BigQuery Reviews

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.

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.

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.

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.