BigQuery Reviews

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. 

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 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. 

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.