BigQuery Reviews

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 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.

This is a solution from Google that is 100% cloud-based, based on GCP. BigQuery is similar to Snowflake in the way it manages data analytics. It completely decouples storage from Compute. It has a proprietary way of storing and accessing data in its own data store and is 100% managed without you needing to install or deploy anything. There is no need to arrange for any infrastructure in order to use this solution. Go to BigQuery.com, create an account and you will get a console on your browser where you can start creating the end to end data platform - databases, data warehouses, roles, users, ETL / ELT pipelines and write transformations - all via the workspace.

Although BigQuery in completely managed on cloud, one has to use many services of BigQuery and GCP in order to create the end-to-end data setup. BigQuery acts as the core Data Warehouse mechanism, but it needs additional services like - Google Cloud Dataflow, Cloud pub/sub, Cloud BigTable, Cloud DataPrep, Cloud DataProc, Cloud SQL. Being different from the traditional way of setting up end-to-end data engineering platform, the learning curve for BigQuery is a bit steeper. Google BigQuery ecosystem can surely make the ecosystem a bit more leaner.  

I have been using this solution for 3 years. 

A very stable solution. All native abilities of Google solutions are inbuilt in BigQuery. I would predict that Snowflake and BigQuery will occupy a much larger share of the cloud data analytics space in the coming years than Azure Synapse in the future. 

This is a very scalable solution. BigQuery's pricing is more suitable for large operations that plan to scale. For smaller businesses, this may be an expensive solution. Creating a BigQuery account is free, but as soon as you start using computations and data capabilities, charges start adding up.

There is no installation involved while using BigQuery. It is as simple as opening a Gmail account and creating your own end-to-end setup. You can start creating a database schema, data bases, create pipelines with step-by-step activities ranging from ingestion to transformation to updating the data marts. Its completely managed and one does not need to worry about licenses of installations.

At e-Zest, in our projects for our enterprise customers, typically between 2 to 8 people were needed for end-to-end data platform development. This included one or two admins, 2-3 ETL developers and 2-3 data warehouse members with strong SQL and database skills.

One terabyte of data costs $20 to $22 per month for storage on BigQuery and $25 on Snowflake. Snowflake is costlier for one terabyte but only marginally. Both charge differently for compute. BigQuery charges based on how much data is inserted into the tables. Reading values from tables has no cost.

BigQuery charges you based on the amount of data that you handle and not the time in which you handle it. This is why the pricing models of Snowflake and BigQuery are different and this becomes a key consideration in the decision of which platform to use. 

We evaluated Snowflake, Azure Synapse and Amazon Redshift along with BigQuery. Snowflake and BigQuery are very similar in the way they operate. However, I would rate Snowflake slightly higher than BigQuery. I would rate Azure Synapse third and AWS Redshift fourth. The way Snowflake operates, and allows integration with other systems makes it a better alternative to BigQuery. Also Snowflake's and BigQuery's underlying architectures are quite different, although for the end user they may be appearing similar for use.

BigQuery takes a different approach to design and this is important to consider. BigQuery on its own is not enough and you need other tools also offered by Google to transform data (some of which I have mentioned in an earlier section).

The BigQuery ecosystem is a little more complex than the Snowflake ecosystem. Those who have traditionally worked on on-premise data warehouses, find Snowflake much easier to set up. Those who are trying to establish warehouses for the first time, find Google easier. 

I would rate this solution a 7 out of 10. 

I use it to deal a lot with marketing, specifically Google Ads, YouTube, and Google Analytics. But mostly, I utilize it for its capabilities to sync directly up with Google ads transfers.

Instead of having to go directly into the platform, pull various reports after and save those reports, port them over into Google Sheets, and then import ranges and queries. Then, having to transform the data to my needs, I can build a SQL script that is to my needs directly within the platform so that when the data comes out at the platform, it's already essentially punched into the format that I needed.

Its SQL editor is very easy to use and very easy to conceptualize. The way that it breaks data down into silos is easily discernible. So, I guess that's really it.

There is a good amount of documentation out there, but they're consistently making changes to the platform, and, like, their literature hasn't been updated on some plans.

I have been using BigQuery for a little over a year.

It's pretty stable. It's fast, and it is able to go through large quantities of data pretty quickly.

I think that it's easy to scale. For instance, when I need the data for a new client, I just ask to have their account added to my MCC, and the MCC deploys through basically, rolls out all the accounts available really quickly.

I am the sole user of the solution in my company.

I've tried getting in touch with the support, and that's actually the difficult part. So, unless you're using a higher-tiered version of the platform, getting support can be problematic.

I got into Google Big Query since it met my needs.

Regarding the deployment model, I work in its native GUI. I'm not sure what the SaaS version is, so I just utilize it with Google Cloud's native console.

Regarding the deployment process, I would have to create your own instance within Google Cloud. You create a project, that project. Then, you start nesting your data streams into that project. And then we do have to backfill some of the data because it'll only start grabbing data from the date that you tell it to in thirty days before. So if you need data that is previous to thirty days, then you've got it going to backfill it. After that, I found that it was a pretty easy and quick deployment.

Speaking about the time for deployment, I would say that having the knowledge I have now, it wouldn't take me even an afternoon. But at the time, because I didn't know what I was doing, it took about two-three days.

I did the deployment myself.

Price-wise, I think that is very reasonable. Like, I don't use a ton of computing when it comes to the platform, so I haven't ever really had to pay when it comes to the product. I really don't have to pay from month to month.

I did not go through other solutions.

I would tell those planning to use the solution to just go out and utilize as much information as possible. There's a ton of great information on the platform and how it can be best utilized.

The solution doesn't necessarily require maintenance.

It's a great platform. It's pretty easy to use. You do have to have some skill and uptake when it comes to actually writing SQL and writing queries. But then it does need better support capabilities. But aside from that, it's a pretty good platform.

I rate the solution a 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.

It is a pivotal component in enterprise data architecture, and crucial in data lake operations, whether supporting data warehouses or functioning as part of a broader data lake ecosystem.

One of the most significant advantages lies in the decoupling of storage and compute which allows to independently scale storage and compute resources, with the added benefit of extremely cost-effective storage akin to object storage solutions. Its unique architecture not only provides robust enterprise data warehouse capabilities but also seamlessly integrates with data lake functionalities.

The main challenges are in the areas of performance and cost optimizations. Achieving optimal results demands a certain level of familiarity with the platform's internals. The key point for improvement lies in the performance optimization.

I have been working with it for three months.

It exhibits a high level of stability and security, there are no notable issues in these aspects. I would rate it nine out of ten.

It is designed to seamlessly scale with the growing demands of data processing, there are no issues with it. I would rate it nine out of ten.

The technical support is commendable. However, there is room for improvement in the availability of resources and documentation from a technological standpoint. I would rate it seven out of ten.

Neutral

In the landscape of enterprise data warehouses, BigQuery stands out as a superior choice when compared to alternatives like Azure Synapse, AWS Redshift, and Snowflake. While Snowflake is known for its higher costs, and Redshift is perceived as both complex and expensive, Azure Synapse presents its own set of constraints with its MPP architecture and reliance on an RDBMS in-between. BigQuery, on the other hand, has a distinct edge with its seamless migration process, vast capabilities, and a harmonious balance of storage, computing, cost-effectiveness, and performance efficiency. This is particularly evident as organizations and professionals, including myself, have experienced ease in migrating from other vendors to BigQuery. Drawing from my extensive experience working across various cloud platforms such as AWS, Azure, and Snowflake, BigQuery consistently emerges as a robust and preferable solution.

The initial setup is straightforward.

Its cost structure operates on a pay-as-you-go model. I would rate it seven out of ten.

Whether for small, medium, or large enterprises, it is a recommendable choice. Its pricing model makes it accessible and manageable based on your usage. Given that many individuals and businesses already have Gmail accounts and utilize Google Cloud workspaces, incorporating BigQuery into operations is seamless. Moreover, a complimentary reporting tool, Looker Studio, is available for free, enhancing the reporting capabilities on BigQuery or via Google Sheets. 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 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.