BigQuery Reviews

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.

We are into conversational commerce platforms. All the conversations and the chat history are captured when we chat with a chatbot. Our application is built on NoSQL. We put the data into BigQuery as a data warehouse, where we refine the data. We analyze the chat history and give analytic reports to our merchants using our SaaS platform. It is to understand the chat conversation, how many people had a conversation, and what key buttons they clicked.

We also provide analytics on how many orders were completed. We are building a commerce and conversational dashboard for our enterprise customers and offering them on Looker. Looker was earlier known as Google Data Studio. For applications, we segment customers and use the customer segments to broadcast messages across social channels. All these things are being queried over BigQuery to do segmentations.

On the front end, we give them the option of segmenting based on different data attributes. Then, it goes to BigQuery to filter out the data and find the number of customers who meet the defined conditions. Based on that, we send the messages to the segmented customers. We are doing multiple things related to conversation commerce using BigQuery.

It is a cloud platform. We just need to query and get the output. Anyone can use the product. Even non-coders can review the data in BigQuery.

There should be an easier way to migrate from NoSQL to SQL. The process of migrating from Datastore to BigQuery should be improved. We use Datastore and BigQuery. If both products can be synced well, it will improve employee productivity. 

We had to write a lot of pipelines and logic for real-time streaming from Datastore, which is a NoSQL, to BigQuery, which is more of a structured database. However, because both products are internal to the Google Cloud Platform, they should have some provision to create and keep syncing it automatically. It will be an advantage for the customers. Currently, we build replicas. It would be easier if some simple connection replicates the changes in BigQuery.

My company has been using the solution for five years. I have been using it for a year.

I rate the product’s stability a nine out of ten.

The solution is more scalable because it is in the cloud. It is an advantage. I rate the scalability of the tool an eight out of ten. If we are integrating it with two different platforms, then it becomes a little difficult for us. If there is a data pipeline error, we cannot scale immediately. If we have to integrate NoSQL with BigQuery, it sometimes becomes a challenge for real-time streaming.

Five developers within my team are building all the logic on BigQuery. We have around 100 to 200 customers with five to six employees each using our platform. When they use our platform and query using different features, these queries hit BigQuery, and we render the data. We are the designers designing using BigQuery, and the end users use the UI.

I would rate technical support a little less. We have always struggled to get quicker support.

The initial setup is very simple. The solution is cloud-based.

The tool has competitive pricing. I rate the pricing an eight out of ten.

I have a technical team that works deeply into it and gives me the output. I don't extensively use BigQuery as a developer to develop things. Overall, I rate the solution an eight out of ten.

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.

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 as a data source.

We mainly use it to do some transformations. Once we collect query data from it, we use other services to do model training or predictions. We don't really utilize all the features provided by BigQuery. We mainly use some basic data transformation options. It also provides some machine learning models.

In many functions, it's very similar to Spark Kubernetes. The cluster is good. It'll provide computation capabilities. 

The setup is simple.

It is stable. The performance is good. 

It is a scalable solution. 

We do not find the solution that expensive. 

Machine learning could be improved. There are some machine learning models in BigQuery; however, maybe more libraries can be provided. We'd like it extended into the Spark ML library. 

I noticed recently it's more expensive now. I didn't compare them to others, however, and in our team, we don't consider the price of it much.

I've been using the solution for several months.

It is stable and reliable. There are no bugs or glitches. 

I'd rate the overall stability an eight out of ten. It offers a good level of performance. There are billions of accounts. 

It's scalable. We don't need to worry about scalability issues in our case. For us, it's good enough.

We have millions of customers and thousands of products. 

I've never dealt with technical support. I can't speak to how helpful or responsive they are. We have a bigger team and tend to learn from each other.

I also use Spark, which has similar functions. I've also used Databricks. 

I've used BigQuery for a longer time, however, Databricks is easier when it comes to the setup of a complete solution. With BigQuery, we need to develop an intranet solution and set up services and then put them together.

It is my understanding that the initial setup is very straightforward and simple. 

The pricing is fine. 

I'd rate the solution seven out of ten. It's a pretty good product overall. 

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.

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.