BigQuery Reviews

Our company uses the solution as a data warehouse. We have ten to twenty users who consume the solution from reports. 

The feature called calibrating the capacity is valuable.

The solution is easy to use and has quite good performance.

We would like to be able to calibrate the solution to run on top of a raw file. Currently, we have to move raw files from Google storage to the solution and load them for transformation. We shouldn't need to move data first to get an analysis.

I have been using the solution for five years. 

The solution is stable so I rate stability a nine out of ten. 

We have experienced a few glitches in our company only. When we run queries, they take a few to five minutes when they should only take one minute. There is a problem with the services in Indonesia. 

The solution is scalable and has quite good performance. You scale at the same time you execute a user's role and can easily get one to ten million pro. 

I rate scalability a nine out of ten. 

Technical support was quite responsive and handled our issue. 

I rate support an eight out of ten. 

Positive

The setup is quite simple so I rate it a nine out of ten. 

The solution is pretty affordable and quite cheap in comparison to PDP or Cloudera. 

The solution could be less expensive. You have to be careful how you design, query, or partition because it could cost you a lot of money.

I rate pricing an eight out of ten. 

When we decided to move to the cloud, we compared the solution to KWS. We found that the performance of Google Cloud and the solution were better than KWS. The setup and configuration were also simpler.

I rate the solution an eight out of ten. 

We primarily use the solution for data analytics. 

I enjoy the scalability of the solution. Its scalability is very impressive.

It's straightforward to set up.

The solution has been stable.

We'd like to have more integrations with other technologies. We'd like something like CrossCloud - something that can be on AWS and Azure and can be easily integrated.

It would be great if they added data anonymization to their list of features. We'd like to see data compliance and masking so we can enforce things region by region.

I've been using the solution since around 2019.

I haven't seen any tickets relating to trouble with scalability. It seems to be reliable. There are no bugs or glitches. It doesn't crash or freeze. 

The scalability is excellent. It can handle large datasets and scale up pretty easily as the data volume grows. It expands very easily.

We have 80 to 100 people using the solution right now. It's used on a daily basis. 

I haven't used technical support just yet. I haven't come across any problems which would require me to reach out. 

I've used Data Warehouse in the past and am familiar with Teradata and Snowflake.

If I have to compare BigQuery with Teradata in terms of performance, capabilities, ease of use, and integrations, BigQuery scales up better. However, in terms of licensing and paper use, Teradata is quite good.

If we compare it with other things like Snowflake, Snowflake has its own unique architectural advantages. However, I haven't seen Snowflake over on Google Cloud. I have seen Snowflake over on AWS and Azure. The architecture of Snowflake has its own unique advantages and is largely on other clouds.

The initial setup is very simple and straightforward. I'd rate the ease of implementation a four out of five.

We find the pricing reasonable enough for our use cases. However, it's too early to comment on if it will be good in the long run. We have to properly plan data around different tiers, including which to archive where so that we use it in a more optimized fashion. We will need to properly plan everything and we haven't really done that yet.

I'd rate it a four out of five in terms of its competitive pricing. 

I'm an end-user. I'm still new to the company. I'm not sure which version of the solution we're on.

All cloud systems have more or less the same functionality. It's just a matter of choosing one that makes sense for your business.

When it comes to how to leverage analytics, some of the AI and machine learning from Google come ahead of the competition. Other than that, the other analytics options are fairly competitive between Google, AWS, and Microsoft. It's just that,  when it comes to extending the analytics to AI/ML, Google is ahead of the competition there.

I'd recommend the solution to others. 

I would rate it eight out of ten.

We implement for customers. We work as a global company and we have 350,000 employees, we serve clients across all industries. There are many use cases. There is no use case that we would only apply in the context of BigQuery and not with Snowflake, or not with Synapse, et cetera. It is use case agnostic.

It can be for fraud, it can be for marketing analytics, customer 360, or any kind of real-time analytics. You can use it for all sorts of stuff.

It's a stable, reliable solution. It has a good reputation for that. 

The product can scale.

As a cloud solution, it's easy to set up. 

To be very specific, here in the Middle East, I'm based out of the UAE, and Google has a very narrow footprint, a very limited footprint here in the region. There is a lack or absence of local data residency compliance. They don't have a local data center here. Therefore, most of the big organizations like banks, and companies in the highly regulated public sector, are not using BigQuery products as it means that the data will have to move out of the country. We'd like to see more local data residency.

We've been implementing this solution since the inception of these products. We are Platinum Elite partners with most vendors.

The solution has a reputation for being stable. It's not a problem. 

The solution is  scalable up to a certain extent. According to the benchmarks, they would be stronger on the one hand, however, depending on the criteria that you're using, what kind of volumes, the velocity, et cetera, it can scale.

I've never dealt directly with technical support. I can't speak to how helpful or responsive they are. 

I did not handle the initial setup. That said, solutions like BigQuery, as opposed to non-cloud, on-prem versions equivalents are generally more straightforward to set up.

How long it takes to set up depends on the requirements. Typically, it takes six months to one year for end-to-end implementation. 

We have data engineers that can handle deployments. How many are needed depends on the scope of the project. 

I don't deal with licensing aspects of the product. The licenses are always purchased by our clients. 

I'd rate the solution seven out of ten.

We use BigQuery to perform data warehouse migration for clients willing to move to GCP from their on-premise solution.

The solution's pricing is really competitive compared to other peers. The most valuable features of BigQuery is that it supports standard SQL and provides good performance.

I have been using BigQuery for three years.

I rate BigQuery a nine out of ten for stability.

Around 30 to 40 users use BigQuery in our organization.

I rate BigQuery ten out of ten for scalability.

I previously worked with Microsoft SQL Server.

The solution’s initial setup is very easy. You just have to spin up a data set and start using it.

I rate BigQuery ten out of ten for the ease of its initial setup.

The solution can be deployed by one person in a few minutes.

The solution's pricing is cheaper compared to other solutions. On a scale from one to ten, where one is cheap, and ten is expensive, I rate the solution's pricing a two or three 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.

In our workflow, we initiate the process by fetching data, followed by a preprocessing step to refine the data. We establish pipelines for seamless data flow. The ultimate objective is to transfer this processed data into BigQuery tables, enabling other teams, such as analytics or machine learning, to easily interpret and utilize the information for various purposes, whether it's gaining insights or developing models.

The primary advantages include its speed, especially when dealing with large datasets or big data. It proves exceptionally useful in handling substantial amounts of data efficiently. A notable benefit is the ability to preview data without executing full queries, saving time and allowing for quick insights. This feature eliminates the need to run extensive queries solely for data preview purposes, streamlining the overall workflow.

The interface is what I find particularly valuable. When crafting queries, it offers estimations on data usage, providing a helpful indication of resource consumption. This predictive capability adds an extra layer of convenience, making the querying process more insightful and efficient.

It would be beneficial to integrate additional tools, particularly from a business intelligence perspective. For instance, incorporating machine learning capabilities could enable users to automatically generate SQL queries.

I have been working with it for over a year now.

I find it to be generally high and satisfactory. However, there is a notable issue we've encountered regarding query limitations at the organization level.

It is scalable up to a certain point. There seems to be a restriction on the number of queries one can run, for example, being limited to processing ten terabytes of queries. Exceeding this limit results in an inability to run additional queries, posing a potential challenge. Resolving this limitation could contribute to a smoother user experience. Currently, the user base exceeds two hundred individuals.

We used Google Cloud Storage, IAM, AWS (specifically VPC), and instances from both AWS and Google Cloud Platform. Regarding comparison with other solutions, particularly AWS, there are notable observations. AWS, being introduced earlier, appears to have more extensive features compared to Google Cloud Platform (GCP). AWS enjoys the advantage of having a more established history, resulting in robust support from their team. It offers a more comprehensive platform with a broader range of features, and its pricing structure appears to be more favorable.

The challenging part lies in the initial setup of the project, especially when integrating with project management tools. When establishing a project on the Google Cloud Platform, you need to navigate through various resources.

Setting up the account, whether at an individual or organizational level, involves providing necessary information, including credit card details for billing purposes. Once the account is set up, accessing resources like Cloud Storage or BigQuery becomes straightforward within the Google Cloud Platform.

For those venturing into cloud platforms, especially at an individual level, I would recommend considering AWS. Given its longer establishment in the industry, many companies utilize AWS. Additionally, both AWS and GCP offer free tiers for new users, but AWS extends this benefit to one year, while GCP limits it to three months. At the organizational level, AWS tends to provide more extensive features compared to GCP, making it a preferable choice. Overall, I would rate it eight out of ten.

We are primarily using the solution to crunch data. Then, we are doing some ETL work on top of the data. 

We like that it is a serverless system. 

We can analyze terabytes of data in a very small amount of time. 

It's similar to a Hadoop cluster, except it's managed by Google.

The initial setup is simple.

We find the product to be very stable.

It scales quite well.

If they can provide any charting platform on top of this product, that would be ideal. BigQuery now only allows us to run queries. It doesn't provide us with any insights. For example, if a query took so many times, they could maybe provide any suggestions on how to optimize the queries or speed up the process. It would be helpful if they could provide some dashboards where you can easily view charts and information. That would be very useful.

I've been using the solution for two or three years. 

This is a highly stable product. There are no bugs or glitches. It doesn't crash or freeze. 

The solution is very scalable. 

Almost my entire team uses it. We have a 50-member team, and pretty much everyone is on it. They are mostly data engineers and developers. 

We have yet to reach out to technical support. We haven't had any issues. 

We chose this solution specifically since all of our services are in GCP, Google Cloud. Google Cloud has a basic internal coupling with BigQuery. That's the reason we are using BigQuery.

The initial setup is very easy. You just have to log in to the Google Cloud console, and then you can just create a few tables and start using it. 

From start to finish it takes about half an hour. It is even less than that to get the tables up and running. The deployment is quite fast.

I'm not sure about the exact cost, however, it is charged on the queries which you run, basically. For example, if you run a query, the amount of data scanned through BigQuery will dictate the costs. 

I am a customer and end-user.

I'm not sure which version of the solution we're using. 

It's a serverless platform deployed on a public cloud. 

I'd advise potential users to set up their tables accordingly. There are two sets of optimization that BigQuery provides as well. You set up whichever columns you want to do the partition and on which columns you want to do the clustering. If these columns are defined properly, then BigQuery's a breeze to use.

On a scale from one to ten, I would rate it at an eight. If they just added a few more features, it would be almost perfect.

BigQuery is a powerful tool for managing and analyzing large datasets. The versatility of BigQuery extends to its compatibility with external data visualization tools like Power BI and Tableau. This means you not only get query results but can also seamlessly integrate and visualize your data for better insights.

The product's most valuable feature is its ability to connect to visualization tools. 

Some of the queries are complex and difficult to understand. 

I have been using the product for more than a year. 

My company has 100 users for BigQuery. 

The tool's support is fast to respond. 

Positive

The tool's deployment is easy if you follow Google's documentation. 

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. 

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.