BigQuery vs VMware Tanzu Data Solutions comparison

"We like the machine learning features and the high-performance database engine."

"It has a proprietary way of storing and accessing data in its own data store and is 100% managed without you needing to install anything. There is no need to arrange for any infrastructure to be able to use this solution."

"It's pretty stable. It's fast, and it is able to go through large quantities of data pretty quickly."

"I like that we can synch and run a large query. I also like that we can work with a large amount of data. You don't need to work separately, as it's a ready-made solution. It also comes with a built-in machine-learning feature. Once we start inputting the data, it will suggest some things related to the data, and we can come up with nice dashboards and statistics from a vast amount of data."

"The feature called calibrating the capacity is valuable."

"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."

"What I like most about BigQuery is that it's fast and flexible. Another advantage of BigQuery is that it's easy to learn."

"We basically used it to store server data and generate reports for enterprise architects. It was a valuable tool for our enterprise design architect."

"The solution is stable."

"It's one of the fastest databases in the market. It's easy to use. From a maintenance perspective it's a good product. The segmentation, or architecture of the product is different than other databases such as Oracle. So even in 10 years, the data distribution for such segments will not affect other segments. The query performance of the product, for complex queries, is very good. It has good integration with Hadoop."

"I like the high throughput of 20K messages/sec, and that it supports multiple protocols."

"The message routing is the most valuable feature. It is effective and flexible."

"Reliability for the messages is key. RabbitMQ ensures your messages are safe. They are not deleted and stuff."

"Large amounts of data can be moved pretty fast using the solution."

"The parallel load features mean that Greenplum is capable of high-volume data loading in parallel to all of the cluster segments, which is really valuable."

"The most valuable feature is asynchronous calls, which are easy to configure."

"There are many tools that you have to use with BigQuery that are different services also provided for by Google. They need to all be integrated into BigQuery to make the solution easier to use."

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

"There are some limitations in the query latency compared to what it was three years ago."

"They could enhance the platform's user accessibility."

"The process of migrating from Datastore to BigQuery should be improved."

"The main challenges are in the areas of performance and cost optimizations."

"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."

"The solution should reduce its pricing."

"VMware RabbitMQ's configuration process could be easier to understand."

"Implementation takes a long time."

"They need to enhance integration with other Big Data products... to integrate with Big Data platforms, and to open a bi-directional connection between Greenplum and Big Data."

"The user interface could be improved."

"RabbitMQ is clearly better supported on Linux than it is on Windows. There are idiosyncrasies in the Windows version that are not there on Linux."

"If messages pile up until the space of the memory is full, then basically, the cluster goes down, and someone has to log in through the backend and purge all messages."

"The product is pretty hard to configure."

"If you're outside IP address range, the clustering no longer has all the features which is problematic."

"The pricing is adaptable, ensuring that organizations can tailor their usage and costs based on their specific requirements and configurations within the Google Cloud Platform."

"The solution is pretty affordable and quite cheap in comparison to PDP or Cloudera."

"1 TB is free of cost monthly. If you use more than 1 TB a month, then you need to pay 5 dollars extra for each TB."

"The price could be better. Usually, you need to buy the license for a year. Whenever you want more, you can subscribe to it, and you can use it. Otherwise, you can terminate the license. You can use it daily or monthly, and we use it based on a project's requirements."

"BigQuery is inexpensive."

"The pricing appears to be competitive for the intended usage scenarios we have in mind."

"The tool has competitive pricing."

"The product’s pricing could be more flexible for end users."

"We are using the open-source version of this solution."

"are using the open-source version, which can be used free of cost."

"It is the best product with best fit for price/performance customer objectives."

"The pricing for RabbitMQ is reasonable. It is worth the cost."

"The solution's pricing is cost-effective as it does not involve significant expenses. Licensing is required only for the server, while clients do not need any licensing. Therefore, it proves to be a cost-efficient option."

"The price is pretty good."

"It is an open-source platform. Although, we have to pay for additional features."

"The product is available for free use since it is an open-source technology."