easy to set up."

"The solution is very useful nowadays for keeping a huge number of records."

"The query tool is scalable and allows for petabytes of data."

"The initial setup is straightforward."

"It has a well-structured suite of complimentary tools for data integration and so forth."

"I like that it's integrated with other Azure products."

"The Cube Solution is quite different when compared to the rest of the competition and has unique functionality for advanced analytics."

"It is closely integrated with other products in the MS portfolio."

"We leverage its capabilities for many applications. We can integrate with our databases, like Oracle, MySQL, or any other, using Microsoft Integration Services."

"Helps our customers to discover trends, which provides useful information based on their business."

"Microsoft Analytics Platform System's most valuable feature is its ecosystems and seamless integration with other Microsoft reporting platforms and databases."

"This solution will connect to any database, you can combine databases, and you can create a cube or tabular model."

"This is a well-integrated solution and that integration empowers results."

"It would be better if BigQuery didn't have huge restrictions. For example, when we migrate from on-premises to on-premise, the data which handles all ebook characters can be handled on-premise. But in BigQuery, we have huge restrictions. If we have some symbols, like a hash or other special characters, it won't accept them. Not in all cases, but it won't accept a few special characters, and when we migrate, we get errors. We need to use Regexp or something similar to replace that with another character. This isn't expected from a high-range technology like BigQuery. It has to adapt all products. For instance, if we have a TV Showroom, the TV symbol will be there in the shop name. Teradata and Apache Spark accept this, but BigQuery won't. This is the primary concern that we had. In the next release, it would be better if the query on the external table also had cache. Right now, we are using a GCS bucket, and in the native table, we have cache. For example, if we query the same table, it won't cost because it will try to fetch the records from the cached result. But when we run queries on the external table a number of times, it won't be cached. That's a major drawback of BigQuery. Only the native table has the cache option, and the external table doesn't. If there is an option to have an external table for cache purposes, it'll be a significant advantage for our organization."

"An area for improvement in BigQuery is its UI because it's not working very well. Pricing for the solution is also very high."

"The solution hinges on Google patterns so continued improvement is important."

"We'd like to have more integrations with other technologies."

"BigQuery should integrate with other tools, such as Cloud Logging and Local Studio, to enhance its capabilities further and enable powerful and innovative analyses."

"There is a good amount of documentation out there, but they're consistently making changes to the platform, and, like, their literature hasn't been updated on some plans."

"There is a limitation when copying data directly from BigQuery; it only supports up to ten MB when copying data to the clipboard."

"We would like to be able to calibrate the solution to run on top of a raw file."

"​Hybrid environments are complex to manage."

"Machine learning and artificial intelligence capabilities need to be more friendly for beginning users."

"The pricing model needs to be improved."

"Releases of new products and functionality is never accompanied by associated documentation, training and resources that adequately explain the release."

"The flexibility of this solution needs to be improved because you cannot make changes at every one of the different steps."

"I think the biggest problem with the product is that it does a data ingest model, which is very expensive."

"Functionality needs to be more up-to-date with competing products."

"We need better real-time analytics capabilities. It's a bit challenging for us."

"Its cost structure operates on a pay-as-you-go model."

"The solution's pricing is cheaper compared to other solutions."

"BigQuery pricing can increase quickly. It's a high-priced solution."

"Price-wise, I think that is very reasonable."

"The pricing is good and there are no additional costs involved."

"The product operates on a pay-for-use model. Costs include storage and query execution, which can accumulate based on data volume and complexity."

"The tool has competitive pricing."

"We are above the free threshold, so we are paying around 40 euros per month for BigQuery."

"Users have to pay extra for premium-level technical support."

"I rate Microsoft Analytics Platform System a seven out of ten for pricing."

"The initial price is lower than Oracle, but extensive use of SQL may lead to a higher total cost of ownership."

"We are currently paying $200,000 a year for all the different parts of the suite during an ingest model Microsoft now charges us $700,000 a year."