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.

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.

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.

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. 

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 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 are into conversational commerce platforms. All the conversations and the chat history are captured when we chat with a chatbot. Our application is built on NoSQL. We put the data into BigQuery as a data warehouse, where we refine the data. We analyze the chat history and give analytic reports to our merchants using our SaaS platform. It is to understand the chat conversation, how many people had a conversation, and what key buttons they clicked.

We also provide analytics on how many orders were completed. We are building a commerce and conversational dashboard for our enterprise customers and offering them on Looker. Looker was earlier known as Google Data Studio. For applications, we segment customers and use the customer segments to broadcast messages across social channels. All these things are being queried over BigQuery to do segmentations.

On the front end, we give them the option of segmenting based on different data attributes. Then, it goes to BigQuery to filter out the data and find the number of customers who meet the defined conditions. Based on that, we send the messages to the segmented customers. We are doing multiple things related to conversation commerce using BigQuery.

It is a cloud platform. We just need to query and get the output. Anyone can use the product. Even non-coders can review the data in BigQuery.

There should be an easier way to migrate from NoSQL to SQL. The process of migrating from Datastore to BigQuery should be improved. We use Datastore and BigQuery. If both products can be synced well, it will improve employee productivity. 

We had to write a lot of pipelines and logic for real-time streaming from Datastore, which is a NoSQL, to BigQuery, which is more of a structured database. However, because both products are internal to the Google Cloud Platform, they should have some provision to create and keep syncing it automatically. It will be an advantage for the customers. Currently, we build replicas. It would be easier if some simple connection replicates the changes in BigQuery.

My company has been using the solution for five years. I have been using it for a year.

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

The solution is more scalable because it is in the cloud. It is an advantage. I rate the scalability of the tool an eight out of ten. If we are integrating it with two different platforms, then it becomes a little difficult for us. If there is a data pipeline error, we cannot scale immediately. If we have to integrate NoSQL with BigQuery, it sometimes becomes a challenge for real-time streaming.

Five developers within my team are building all the logic on BigQuery. We have around 100 to 200 customers with five to six employees each using our platform. When they use our platform and query using different features, these queries hit BigQuery, and we render the data. We are the designers designing using BigQuery, and the end users use the UI.

I would rate technical support a little less. We have always struggled to get quicker support.

The initial setup is very simple. The solution is cloud-based.

The tool has competitive pricing. I rate the pricing an eight out of ten.

I have a technical team that works deeply into it and gives me the output. I don't extensively use BigQuery as a developer to develop things. Overall, I rate the solution an eight out of ten.