BigQuery vs TIBCO Live Datamart comparison

"BigQuery excels at structuring data, performing predictions, and conducting insightful analyses and it leverages machine learning and artificial intelligence capabilities, powered by Google's Duarte AI."

"It's similar to a Hadoop cluster, except it's managed by Google."

"The main thing I like about BigQuery is storage. We did an on-premise BigQuery migration with trillions of records. Usually, we have to deal with insufficient storage on-premises, but in BigQuery, we don't get that because it's like cloud storage, and we can have any number of records. That is one advantage. The next major advantage is the column length. We have some limits on column length on-premises, like 10,000, and we have to design it based on that. However, with BigQuery, we don't need to design the column length at all. It will expand or shrink based on the records it's getting. I can give you a real-life example based on our migration from on-premises to GCP. There was a dimension table with a general number of records, and when we queried that on-premises, like in Apache Spark or Teradata, it took around half an hour to get those records. In BigQuery, it was instant. As it's very fast, you can get it in two or three minutes. That was very helpful for our engineers. Usually, we have to run a query on-premises and go for a break while waiting for that query to give us the results. It's not the case with BigQuery because it instantly provides results when we run it. So, that makes the work fast, it helps a lot, and it helps save a lot of time. It also has a reasonable performance rate and smart tuning. Suppose we need to perform some joins, BigQuery has a smart tuning option, and it'll tune itself and tell us the best way a query can be done in the backend. To be frank, the performance, reliability, and everything else have improved, even the downtime. Usually, on-premise servers have some downtime, but as BigQuery is multiregional, we have storage in three different locations. So, downtime is also not getting impacted. For example, if the Atlantic ocean location has some downtime, or the server is down, we can use data that is stored in Africa or somewhere else. We have three or four storage locations, and that's the main advantage."

"The most valuable aspect of BigQuery is its ability to handle high data workloads without causing friction with our online systems."

"BigQuery excels at data analysis. It processes vast amounts of information using its advanced architecture and sophisticated querying capabilities, making it crucial for critical insights and safe for handling sensitive data."

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

"BigQuery processes a substantial amount of data, whether in gigabytes or terabytes, swiftly producing desired data within one or two minutes."

"The product's most valuable features include its scalability and the ability to handle complex queries on large datasets."

"You can create your own rules that include mathematic calculations."

"The solution has a powerful aggregating feature"

"We'd like to see more local data residency."

"When I open many of the Google Cloud products, I am in an environment that I do not feel familiar with; it is a little overwhelming."

"With other columnar databases like Snowflake, you can actually increase your VM size or increase your machine size, and you can buy more memory and it will start working faster, but that's not available in BigQuery. You have to actually open a ticket and then follow it up with Google support."

"When it comes to queries or the code being executed in the data warehouse, the management of this code, like integration with the GitHub repository or the GitLab repository, is kind of complicated, and it's not so direct."

"Instead of connecting directly to BigQuery, we connect to GCP, Cloud Run, and then to BigQuery, which is a long process."

"The processing capability can be an area of improvement."

"It would be beneficial if BigQuery could be made more affordable."

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

"Improvements need to be made on the load balancing side."

"The solution's setup could be quicker and easier."

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

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

"The tool has competitive pricing."

"I have tried my own setup using my Gmail ID, and I think it had a $300 limit for free for a new user. That's what Google is offering, and we can register and create a project."

"BigQuery is inexpensive."

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

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

"The solution might be considered a bit expensive because it competes with open-source products."