BigQuery Reviews

My main use cases for BigQuery are primarily research work to do things like PESTEL analysis.

With the machine learning aspect of BigQuery, it has helped my machine learning process with other tools as I'm using this more to store data and then stacking on other tools for data analysis. It's not as much of a machine learning function as it is using it to store the data and then to query the data.

I track various metrics to measure the success of analytics using BigQuery. The work I'm doing is not necessarily real-time pertinent. I'm doing a study of essentially the combination of different segments, such as current students and students who've graduated in the last four years. We did a big longitudinal study combining data from surveys with corporate leaders, data gathered from interviews with government leaders, policy leaders, state, local, and federal on the education side, and then a huge study with higher education. We're essentially analyzing and assessing the gap between higher education production, meaning the graduates they're producing, and the needs of the corporate world.

The PESTEL analysis involves chunking in data from the Bureau of Labor and Statistics, different groups within labor, and state labor entities. We're layering in information on the economy by pulling different metrics over the last 10 years. We're also bringing in additional synthetic data created based on predictive analytics, along with data from higher ed surveys conducted with 500 different institutions. Real-time analysis wouldn't make sense because everything is either rear-looking data or data synthesized using previous data.

The best features of BigQuery for me are the fact that it's low-code, no-code. You don't have to be a data scientist to really utilize the tool.

The support for SQL queries in BigQuery has impacted my data analysis through Google's combination tools. I haven't written complex SQL queries in 15 years, but using the ability to use natural language to create queries, as long as you're familiar with the ERD, is pretty beneficial.

There are areas that could be improved with BigQuery, such as more bolt-on capabilities and the ability to use more bolt-ons for APIs. Having more of a library of connectors would be really beneficial.

I have dealt with BigQuery for probably three years now.

I do not deal with Google support for BigQuery as I don't have to deal with their support.

I would rate their customer service pretty good on a scale of one to 10, as they gave me access to the platform on a grant.

Positive

I did not deal with Appian in the last 12 months within this year. I do not have experience with other RPA solutions as I moved out of the digital transformation sector.

I do not deal with Microsoft tools Azure, AWS products, or Google products as I moved over into organizational transformation.

I predominantly deal with AI and BI tools and am not dealing with IT solutions, management tools, or integration tools.

I have experience with ChatGPT, Gemini, and other similar tools.

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.

We are mostly dealing with Google solutions such as BigQuery, NoSQL, SQL analytical database, secrets manager, and most of the serverless infrastructure as well, databases.

I run SQL queries on BigQuery, and that runs fine.

We do not do as much data analysis; we do data processing, and I do not think I have any immediate problems.

The features I have found most valuable in BigQuery include the query cache, which is good and scales well. We have not seen problems with scaling. It handles huge amounts of data quite well, and the multi-regions feature works well.

To measure the effectiveness of BigQuery, I think it is quite good.

The metrics I use to evaluate BigQuery's effectiveness include that scaling is quite good and handling of big amounts of data. We do not see any glitches, and we do not see major timeouts. Google platform in general has been quite stable, and we do not see that many outages at resource level or at overall zonal level.

With what I have seen in BigQuery, I had some response times problems, but then it is an analytical database and not a transactional database, so it comes with eventual consistency. I cannot have everything. I am fine with what it is offering.

The only thing that I saw was some response times problems; otherwise, I have been completely satisfied with the solution.

I cannot really think of any additional features I would like to see in the future at the moment, but if I sit down and start thinking deeply, then maybe I will come up with something.

I have been using BigQuery for close to two years.

In the past one and a half years that I have been running with BigQuery, I have not needed to raise any technical support with BigQuery or with Google, but I see that they have various tiers when it comes to support, and we see that P1 issues are answered in an hour, so that is good.

The features I have found most valuable in BigQuery include the query cache, which is good and scales well. We have not seen problems with scaling. It handles huge amounts of data quite well, and the multi-regions feature works well.

In the past one and a half years that I have been running with BigQuery, I have not needed to raise any technical support with BigQuery or with Google, but I see that they have various tiers when it comes to support, and we see that P1 issues are answered in an hour, so that is good.

Positive

BigQuery is the first analytical database that I have used. Before that, it was all SQL transactional databases, so I do not have any benchmarks that I have hands-on experience with.

It was quite easy to learn how to use BigQuery and very easy to configure it.

We have not seen other options. BigQuery is the first analytical database that I have used. Before that, it was all SQL transactional databases, so I do not have any benchmarks that I have hands-on experience with.

I have not used BigQuery ML.

I have not used BigQuery's integration with Google Analytics for customer behavior analysis for that purpose, but I know that there was another company which was using it, and I cannot say much because I have not had that use case.

I have not utilized BigQuery's geospatial analysis capabilities.

There were no issues whatsoever, and it was easily integrated.

I would rate this review an eight out of ten.

The main use cases for this solution are statistical analysis of Google Analytics. I gather Google Analytics data and insert it into BigQuery, and then I do statistical analysis on top of that.

Regarding the room for improvement in BigQuery, it is pretty good as it is. I would try to improve the ease of connecting external data, as integrations would be an area in which I think BigQuery could improve.

The features I find most valuable in this solution are the ability to run and handle large data sets in a very efficient way with multiple types of data, relational as SQL data. It is kind of difficult to explain, but structured data and the ability to handle large data sets are key features.

The data integration capabilities in BigQuery were, in fact, an issue at the beginning. There are two types of integrations. As long as integration is within Google, it is pretty simple. When you start to try to connect external clients to that data, it becomes more complex. It is not related to BigQuery, it is related to Google security model, which is not easy to manage. I would not call it an integration issue of BigQuery, I would call it an integration issue of Google security model.

I have not used BigQuery for AI and machine learning projects myself. I know how to use it, and I can see where it would be useful, but so far, in my projects, I have not included a BigQuery component.

BigQuery is already integrating Gemini AI into the data extraction process directly in order to reduce costs. I think that is a big part of improvement because BigQuery can be very expensive if not used properly. Introducing AI tools that would allow you to optimize the data extraction process is an area of serious improvement.

I have been working with BigQuery for a year and a half, which is eighteen months.

It does require a deployment process if you isolate different environments development, stage, and production. The use cases I have been using do not require those deployment steps since they are non-mission-critical read-only cases, so I cannot comment on that.

I have no complaints about the stability of the tool.

This solution is highly scalable. It is probably the biggest advantage of all is the scalability. It is a 10 out of 10 in terms of scalability.

I have not needed technical support for BigQuery. I have been self-taught and I have been able to handle all my problems alone.

Positive

I have worked with many other database-related solutions before working with BigQuery, but they are all different in the model to BigQuery. I have used Oracle, SQL Server, traditional relational databases, but for these kinds of solutions, the competitors would be Snowflake and others. My experience with Snowflake is marginal and not relevant. I am not able to compare Snowflake with BigQuery. I believe that would be the biggest competitor, that and Databricks. I do not have relevant experience, so I do not know how to compare it with others. I just compare it directly for cost basis, because when I evaluate different tools, I just look at the costs, and from a cost perspective, BigQuery is very competitive to Snowflake and Databricks.

It is very easy to set up BigQuery.

Around 15 users are working with this tool in my company.

These users are different people within the organization that have to look at statistics on Google Analytics. It is a six-level corporation, so six levels. It goes from level five to level three. It is in the middle of the organization.

I believe the cost of BigQuery is competitive versus the alternatives in the market, but it can become expensive if the tool is not used properly. It is on a per-consumption basis, the billing, so it depends on how much data and how you query the data. Being able to optimize the queries to data is critical. Otherwise, you could spend a fortune.

My recommendation to those who are looking into using BigQuery is that they evaluate their data needs and do some proof of concepts before jumping into the solution because I believe that any organization should see how these tools fit into their ecosystem, and if the data requirements are met in terms of cost and performance.

I would rate BigQuery an eight out of 10.

We are migrating all the data to BigQuery from SaaS. We are creating projects and using it for data analysis in the healthcare environment, specifically for Medicare and Medicaid.

The use of SQL is valuable. I am very familiar with the flexibility of the environment. It is not just a cloud environment but also offers a nice interface. BigQuery has a very nice interface that you can easily learn if you know SQL. It is really fast because it can process millions of rows in just a matter of one or two seconds. It is fast.

When I open many of the Google Cloud products, I am in an environment that I do not feel familiar with; it is a little overwhelming. In general, if I know SQL and start playing around, it will start making sense. 

However, the first impression is a little overwhelming, with all the wire pins and options. It is probably not for beginners, yet for people very familiar with SQL. They will figure out what to choose or do since every option probably has a technical definition or a pre-assumed concept. If they improve something, it might be a different product with a more friendly environment, although it is friendly now. Every little thing I do assumes I know what I am doing.

I have used the solution for six months.

The scalability is definitely good because we are migrating to the cloud since the computers on the premises or the big database we need are no longer enough. Google Cloud offers bigger spaces, however, it requires detailed administration of the databases.

I do not receive customer service from Google Cloud, however, we have internal customer support.

Positive

We used Microsoft TSS before. I do not use it now.

The initial setup is simple. It can be complicated if you are migrating from another platform.

I would say the overall rating is nine out of ten.

I use BigQuery for obtaining data. I execute different queries to gather raw data. Additionally, I use BigQuery in conjunction with Google Cloud Platform for managing large volumes of data.

BigQuery processes a substantial amount of data, whether in gigabytes or terabytes, swiftly producing desired data within one or two minutes. Data retrieval occurs efficiently on the dashboard utilizing BigQuery. 

Additionally, BigQuery's SQL querying capabilities are highly regarded and facilitate integration with SQL. Its deployment as a SaaS-based solution through Google Cloud Platform simplifies utilization. As part of GCP, it offers a streamlined experience without the need for installation.

When I execute a query, the dashboard doesn't always present the output seamlessly. Troubleshooting requires opening each pipeline individually, which is time-consuming. Moreover, pricing, the absence of a streamlined dashboard, and the time-intensive nature of some tasks underscore areas for potential improvement.

I have personally been using BigQuery for the past six months. However, the organization has been utilizing it for almost 15 to 20 years.

I rate its stability between eight and nine out of ten. It is a highly stable solution.

I am a premium customer and receive very good support from Google, rating the customer support at ten points out of ten.

Positive

I have not used any solution before BigQuery.

BigQuery is provided as a SaaS-based solution, eliminating the need for any initial setup or installation.

The solution offers approximately 75% cost savings.

The price is perceived as expensive, rated at eight out of ten in terms of costliness. Still, it offers significant cost savings.

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.

Ford has a customer-facing application. We use BigQuery to keep multiple metric records of Ford motors. Ford uses BigQuery to store huge customer data regarding a vehicle's uptime, downtime, velocity, etc. They save this data using BigQuery instead of the normal database.

The solution is very useful nowadays for keeping a huge number of records. It's easy to maintain. Since it has a columnar storage data format, BigQuery is more useful to provide the analytical details. BigQuery can be used to perform some analytical activity over the data.

Nowadays, AI and data analytics projects use a columnar data approach. This will help achieve filtration and analyze data presented in a table format. It is very useful to provide the analytical data. Since it is provided over the cloud, its SLA value is more than 99.9%, and it provides high availability.

Since I used BigQuery over the GCP cloud environment, I'm not sure whether we can go through internal IDEAs like IntelliJ or DBeaver that we use to connect with databases. Instead of connecting directly to BigQuery, we connect to GCP, Cloud Run, and then to BigQuery, which is a long process.

Sometimes, we face some issues, bugs, and defects. We must first connect with a VPN to check data issues while working from home. Then, it allows you to connect to the cloud. After logging into the cloud, it searches for the service we are looking for, and then we go to BigQuery. This is a long process.

After that, we analyze the issues in a table. Instead, it would be very helpful if it could provide a tool that we can install on our MacBook or Windows system. Once we open this tool, we can connect directly to the BigQuery server and easily perform tasks.

I have been using BigQuery for two years.

I rate the solution’s stability a nine out of ten.

Scalability deals with several instances of how we are going to use it. It also considers the number of slots needed to execute the job. BigQuery is very useful for scaling the data. It depends on the users and how frequently data is going to be used with the auto-scaling part.

I did not face any challenges while implementing and configuring BigQuery because I have experience with RDBMS and NoSQL.

There are different databases like BigQuery, Oracle, MySQL, and PostgreSQL. These are just flavors, but the goal is to store the data. We are changing the flavor called BigQuery. It is part of the database, but some of the structure will change, and some analytical activity will be added or improved.

BigQuery is more powerful than the earlier version. Some people use Bigtable, while others use BigQuery. It depends on their use cases. Bigtable is a NoSQL that provides structured data and unstructured data. If we are going for structured data, BigQuery is more powerful and provides analytical activity.

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.

Right now, we are downloading raw Google Analytics four data to BigQuery and then manipulating it. It's mostly used for behavioral data in online datasets.

It has not impacted our operational costs and productivity much; however, it offers a valuable solution for our use case.

BigQuery's querying capabilities are very optimized for large datasets. It generally works faster, making it effective for large data analytics projects.

It would be beneficial if BigQuery could be made more affordable.

I have been familiar with BigQuery for at least four years, probably around six.

I would rate the stability of BigQuery as seven out of ten.

Scalability is easier with BigQuery, and I would rate it as ten out fo ten.

We haven't really needed to use Google's technical support.

Positive

We previously worked with Redshift. We found BigQuery to be the only option for our current use case. Redshift is mostly cheaper, especially if you want to manage your own data warehouse. 

We had some issues with BigQuery, which, when combined with cost, influenced our decision to move back to Redshift as our main data warehouse.

No cloud solution is straightforward. We faced difficulties in configuration, permissions, etc. All cloud providers use their own terminology, and while documented, adapting to their terminology takes time.

We have internal people for maintenance, so we don't pay for additional support.

We are above the free threshold, so we are paying around 40 euros per month for BigQuery. It is generally low cost.

In addition to working with Redshift earlier, we also had integration with Alooma as an ETL solution. However, Alooma doesn't exist anymore.

I would recommend BigQuery to others. However, making it more affordable would be appreciated.

I'd rate the solution eight out of ten.

We use Cloud SQL for our web applications. Previously, we used Microsoft Cloud, but we transitioned due to cost benefits. We find Google Cloud Platform (GCP) to be more cost-effective. For BigQuery, we store data in a message queue similar to Kafka, and when an event occurs, that data is triggered to be inserted into a BigQuery table through subscriptions.

We have seen significant improvements in data management processes, particularly with integration capabilities that allow us to easily retrieve and manipulate data through simple queries. This enhances our workflow significantly.

BigQuery allows for very fast access, and it is efficient in handling large datasets compared to other SQL databases. It integrates well with other GCP products, and creating subscriptions in the UI is straightforward. The whole ecosystem of GCP products makes BigQuery beneficial for our data-handling tasks. Additionally, it is more cost-effective compared to alternatives like AWS.

There is a limitation when copying data directly from BigQuery; it only supports up to ten MB when copying data to the clipboard. For larger data, we have to download it as JSON or Excel files. This limitation could be addressed for better usability.

I have been working with Google Cloud SQL for over one year.

I have not experienced any downtime issues with the solution; it has been stable.

There are some limitations in scalability, particularly when dealing with very large datasets. However, the cost savings we gain often balance this out.

We haven't had much interaction with technical support outside of accessing documentation available online.

Positive

Before using BigQuery, we were utilizing Azure Warehouse. We switched to BigQuery as GCP services were slightly cheaper and more cost-effective. The cost savings were a significant factor in our decision.

The initial setup process for BigQuery was straightforward. By using Terraform, we were able to manage the entire setup efficiently and keep track of who is making changes.

We have a team of developers who manage the platform. Infrastructure changes are tracked, and project owners approve updates.

I can't provide concrete documentation on ROI, but GCP's evolving services have been more cost-effective compared to AWS.

AWS has a large number of users and has built a model with high costs, whereas GCP offers cost-effective solutions.

AWS was another option considered, but due to cost considerations, we opted for GCP.

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.

My primary use case for the solution is as a powerful tool for handling and analyzing large datasets. The transition to GA4, which uses an event-based measurement framework, necessitated a more robust solution for detailed reporting and data analysis. It serves as both the storage and querying framework for this data.

The platform has significantly improved the organization's ability to analyze detailed and scalable data. It efficiently handles large volumes of data, crucial for timely decision-making and in-depth analytics. However, the shift from free reporting tools to a pay-for-use model has introduced additional costs.

The product's most valuable features include its scalability and the ability to handle complex queries on large datasets. The system's capacity to dynamically allocate resources based on data size and query complexity ensures efficient performance.

The product could benefit from improvements in user-friendliness, particularly in terms of the user interface. An easier, more intuitive graphical user interface (GUI) with drag-and-drop functionality for creating reports and segments would enhance usability.

I have been using BigQuery for approximately five to six years. My usage has increased recently, especially after the launch of GA4 (Google Analytics 4).

The platform's stability is commendable. As part of Google's infrastructure, it benefits from robust reliability and failover mechanisms, ensuring consistent performance and data integrity.

This solution is highly scalable and can efficiently handle vast amounts of data and complex queries. Its dynamic resource allocation ensures that performance scales with data size and query demands.

Google offers limited customer service and support. For detailed assistance, users may need to consult external experts or partners. Support primarily directs users to documentation and community resources.

Negative

Before using BigQuery, I relied on native analytics tools, which offered less detailed reporting. The switch was driven by the need for more comprehensive and scalable reporting capabilities.

The initial setup is relatively straightforward as it is a SaaS offering. However, preparing data for import and setting up queries can require considerable effort and technical knowledge.

Implementation was handled in-house. The expertise required for effectively using this platform often involves extensive reading and self-learning, as the process is quite technical.

The product operates on a pay-for-use model. Costs include storage and query execution, which can accumulate based on data volume and complexity.

While exploring options, I considered various analytics platforms and frameworks, but BigQuery was selected due to its integration with Google's ecosystem and its robust handling of large datasets.

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.

BigQuery allows you to quickly analyze logs from your systems to identify the severity of issues. It integrates well with other Google Cloud services, such as Cloud Logging, where you can easily manipulate various data types and analyze all logs.

BigQuery excels at data analysis. It processes vast amounts of information using its advanced architecture and sophisticated querying capabilities, making it crucial for critical insights and safe for handling sensitive data. 

BigQuery should integrate with other tools, such as Cloud Logging and Local Studio, to enhance its capabilities further and enable powerful and innovative analyses.

I have been using BigQuery for two years.

I have opted for Fireye, Elasticsearch, and Alcon. One principal difference is that BigQuery starts with machine learning and WAN implementations, while you can implement VMware or other active boxes. Therefore, it is recommended that cloud VMs be used for BigQuery processes. You can execute jobs in the cloud, such as VMware.

For instance, you can compute analytics for email, apply filters, and manipulate weather data. It provides higher efficiency, though exact benchmarks are unclear. Additionally, starting the query flow login request can also be advantageous.

The initial setup is automatic. It requires one person. You need to log in to the Google Cloud platform, import the necessary package into your query, and then you can start querying your data. 

If you need a solid CRM solution integrated with Azure, you'll need knowledgeable people to support it. Three individuals can form a strong CRM team connected to Azure, leveraging BigQuery.

You can use BigQuery to generate and manage large datasets efficiently. Whether using a flexible integrated environment like Dataflow or a local studio, BigQuery provides powerful tools for querying and analyzing data.

The product is free of cost.

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.