BigQuery Reviews

I usually need to catalog. In my case, it's more related to data governance. I need to catalog information from BigQuery. I want to ensure the data quality tool is in sync with BigQuery, so I go to BigQuery and do queries to make sure it was synced with Atlan, for example, for data quality tools. I create validation rules and need to write the rule in BigQuery to create a query there, see how long it takes to run, and evaluate its performance in a data quality tool.

What I have seen is that they are using BigQuery as a central repository. They bring dispersed information to BigQuery, which facilitates exploring the data and gaining insights. Consequently, it improves operations, response time, and the business overall.

As a user, I have liked using BigQuery to create queries. They have a table explorer feature that allows you to select a table, choose fields, and generate queries easily, which significantly facilitates my workflow. I also appreciate the lineage feature, which shows how tables relate to each other and enables end-to-end usage visualization. 

Furthermore, its integration with other tools like Atlan through a Google Chrome extension is highly beneficial. Using BigQuery's central repository brings dispersed information together, which facilitates exploring the data and gaining insights. Consequently, it improves operations, response time, and the business overall.

There are integration challenges, particularly with performance when exporting data to BigQuery from other tools like Qualitics. It can be slower and more problematic compared to other platforms such as Snowflake.

I have been working with BigQuery for one year.

I have not seen a lot of problems, so I would say BigQuery is quite stable.

In my opinion, BigQuery is very scalable yet has some limitations regarding performance that are not always as required.

I don't have direct contact with BigQuery's support team. Our organization manages this through internal communication, and I contact my company’s team when issues arise.

I would recommend using BigQuery because it's a very good tool, easy to manage, and similar to other databases. Those familiar with SQL Server or Oracle can adapt to BigQuery easily. It's a scalable cloud solution. 

Overall, I would rate BigQuery as nine 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.

We basically used it to store server data and generate reports for enterprise architects. It was a valuable tool for our enterprise design architect.

Everything they advertised or listed worked exactly as promised. That was advantageous to us. 

In future releases, I would like to see more pre-defined aggregated forms. After using BigQuery, we need to use the data in an enterprise architecture dimensional data model. So, having pre-defined aggregated forms would be helpful. 

Additionally, I would like to see version-based implementation and a fallback arrangement for data stored in BigQuery storage. These are some features I'm interested in.

I have experience with BigQuery. 

When I joined the company, BigQuery was already implemented by our team.

It is a cheap solution. 

I would recommend getting a clear understanding of BigQuery's functionalities and what it's best suited for. If your needs align with its capabilities, then you should definitely proceed. 

BigQuery offers fantastic features, but it's important to understand its purpose beforehand. Otherwise, you might face difficulties later on.

Overall, I would rate the solution an eight out of ten.

The primary use case of BigQuery is within banking applications in the CDP. The front-end system pushes data, specifically mobile and net banking data, into BigQuery for processing and analysis. It involves significant data and requires specialized tools to utilize it fully. For example, we use AMS reports, breaking the data into various layers rather than using it in a single database.

At our company, the adoption is still in progress at various layers, but it was recently restarted and put into production. There are less than 200 users currently, but we need to figure out why we even have all this data we send out and whether we should rely on vendor-based databases.

We would want a great database for any new products we develop or if we need to send out an application from one store to another.

The integrated data storage features are good. Altogether, it provides the required functionality.

BigQuery is a single platform that can support different use cases and data bandwidths, whereas other platforms may require additional data platforms for each use case.

There are some limitations in the query latency compared to what it was three years ago. Despite this, BigQuery still provides the necessary functionality as compared to the other platforms.

An additional feature I would like is the one available in AWS, where you have a framework to onboard past services and start building analytical models and data design. The framework makes it easier for any new organization to adopt cloud computing quickly.

I have been working with BigQuery for nine-plus years.

There is a lot of room for improvement in stability.

So they're quickly catching up with the business and marketing needs. I know Google BigQuery started very late in the game, and they covered a lot. However, there is room to improve a lot on that. I rate the stability a seven out of ten.

It is a very scalable solution. I would rate it a ten out of ten.

Sometimes the tickets take time to go through. I would rate it an eight out of ten.

Positive

Our organization has a multi-cloud strategy, and we use different data transmission and storage tools depending on the cloud provider. For example, in Azure, we use Databricks for data transmission and Sines for storing data. In AWS, we use DynamoDB for specific use cases. Regarding Google, we use the CDP platform, specifically BigQuery, for their data storage and analysis needs. 

We have various tools across the different platforms to meet the specific use case needs. We use BigQuery within the Google CDP platform for their data storage and analysis needs. It varies from use case to use case, and we use different platforms accordingly.

The initial setup is very simple.

The costing model is a bit expensive as compared to its equivalent partners. If they can optimize the cost, it would be much better. Otherwise, people would step back.

I would rate it a seven on a scale of one to ten, where ten is for the cheapest, and one is for being high priced.

Our organization has solutions independent of the cloud-native solution, and Microsoft encourages that. For instance, Database is one of the tools which can be deployed across different clouds.

In terms of storing data, we prefer to go with the table as compared to Synapse as the database. Then, in terms of enabling the porter on Databricks, which is much faster compared to any other database in the current industry.

BigQuery scores pretty well for trusting the larger as well as broader data. Across all the 99 security queries, the benchmark can be pretty impressive. And that is the only reason we eventually did the Databricks with Azure. The partnership with Databricks and Azure was great.

BigQuery is a tool wherein it can support your structured, unstructured, secured, and unsecured data, and it can support the server if you use any right-level services from BigQuery.

However, data encryption and integration could be difficult if you want to transfer data to another cloud. For example, when I have data from the other cloud, it would be difficult to bring that data into the data systems for me. Even if I consider doing it, it will cost me and might be expensive.

When you try to import data from one vendor to another, it also results in additional data transfer costs and data integration issues.

If you keep the solution in the same platform and the same data fabric level, then the data from that level get joined and maintained locally to that cloud. And if you're sending some data across the cloud, only use the basics to connect the data. That way it'll detect the fabric. So if you go with the native tool, that is the limitation we'll have. Cloud diagnostics does get you out of it.

When it comes to BigQuery, it is deployed in one cloud. It is native to Google and can only stay on Google; that is the only drawback.

Overall, I would rate it a seven out of ten.

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 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.

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.