I do not use AWS as my main cloud provider within the company and am not currently using it with AWS. My company did not buy it through the AWS Marketplace as I'm the founder of the company. I do not use BigQuery's integration with Google Analytics for custom behavior analysis. I have not utilized the geospatial analysis capabilities with BigQuery as I'm doing mostly human capital work, so geospatial wouldn't make sense. I would rate their service on a scale of one to 10 for Google as ten. I would rate BigQuery as nine out of 10. My experience with the pricing for BigQuery was through a grant from Google. Their pricing is fairly priced and pretty comparable to Microsoft's offering. I haven't had direct experience, but I did look at the pricing of the Microsoft offering and it's pretty similar. I do not deal with Google AI tools such as Vertex.
I rate BigQuery as eight out of ten due to issues with pricing, the dashboard, and time-consuming aspects. Despite these areas for improvement, my overall experience remains satisfactory.
I recommend BigQuery, especially if you're already using GCP products, as the integration with other Google services is seamless. I'd rate the solution nine out of ten.
I would recommend BigQuery to other users because it will be helpful for AI and analytics related things. The updated technologies we use and analytical activities we perform should be updated. Overall, I rate the solution a nine out of ten.
Senior Manager.Marketing Strategy & Analysis. at a computer software company with 10,001+ employees
Reseller
Top 20
Aug 19, 2024
While BigQuery offers powerful capabilities, managing costs effectively and considering the investment required to use the platform at scale is crucial. Additionally, investing in training or consulting services may be necessary to maximize the solution's benefits. I rate it a ten out of ten.
Setting up BigQuery on GCP is crucial. When creating a service account, you define the permissions required for project identification or access monitoring systems. You configure policies using IAM roles to manage access permissions effectively within GCP. These roles govern the service accounts created for specific tasks such as data processing, system monitoring, or other service integrations. When you activate these policies, a JSON token is generated. This token can authenticate and authorize access to Google services like BigQuery or other third-party applications. Moreover, by configuring VMs to match data processing requirements, you ensure that the data is securely handled by the applications associated with the service accounts. This setup enables seamless communication between your applications and Google services, facilitating efficient data acquisition and processing. Overall, I rate the solution a seven out of ten.
I recommend BigQuery. It offers low costs and is quite easy to use, compared to other options like AWS or Azure. If you're already working within the Google Cloud ecosystem, it's a perfect match. However, if you're primarily focused on data warehousing and need something more accessible, Snowflake might be a better option. The integration with other tools has greatly enhanced our data visualization capabilities. It's tightly integrated across various components. The serverless architecture has been immensely beneficial for our projects. We no longer have to concern ourselves with infrastructure management or maintenance, as everything is automated. It makes our team smaller and alleviates worries about infrastructure or downtime. For a beginner learning to use it for the first time, it's relatively straightforward. I rate it an eight out of ten.
Associate Consultant (Data Engineer) at MediaAgility
Real User
Dec 19, 2023
Potential users can trust BigQuery without any second thoughts. The solution's pricing is great compared to other solutions. The solution provides more flexibility and supports standard SQL, and anyone coming out from a different platform would not face any challenges adopting BigQuery. Overall, I rate BigQuery a nine out of ten.
Sr Data Architect at a comms service provider with 10,001+ employees
Real User
Top 20
Dec 6, 2023
Whether for small, medium, or large enterprises, it is a recommendable choice. Its pricing model makes it accessible and manageable based on your usage. Given that many individuals and businesses already have Gmail accounts and utilize Google Cloud workspaces, incorporating BigQuery into operations is seamless. Moreover, a complimentary reporting tool, Looker Studio, is available for free, enhancing the reporting capabilities on BigQuery or via Google Sheets. Overall, I would rate it eight out of ten.
Data Engineering and AI Intern at .3Lines Venture Capital
Real User
Dec 5, 2023
If you have a big database and lots of analytics, BigQuery is a really good tool. It helps save and manage your queries and gives you results you can show clients and others. I rate it a nine out of ten.
Data Engineer at a recreational facilities/services company with 10,001+ employees
Real User
Nov 2, 2023
In terms of the data warehousing, and data analytical platform, BigQuery is one of the products in the Google Cloud platform. So, I would rate it a nine out of ten in terms of data warehousing.
Senior Managing Consultant at Abacus Cambridge Partners
Real User
Sep 26, 2023
My advice would be to first understand your client's weak points, the challenges they face, their ambitions, vision, and data-related dreams. It's crucial to identify their desired analytical capabilities for informed decision-making within their organization. Once these critical aspects are on the table, the choice between BigQuery or any other data warehouse and analytical platform can be made. Through this approach, clients will gradually build their understanding of how BigQuery can serve as a database house and analytical platform within their architecture. It empowers them to efficiently store, analyze, and query large datasets, making it an ideal choice for organizations dealing with substantial data volumes and the need for rapid, data-driven decision-making. I would rate it nine out of ten.
Senior Cyber Security Architect Global ICT at a construction company with 10,001+ employees
Real User
Aug 16, 2023
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.
I would tell those planning to use the solution to just go out and utilize as much information as possible. There's a ton of great information on the platform and how it can be best utilized. The solution doesn't necessarily require maintenance. It's a great platform. It's pretty easy to use. You do have to have some skill and uptake when it comes to actually writing SQL and writing queries. But then it does need better support capabilities. But aside from that, it's a pretty good platform. I rate the solution a seven out of ten.
Vice President - Data Engineering and Analytics at a financial services firm with 10,001+ employees
Real User
Feb 21, 2023
BigQuery is a tool wherein it can support your structured, unstructured, secured, and unsecured data, and it can support the server if you use any right-level services from BigQuery. However, data encryption and integration could be difficult if you want to transfer data to another cloud. For example, when I have data from the other cloud, it would be difficult to bring that data into the data systems for me. Even if I consider doing it, it will cost me and might be expensive. When you try to import data from one vendor to another, it also results in additional data transfer costs and data integration issues. If you keep the solution in the same platform and the same data fabric level, then the data from that level get joined and maintained locally to that cloud. And if you're sending some data across the cloud, only use the basics to connect the data. That way it'll detect the fabric. So if you go with the native tool, that is the limitation we'll have. Cloud diagnostics does get you out of it. When it comes to BigQuery, it is deployed in one cloud. It is native to Google and can only stay on Google; that is the only drawback. Overall, I would rate it a seven out of ten.
My company is using a data warehouse solution called BigQuery. My advice to anyone deciding on using BigQuery is to be aware of the pricing mechanism and have a better understanding of it to avoid surprises. You pay for what you use, so it could be very easy to lose control, which means the BigQuery costs could go up fast. I'd rate BigQuery as nine out of ten.
Team Lead Data & Analytics at a hospitality company with 501-1,000 employees
Real User
Nov 24, 2022
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.
BigQuery takes a different approach to design and this is important to consider. BigQuery on its own is not enough and you need other tools also offered by Google to transform data. The BigQuery ecosystem is a little more complex than the Snowflake ecosystem. Those who have traditionally worked on on-premise data warehouses, find Snowflake much easier to set up. Those who are trying to establish warehouses for the first time, find Google easier. I would rate this solution a seven out of ten.
BigQuery is an enterprise data warehouse that solves this problem by enabling super-fast SQL queries using the processing power of Google's infrastructure. ... You can control access to both the project and your data based on your business needs, such as giving others the ability to view or query your data.
I do not use AWS as my main cloud provider within the company and am not currently using it with AWS. My company did not buy it through the AWS Marketplace as I'm the founder of the company. I do not use BigQuery's integration with Google Analytics for custom behavior analysis. I have not utilized the geospatial analysis capabilities with BigQuery as I'm doing mostly human capital work, so geospatial wouldn't make sense. I would rate their service on a scale of one to 10 for Google as ten. I would rate BigQuery as nine out of 10. My experience with the pricing for BigQuery was through a grant from Google. Their pricing is fairly priced and pretty comparable to Microsoft's offering. I haven't had direct experience, but I did look at the pricing of the Microsoft offering and it's pretty similar. I do not deal with Google AI tools such as Vertex.
I would rate BigQuery an eight out of 10.
I would say the overall rating is nine out of ten.
I rate BigQuery as eight out of ten due to issues with pricing, the dashboard, and time-consuming aspects. Despite these areas for improvement, my overall experience remains satisfactory.
I rate BigQuery nine out of ten. I recommend it to others and have used it in various situations over the years.
I would recommend BigQuery to others. However, making it more affordable would be appreciated. I'd rate the solution eight out of ten.
I recommend BigQuery, especially if you're already using GCP products, as the integration with other Google services is seamless. I'd rate the solution nine out of ten.
I would recommend BigQuery to other users because it will be helpful for AI and analytics related things. The updated technologies we use and analytical activities we perform should be updated. Overall, I rate the solution a nine out of ten.
While BigQuery offers powerful capabilities, managing costs effectively and considering the investment required to use the platform at scale is crucial. Additionally, investing in training or consulting services may be necessary to maximize the solution's benefits. I rate it a ten out of ten.
Setting up BigQuery on GCP is crucial. When creating a service account, you define the permissions required for project identification or access monitoring systems. You configure policies using IAM roles to manage access permissions effectively within GCP. These roles govern the service accounts created for specific tasks such as data processing, system monitoring, or other service integrations. When you activate these policies, a JSON token is generated. This token can authenticate and authorize access to Google services like BigQuery or other third-party applications. Moreover, by configuring VMs to match data processing requirements, you ensure that the data is securely handled by the applications associated with the service accounts. This setup enables seamless communication between your applications and Google services, facilitating efficient data acquisition and processing. Overall, I rate the solution a seven out of ten.
I recommend BigQuery. It offers low costs and is quite easy to use, compared to other options like AWS or Azure. If you're already working within the Google Cloud ecosystem, it's a perfect match. However, if you're primarily focused on data warehousing and need something more accessible, Snowflake might be a better option. The integration with other tools has greatly enhanced our data visualization capabilities. It's tightly integrated across various components. The serverless architecture has been immensely beneficial for our projects. We no longer have to concern ourselves with infrastructure management or maintenance, as everything is automated. It makes our team smaller and alleviates worries about infrastructure or downtime. For a beginner learning to use it for the first time, it's relatively straightforward. I rate it an eight out of ten.
Potential users can trust BigQuery without any second thoughts. The solution's pricing is great compared to other solutions. The solution provides more flexibility and supports standard SQL, and anyone coming out from a different platform would not face any challenges adopting BigQuery. Overall, I rate BigQuery a nine out of ten.
Whether for small, medium, or large enterprises, it is a recommendable choice. Its pricing model makes it accessible and manageable based on your usage. Given that many individuals and businesses already have Gmail accounts and utilize Google Cloud workspaces, incorporating BigQuery into operations is seamless. Moreover, a complimentary reporting tool, Looker Studio, is available for free, enhancing the reporting capabilities on BigQuery or via Google Sheets. Overall, I would rate it eight out of ten.
If you have a big database and lots of analytics, BigQuery is a really good tool. It helps save and manage your queries and gives you results you can show clients and others. I rate it a nine out of ten.
Overall, I would rate it eight out of ten.
In terms of the data warehousing, and data analytical platform, BigQuery is one of the products in the Google Cloud platform. So, I would rate it a nine out of ten in terms of data warehousing.
My advice would be to first understand your client's weak points, the challenges they face, their ambitions, vision, and data-related dreams. It's crucial to identify their desired analytical capabilities for informed decision-making within their organization. Once these critical aspects are on the table, the choice between BigQuery or any other data warehouse and analytical platform can be made. Through this approach, clients will gradually build their understanding of how BigQuery can serve as a database house and analytical platform within their architecture. It empowers them to efficiently store, analyze, and query large datasets, making it an ideal choice for organizations dealing with substantial data volumes and the need for rapid, data-driven decision-making. I would rate it nine out of ten.
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.
I would tell those planning to use the solution to just go out and utilize as much information as possible. There's a ton of great information on the platform and how it can be best utilized. The solution doesn't necessarily require maintenance. It's a great platform. It's pretty easy to use. You do have to have some skill and uptake when it comes to actually writing SQL and writing queries. But then it does need better support capabilities. But aside from that, it's a pretty good platform. I rate the solution a seven out of ten.
I rate BigQuery seven out of 10.
BigQuery is a tool wherein it can support your structured, unstructured, secured, and unsecured data, and it can support the server if you use any right-level services from BigQuery. However, data encryption and integration could be difficult if you want to transfer data to another cloud. For example, when I have data from the other cloud, it would be difficult to bring that data into the data systems for me. Even if I consider doing it, it will cost me and might be expensive. When you try to import data from one vendor to another, it also results in additional data transfer costs and data integration issues. If you keep the solution in the same platform and the same data fabric level, then the data from that level get joined and maintained locally to that cloud. And if you're sending some data across the cloud, only use the basics to connect the data. That way it'll detect the fabric. So if you go with the native tool, that is the limitation we'll have. Cloud diagnostics does get you out of it. When it comes to BigQuery, it is deployed in one cloud. It is native to Google and can only stay on Google; that is the only drawback. Overall, I would rate it a seven out of ten.
My company is using a data warehouse solution called BigQuery. My advice to anyone deciding on using BigQuery is to be aware of the pricing mechanism and have a better understanding of it to avoid surprises. You pay for what you use, so it could be very easy to lose control, which means the BigQuery costs could go up fast. I'd rate BigQuery as nine out of ten.
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.
If you are interested in a NoSQL option, definitely try the solution. I rate the solution a ten out of ten.
On a scale from one to ten, I would give BigQuery a nine.
I'd rate the solution seven out of ten. It's a pretty good product overall.
BigQuery takes a different approach to design and this is important to consider. BigQuery on its own is not enough and you need other tools also offered by Google to transform data. The BigQuery ecosystem is a little more complex than the Snowflake ecosystem. Those who have traditionally worked on on-premise data warehouses, find Snowflake much easier to set up. Those who are trying to establish warehouses for the first time, find Google easier. I would rate this solution a seven out of ten.
On a scale from one to ten, I would give BigQuery an eight.