BigQuery Reviews

The solution is mostly used for data analysis. We can store data and use the tool for data analysis. 

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. 

The solution should reduce its pricing. 

I have been working with the product for more than five years. 

The product's stability is great because of Google's servers.

The solution is scalable and we can scale up to petabytes of data. My company has more than 100 users for the product. 

We seek support whenever there are quota issues. 

The product's setup is straightforward. The solution's setup does not take more than 30 minutes to complete. We need to create datasets and within the datasets, we need to create tables. 

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. 

I would rate the solution a nine out of ten. SQL knowledge is required to work on the query.

Our primary use case is for data processing and searching the data. It is basically a data warehouse. We use BigQuery to process and store the data and gather the data from BigQuery to build machine learning models.

When integrating their system into the cloud-based solutions, we were able to increase their efficiency and overall productivity twice compared with their on-premises option.

The data warehouse has all the features that are contained in the data warehouse solution.

The initial setup could be improved making it easier to deploy.

I have been using BigQuery for the past three years now.

The stability ranks around a seven or an eight on a scale of one to ten.

On a scale of one to ten, the scalability is around an eight.

When it comes to customer support I have found some really good documentation online.

When comparing with Azure in the past the difference was the price was cheaper. Google and Azure were offering the same features.

The initial setup is somewhere in the middle between straightforward and complex. You do need some experience or initial skills when setting it up.

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

I would rate BigQuery a nine out of ten on the overall scale.

I use it for education and training purpose purposes and not for work. At Yemen Mobile, it is prohibited to use cloud services, and all services are on-premises. 90% of our solutions are from Viacom, like VB and Engineers Assistant. We also have IP solutions like Oracle business suite.

In Yemen, when you try to convince anyone about cloud services, they believe it is unacceptable and prefer to use on-premises services here in Yemen. So our challenge in Yemen is convincing many people to go to cloud services. There are some success stories where the biggest company in Yemen partnered with Microsoft and SAP and moved to cloud. In the near future, many companies will move to cloud, but it will take some time.

I have been using this solution for three months, and it is a cloud-based solution.

It is a stable solution.

I am unsure of the scalability because I need to test it with a bigger project.

I have not used technical support.

The initial setup is straightforward.

The price is acceptable.

I rate this solution an eight out of ten. I recommend this solution because Google is a big company, and BigQuery is a very nice product. It is a good product, scalable, and has high performance.

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 price could be better. Compared to competing solutions, BigQuery is expensive. It's only suitable for enterprise customers, not small and medium-sized businesses, as they cannot afford this kind of solution. 

In the next release, it would be better if they improved their AI bot. Although machine learning and artificial intelligence are doing wonders, there is still a lot of room to enhance them.

I have been working with BigQuery for two and a half years.

BigQuery is a stable solution.

BigQuery is a scalable solution. At present, we have about five different users using this solution. But BigQuery is handling the data of 3,000,000 customers.

We subscribed to technical support from Google. Whenever my team finds an issue, they contact support. I did not get a chance to contact the support team because we never had any difficulties or glitches while configuring it.

The initial setup is relatively straightforward. It's not simple, and it's not very complex. We are doing maintenance of our regular cloud services and working with some assistants and microservice architecture. I don't think we have ever set up in less than one day.

We implemented this solution.

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.

On a scale from one to ten, I would give BigQuery a nine.

Our company uses the solution as a data warehouse for implementing machine learning use cases and queries. 

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

The NoSQL model and feeds for machine learning are based on the support of competent technologies. 

The solution includes plenty of additional features. 

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

I have been using the solution for two years. 

The solution is stable. 

The solution is scalable and we have 200 users with no issues. 

Google has one technical support channel for all products and services. If you place a support ticket, they will respond to you in order of priority. 

There is no setup because the solution resides in the cloud. Once you enable the APIs in the Google Cloud ecosystem, you can start consuming right away. 

The price is a bit high but the technology is worth it. If you do not use the solution in the right way, it will be expensive.

There is not an equivalent competitor product because the solution is Google's proprietary technology. 

If you are interested in a NoSQL option, definitely try the solution. 

I rate the solution a ten out of ten. 

I would say that for our use cases, sticking with BigQuery made sense because we were using Google Analytics Data, which has direct integration with BigQuery.

The most valuable features of this solution, in my opinion, are speed and performance, as well as cost-effectiveness.

I haven't done much research on other competitors. As previously stated, I am unfamiliar with the Azure and AWS counterparts. I understand that Snowflake has made some improvements on its end to further reduce costs, so I believe BigQuery can catch up.

I have heard that BigQuery is being expanded to become more of a Lakehouse to support unstructured data, and I am looking forward to that.

I have been working with BigQuery for one year.

BigQuery is very stable.

I have not had any contact with technical support.

The major cloud providers' cloud solutions. We are using BigQuery for the Data Warehouse. And much of the stack we're working with is open source. If you've heard of the modern data stack, we've used a lot of them, such as DBT for data transformation and so on, which isn't really commercial, off-the-shelf software.

BigQuery is inexpensive.

We had previously evaluated products such as Informatica MDM, Microsoft MDS, Informatica Cloud Test Data Manager, IBM InfoSphere MDM, and Tipco XBX; however, I was unable to secure management funding to implement these solutions. I left the company I was working for at the time, and yes, I stopped focusing on MDM. I just focused on my bread and butter, which is BI.

The company is an enterprise customer of Google.

I would rate BigQuery an eight out of ten.

We use BigQuery for data warehousing purposes.

The product’s most valuable feature is its ability to manage the database on the cloud.

The product’s performance could be much faster.

We have been using BigQuery for four years.

I rate BigQuery’s stability a ten out of ten.

We have two to three BigQuery users in our company. I rate its scalability an eight out of ten.

We have used many databases such as Oracle, MySQL, MongoDB, etc. We switched to BigQuery for better database management. Using it, we only need to focus on data ingestion and generating query output.

The initial setup is easy.

We implemented the product in-house.

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

I recommend BigQuery to others and rate it a nine out of ten.

We use BigQuery for analytics and reporting.

The most valuable feature of BigQuery is its capability to integrate. The product fits pretty well within our ecosystem. The solution's reporting, dashboard, and out-of-the-box capabilities match exactly our requirements.

As a product, BigQuery still requires a lot of maturity to accommodate other use cases and to be widely acceptable across other organizations. It's not as old as other applications like Tableau or Power BI, but as long as it's supported by Google, I think it will continue to progress.

I have been working with BigQuery for about two years.

BigQuery's stability is good. I rate BigQuery a nine out of ten for stability.

We have tested and found that BigQuery's scalability is good. I rate BigQuery a seven to eight out of ten for scalability.

BigQuery's initial was simple because it's provided over the cloud.

BigQuery is suitable for all sorts of business types. Medium and small businesses will find the solution's out-of-the-box use cases more useful.

Overall, I rate BigQuery an eight out of ten.