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.

This is a cloud-based data warehouse. 

The product is updated automatically without people having to worry about doing anything. It is managed completely by Google. 

The performance is good. It's very user-friendly for people not coming from the technical area. 

It has a very friendly user interface and a console for command line. 

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

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. When people are working on long queries, and so forth, they have to save them. It is a little bit clunky. The interface for saving them and version control is not really doable. We have to support the queries manually.

I've used the solution across different companies. I've used it for about six or seven years. 

In my previous company, we were not spending that much. You give more money away to the other tools from GCP. We paid maybe €200 or something like that and no more than that. This year, we pay €170 a month.

We are an end-user.

The product is a software as a service, and therefore, we are always on the latest version. They do everything for us. 

I'd rate the product eight out of ten as it's a very good data warehouse, and it's very easy to learn how to use it. It's very user-friendly. I can have my team handle it, even if they are non-technical and they can be doing a lot of coding there without problems. 

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. 

BigQuery is a PaaS solution. There's only one version available on Google Cloud. Because it's deployed on cloud, it will update automatically.

If I'm collaborating with Google Data Cloud, I can use the cache, and I don't have to pay again and again. There are some performance features like partitioning, which you can do based on an integer, and it improves the performance a lot. There's also the Array function. You can also enable Spark on BigQuery, which is actually faster than any other Spark. If you use Dataproc, Spark on BigQuery is much faster.

Spark will actually eliminate the usage of a lot of Adobe legacy things. It will act as a Spark SQL.

It is not that cost-friendly, but it is very performance-friendly. There are also machine learning features.

For example, if I have a query, and I have done everything to improve it, the query will still take 15 minutes. 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.

I have been using this solution for two and a half years.

BigQuery is very stable. It is getting used a lot.

It is definitely scalable. You do not have to do any configurations. It will be able to handle petabytes of data.

Technical support is excellent. It is Google, and they always provide the best. We haven't needed to contact Google for BigQuery specifically, but I have contacted Google support for other things and they were pretty responsive.

I have experience with Snowflake.

I was working on a project where we were building systems and loading the data manually. Once we moved to BigQuery, we saw ROI in terms of cost savings. We saw 27% performance improvement in most of our queries. Our total costs were reduced by about 17%. In terms of cost and time, we were able to save effort.

There was some learning and training involved, which lasted six months, so we saw the real ROI after a year.

I would rate this solution 8 out of 10.

My advice is to first identify your use case. If you have Google Cloud then you have two databases to compare, BigQuery and Snowflake. BigQuery is typically used to analyze petabytes of data. If you're looking for transitional query, then you should have a different system. BigQuery cannot handle unstructured data, so that is one thing you have to think about. 

In terms of latency, if you want single-digit millisecond latency then BigQuery is not good. It is very fast, but if you want single-digit millisecond latency, then you probably have to go to a no-SQL database solution.

My suggestion is to analyze your use case and then map it with the BigQuery features.

We use BigQuery to store data in a table and query it. Data storage can be either an internal native table or an external table where the external source will point to Google Cloud Storage or Google Drive. 

Wherever we can have external storage, we can have a table built pointing to that external storage and query the tables. In BigQuery, we can query the table or even do DML operations, like insert, delete, etc.

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.

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.

I have been using BigQuery for more than three years.

BigQuery is a stable solution.

BigQuery is highly scalable. We can have unlimited storage if we do 20 records, and It's very fast. Even if we scale it to 20 trillion, it will still be fast. 

In my organization, about two in five use BigQuery. When I joined the company a year back, usage was relatively moderate. However, now usage increased because of the on-premise to GCP migration. Because of many successful projects, several people are using BigQuery now.

We have dedicated support people who help us with the framework. If there is a technical issue in BigQuery, we just get help from the technical team. But if there are any engineering issues or some data issues, our team will handle them.

I use Teradata and then Apache Spark on-premises.

The initial setup is relatively straightforward. There are some restrictions, like the project's name. It has to be unique, but once that project is created, we can simply go to an option, query, and the query control will open, and we can start creating a table, loading data, querying, and everything. So that's quite simple and straightforward.

When I joined PayPal, the setup was done in-house. When I worked at another organization, Cognizant, we had Google's help. So a Google specialist helped us set up and everything.

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.

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

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.

We use BigQuery for data warehousing. 

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

I have used BigQuery for about three years.

I rate BigQuery 10 out of 10 for stability. 

I rate BigQuery 10 out of 10 for scalability because it's a fully managed solution.

Setting up BigQuery is easy because it's a managed database.

I rate BigQuery six out of 10 for affordability. It could be cheaper. 

We compared BigQuery to Oracle. In my opinion, BigQuery is better because it's fully managed and less expensive. 

I rate BigQuery seven out of 10.