BigQuery Reviews

We basically used it to store server data and generate reports for enterprise architects. It was a valuable tool for our enterprise design architect.

Everything they advertised or listed worked exactly as promised. That was advantageous to us. 

In future releases, I would like to see more pre-defined aggregated forms. After using BigQuery, we need to use the data in an enterprise architecture dimensional data model. So, having pre-defined aggregated forms would be helpful. 

Additionally, I would like to see version-based implementation and a fallback arrangement for data stored in BigQuery storage. These are some features I'm interested in.

I have experience with BigQuery. 

When I joined the company, BigQuery was already implemented by our team.

It is a cheap solution. 

I would recommend getting a clear understanding of BigQuery's functionalities and what it's best suited for. If your needs align with its capabilities, then you should definitely proceed. 

BigQuery offers fantastic features, but it's important to understand its purpose beforehand. Otherwise, you might face difficulties later on.

Overall, I would rate the solution an eight out of ten.

We have a cloud solution that runs in a centralized mode for a few hundred senior managers who require diverse reports, ranging from daily operational details to more substantial analyses, such as sales trends, movie ticket sales clustering, and reporting.

The flexibility of its serverless architecture is advantageous in handling the variable nature of our workloads. Instead of relying on a fixed database cluster with constant costs, it allows you to pay for the resources you consume during peak times. This on-demand pricing model appears to be more cost-effective, particularly when dealing with occasional heavy queries that involve analyzing billions of data points, such as ticket sales for millions of movies. The ability to scale internally using Kubernetes adds another layer of flexibility to our setup, allowing us to adapt to varying demands efficiently. Its fast response times during peak usage make it a suitable choice for our dynamic and variable data processing needs. I appreciate its impressive optimization and automation features, observed during small-scale tests. It stands out in efficiently handling internal actions without the need for manual intervention in tasks like building cubes and defining final dimensions.

The primary hurdle in this migration lies in the initial phase of moving substantial volumes of data to cloud-based platforms. This becomes even more pronounced when dealing with terabytes of data. Uploading data to cloud services requires careful consideration and optimization to ensure a smooth and efficient migration, especially when dealing with large datasets.

I started using it recently.

It inherently manages scalability with its auto-scaling capabilities. The ability to dynamically adjust resources based on demand is a key factor in optimizing performance and ensuring that our system can handle varying workloads efficiently. We operate as a small company with a modest business scale, handling a few medium-sized projects each year.

The current bottleneck in our migration process primarily revolves around bandwidth issues, especially during the initial data ingestion phase.

The deployment process itself is straightforward and not a source of concern. The real challenge lies in the bandwidth limitations and the time-consuming nature of data uploading. While a comprehensive evaluation is still pending, it's anticipated that the data upload alone might take up to a week or more.

The pricing appears to be competitive for the intended usage scenarios we have in mind.

In my evaluation of alternative solutions, I'm exploring Hydra, a columnar version of Postgres with partitioning capabilities. While I'm still learning about its features and performance, it seems promising. Additionally, I'm considering ClickHouse, which has shown exceptional benchmark results. I've completed an initial installation to assess its functionality.

Overall, I would rate it eight 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.

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. 

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.

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.