BigQuery Reviews

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.

My company uses BigQuery as a data warehouse.

What I like most about BigQuery is that it's fast and flexible.

Another advantage of BigQuery is that it's easy to learn.

You can also use it from anywhere.

An area for improvement in BigQuery is its UI because it's not working very well.

Pricing for the solution is also very high.

In general, though, I like the solution very much.

I've been using BigQuery for six months now.

I found BigQuery stable in my six months of using it, and I'd rate its stability as ten out of ten.

BigQuery is a scalable solution, and it's a nine out of ten in terms of scalability.

I've never interacted with BigQuery support.

We used Redshift as a database for our operations, but now, we've moved to BigQuery because BigQuery is much more than a database. It has more features than Redshift, and we hope to pay less than what we paid when we were using Redshift because Redshift required us to pay ahead each month, and the total cost was too much.

BigQuery was easy to set up, but you'll need to learn how to do it. In general, the initial setup is straightforward.

I'd rate the BigQuery setup as eight out of ten.

Our in-house team implemented BigQuery for the company.

BigQuery pricing can increase quickly. It's a high-priced solution.

It would help if you researched how to reduce the price. It would take some time to find out how to set up BigQuery in a way that reduces its pricing.

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.

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.

We use BigQuery at our organization to access daily transactional data from our POS solutions, which are used to sell products to our clients. We gather the most essential information for our clients and upload it to our data lake using BigQuery.

We gather the most essential information for our clients and upload it to our data lake using BigQuery. At the end of the month, we have sufficient information in our data lake to generate legal reports, balances, and reconciliations with partners.

The most valuable aspect of BigQuery is its ability to handle high data workloads without causing friction with our online systems. We can obtain significant amounts of data, which is critical, even if it's not in real-time. 

Additionally, we can solve small issues while working with the platform, and it's rare that we need external support.

Sometimes, support specialists might not have enough experience or business understanding, which can be an issue. They might have basic knowledge but lack specific insights related to the specific configuration or context required by the client.

Google's customer service is good but not the best. They receive a score of eight out of ten.

Positive

Setting up BigQuery is not difficult. Although I do not directly handle this aspect, my team appears comfortable with it and does not encounter major issues requiring outside assistance.

I rate BigQuery nine out of ten. I recommend it to others and have used it in various situations over the years.

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. 

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.