BigQuery OverviewUNIXBusinessApplication

BigQuery is the #6 ranked solution in top Cloud Data Warehouse tools. PeerSpot users give BigQuery an average rating of 8.0 out of 10. BigQuery is most commonly compared to Teradata: BigQuery vs Teradata. BigQuery is popular among the large enterprise segment, accounting for 69% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a computer software company, accounting for 20% of all views.
BigQuery Buyer's Guide

Download the BigQuery Buyer's Guide including reviews and more. Updated: December 2022

What is BigQuery?

BigQuery is an enterprise data warehouse that solves this problem by enabling super-fast SQL queries using the processing power of Google's infrastructure. ... You can control access to both the project and your data based on your business needs, such as giving others the ability to view or query your data.

BigQuery Video

BigQuery Pricing Advice

What users are saying about BigQuery pricing:
  • "I have tried my own setup using my Gmail ID, and I think it had a $300 limit for free for a new user. That's what Google is offering, and we can register and create a project."
  • "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 BigQuery charges based on how much data is inserted into the tables. 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 are different and it becomes a key consideration in the decision of which platform to use."
  • "The price could be better. Usually, you need to buy the license for a year. Whenever you want more, you can subscribe to it, and you can use it. Otherwise, you can terminate the license. You can use it daily or monthly, and we use it based on a project's requirements."
  • BigQuery Reviews

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    Anonymous  - PeerSpot reviewer
    Data Engineer at a financial services firm with 10,001+ employees
    Real User
    Top 20
    A fully-managed, serverless data warehouse with good storage and unlimited table length
    Pros and Cons
    • "The main thing I like about BigQuery is storage. We did an on-premise BigQuery migration with trillions of records. Usually, we have to deal with insufficient storage on-premises, but in BigQuery, we don't get that because it's like cloud storage, and we can have any number of records. That is one advantage. The next major advantage is the column length. We have some limits on column length on-premises, like 10,000, and we have to design it based on that. However, with BigQuery, we don't need to design the column length at all. It will expand or shrink based on the records it's getting. I can give you a real-life example based on our migration from on-premises to GCP. There was a dimension table with a general number of records, and when we queried that on-premises, like in Apache Spark or Teradata, it took around half an hour to get those records. In BigQuery, it was instant. As it's very fast, you can get it in two or three minutes. That was very helpful for our engineers. Usually, we have to run a query on-premises and go for a break while waiting for that query to give us the results. It's not the case with BigQuery because it instantly provides results when we run it. So, that makes the work fast, it helps a lot, and it helps save a lot of time. It also has a reasonable performance rate and smart tuning. Suppose we need to perform some joins, BigQuery has a smart tuning option, and it'll tune itself and tell us the best way a query can be done in the backend. To be frank, the performance, reliability, and everything else have improved, even the downtime. Usually, on-premise servers have some downtime, but as BigQuery is multiregional, we have storage in three different locations. So, downtime is also not getting impacted. For example, if the Atlantic ocean location has some downtime, or the server is down, we can use data that is stored in Africa or somewhere else. We have three or four storage locations, and that's the main advantage."
    • "It would be better if BigQuery didn't have huge restrictions. For example, when we migrate from on-premises to on-premise, the data which handles all ebook characters can be handled on-premise. But in BigQuery, we have huge restrictions. If we have some symbols, like a hash or other special characters, it won't accept them. Not in all cases, but it won't accept a few special characters, and when we migrate, we get errors. We need to use Regexp or something similar to replace that with another character. This isn't expected from a high-range technology like BigQuery. It has to adapt all products. For instance, if we have a TV Showroom, the TV symbol will be there in the shop name. Teradata and Apache Spark accept this, but BigQuery won't. This is the primary concern that we had. In the next release, it would be better if the query on the external table also had cache. Right now, we are using a GCS bucket, and in the native table, we have cache. For example, if we query the same table, it won't cost because it will try to fetch the records from the cached result. But when we run queries on the external table a number of times, it won't be cached. That's a major drawback of BigQuery. Only the native table has the cache option, and the external table doesn't. If there is an option to have an external table for cache purposes, it'll be a significant advantage for our organization."

    What is our primary use case?

    We use BigQuery to store data in a table and query it. Data storage can be either an internal native table or an external table where the external source will point to Google Cloud Storage or Google Drive. 

    Wherever we can have external storage, we can have a table built pointing to that external storage and query the tables. In BigQuery, we can query the table or even do DML operations, like insert, delete, etc.

    What is most valuable?

    The main thing I like about BigQuery is storage. We did an on-premise BigQuery migration with trillions of records. Usually, we have to deal with insufficient storage on-premises, but in BigQuery, we don't get that because it's like cloud storage, and we can have any number of records. That is one advantage. 

    The next major advantage is the column length. We have some limits on column length on-premises, like 10,000, and we have to design it based on that. However, with BigQuery, we don't need to design the column length at all. It will expand or shrink based on the records it's getting.

    I can give you a real-life example based on our migration from on-premises to GCP. There was a dimension table with a general number of records, and when we queried that on-premises, like in Apache Spark or Teradata, it took around half an hour to get those records. In BigQuery, it was instant. As it's very fast, you can get it in two or three minutes. That was very helpful for our engineers.

    Usually, we have to run a query on-premises and go for a break while waiting for that query to give us the results. It's not the case with BigQuery because it instantly provides results when we run it. So, that makes the work fast, it helps a lot, and it helps save a lot of time. 

    It also has a reasonable performance rate and smart tuning. Suppose we need to perform some joins, BigQuery has a smart tuning option, and it'll tune itself and tell us the best way a query can be done in the backend. 

    To be frank, the performance, reliability, and everything else have improved, even the downtime. Usually, on-premise servers have some downtime, but as BigQuery is multiregional, we have storage in three different locations. So, downtime is also not getting impacted.

    For example, if the Atlantic ocean location has some downtime, or the server is down, we can use data that is stored in Africa or somewhere else. We have three or four storage locations, and that's the main advantage.

    What needs improvement?

    It would be better if BigQuery didn't have huge restrictions. For example, when we migrate from on-premises to on-premise, the data which handles all ebook characters can be handled on-premise. But in BigQuery, we have huge restrictions. If we have some symbols, like a hash or other special characters, it won't accept them. Not in all cases, but it won't accept a few special characters, and when we migrate, we get errors. 

    We need to use Regexp or something similar to replace that with another character. This isn't expected from a high-range technology like BigQuery. It has to adapt all products. For instance, if we have a TV Showroom, the TV symbol will be there in the shop name. Teradata and Apache Spark accept this, but BigQuery won't. This is the primary concern that we had.

    In the next release, it would be better if the query on the external table also had cache. Right now, we are using a GCS bucket, and in the native table, we have cache. For example, if we query the same table, it won't cost because it will try to fetch the records from the cached result. But when we run queries on the external table a number of times, it won't be cached. That's a major drawback of BigQuery. Only the native table has the cache option, and the external table doesn't. If there is an option to have an external table for cache purposes, it'll be a significant advantage for our organization.

    For how long have I used the solution?

    I have been using BigQuery for more than three years.

    Buyer's Guide
    BigQuery
    December 2022
    Learn what your peers think about BigQuery. Get advice and tips from experienced pros sharing their opinions. Updated: December 2022.
    657,849 professionals have used our research since 2012.

    What do I think about the stability of the solution?

    BigQuery is a stable solution.

    What do I think about the scalability of the solution?

    BigQuery is highly scalable. We can have unlimited storage if we do 20 records, and It's very fast. Even if we scale it to 20 trillion, it will still be fast. 

    In my organization, about two in five use BigQuery. When I joined the company a year back, usage was relatively moderate. However, now usage increased because of the on-premise to GCP migration. Because of many successful projects, several people are using BigQuery now.

    How are customer service and support?

    We have dedicated support people who help us with the framework. If there is a technical issue in BigQuery, we just get help from the technical team. But if there are any engineering issues or some data issues, our team will handle them.

    Which solution did I use previously and why did I switch?

    I use Teradata and then Apache Spark on-premises.

    How was the initial setup?

    The initial setup is relatively straightforward. There are some restrictions, like the project's name. It has to be unique, but once that project is created, we can simply go to an option, query, and the query control will open, and we can start creating a table, loading data, querying, and everything. So that's quite simple and straightforward.

    What about the implementation team?

    When I joined PayPal, the setup was done in-house. When I worked at another organization, Cognizant, we had Google's help. So a Google specialist helped us set up and everything.

    What's my experience with pricing, setup cost, and licensing?

    I have tried my own setup using my Gmail ID, and I think it had a $300 limit for free for a new user. That's what Google is offering, and we can register and create a project.

    What other advice do I have?

    On a scale from one to ten, I would give BigQuery an eight.

    Which deployment model are you using for this solution?

    Public Cloud

    If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

    Google
    Disclosure: I am a real user, and this review is based on my own experience and opinions.
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    PeerSpot user
    MandarGarge - PeerSpot reviewer
    V.P. Digital Transformation at e-Zest Solutions
    Real User
    Top 5Leaderboard
    Cost-effective Cloud data platform based on Google Cloud that is fully managed service, very easy to set up and manage
    Pros and Cons
    • "It has a proprietary way of storing and accessing data in its own data store and is 100% managed without you needing to install anything. There is no need to arrange for any infrastructure to be able to use this solution."
    • "There are many tools that you have to use with BigQuery that are different services also provided for by Google. They need to all be integrated into BigQuery to make the solution easier to use."

    What is our primary use case?

    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.

    What needs improvement?

    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.  

    For how long have I used the solution?

    I have been using this solution for 3 years. 

    What do I think about the stability of the solution?

    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. 

    What do I think about the scalability of the solution?

    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.

    How was the initial setup?

    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.

    What's my experience with pricing, setup cost, and licensing?

    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. 

    Which other solutions did I evaluate?

    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.

    What other advice do I have?

    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. 

    Which deployment model are you using for this solution?

    Public Cloud

    If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

    Google
    Disclosure: I am a real user, and this review is based on my own experience and opinions.
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    PeerSpot user
    Buyer's Guide
    BigQuery
    December 2022
    Learn what your peers think about BigQuery. Get advice and tips from experienced pros sharing their opinions. Updated: December 2022.
    657,849 professionals have used our research since 2012.
    Deputy General Manager at a tech vendor with 10,001+ employees
    Real User
    Gave us 27% performance improvement and reduced costs by about 17%
    Pros and Cons
    • "There are some performance features like partitioning, which you can do based on an integer, and it improves the performance a lot."
    • "With other columnar databases like Snowflake, you can actually increase your VM size or increase your machine size, and you can buy more memory and it will start working faster, but that's not available in BigQuery. You have to actually open a ticket and then follow it up with Google support."

    What is our primary use case?

    BigQuery is a PaaS solution. There's only one version available on Google Cloud. Because it's deployed on cloud, it will update automatically.

    What is most valuable?

    If I'm collaborating with Google Data Cloud, I can use the cache, and I don't have to pay again and again. There are some performance features like partitioning, which you can do based on an integer, and it improves the performance a lot. There's also the Array function. You can also enable Spark on BigQuery, which is actually faster than any other Spark. If you use Dataproc, Spark on BigQuery is much faster.

    Spark will actually eliminate the usage of a lot of Adobe legacy things. It will act as a Spark SQL.

    It is not that cost-friendly, but it is very performance-friendly. There are also machine learning features.

    What needs improvement?

    For example, if I have a query, and I have done everything to improve it, the query will still take 15 minutes. With other columnar databases like Snowflake, you can actually increase your VM size or increase your machine size, and you can buy more memory and it will start working faster, but that's not available in BigQuery. You have to actually open a ticket and then follow it up with Google support.

    For how long have I used the solution?

    I have been using this solution for two and a half years.

    What do I think about the stability of the solution?

    BigQuery is very stable. It is getting used a lot.

    What do I think about the scalability of the solution?

    It is definitely scalable. You do not have to do any configurations. It will be able to handle petabytes of data.

    How are customer service and support?

    Technical support is excellent. It is Google, and they always provide the best. We haven't needed to contact Google for BigQuery specifically, but I have contacted Google support for other things and they were pretty responsive.

    Which solution did I use previously and why did I switch?

    I have experience with Snowflake.

    What was our ROI?

    I was working on a project where we were building systems and loading the data manually. Once we moved to BigQuery, we saw ROI in terms of cost savings. We saw 27% performance improvement in most of our queries. Our total costs were reduced by about 17%. In terms of cost and time, we were able to save effort.

    There was some learning and training involved, which lasted six months, so we saw the real ROI after a year.

    What other advice do I have?

    I would rate this solution 8 out of 10.

    My advice is to first identify your use case. If you have Google Cloud then you have two databases to compare, BigQuery and Snowflake. BigQuery is typically used to analyze petabytes of data. If you're looking for transitional query, then you should have a different system. BigQuery cannot handle unstructured data, so that is one thing you have to think about. 

    In terms of latency, if you want single-digit millisecond latency then BigQuery is not good. It is very fast, but if you want single-digit millisecond latency, then you probably have to go to a no-SQL database solution.

    My suggestion is to analyze your use case and then map it with the BigQuery features.

    Which deployment model are you using for this solution?

    Public Cloud

    If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

    Google
    Disclosure: My company has a business relationship with this vendor other than being a customer: Partner
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    PeerSpot user
    Sr. Manager - TAAS at a manufacturing company with 10,001+ employees
    Real User
    Issue-free, straightforward to set up and offers good expansion capabilities
    Pros and Cons
    • "It's straightforward to set up."
    • "We'd like to have more integrations with other technologies."

    What is our primary use case?

    We primarily use the solution for data analytics. 

    What is most valuable?

    I enjoy the scalability of the solution. Its scalability is very impressive.

    It's straightforward to set up.

    The solution has been stable.

    What needs improvement?

    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.

    For how long have I used the solution?

    I've been using the solution since around 2019.

    What do I think about the stability of the solution?

    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. 

    What do I think about the scalability of the solution?

    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. 

    How are customer service and support?

    I haven't used technical support just yet. I haven't come across any problems which would require me to reach out. 

    Which solution did I use previously and why did I switch?

    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.

    How was the initial setup?

    The initial setup is very simple and straightforward. I'd rate the ease of implementation a four out of five.

    What's my experience with pricing, setup cost, and licensing?

    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. 

    What other advice do I have?

    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.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
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    PeerSpot user
    Swayan Jeet Mishra - PeerSpot reviewer
    Lead Machine Learning Engineer at Schlumberger
    Real User
    Top 5
    A serverless system that is easy to set up and offers fast analysis of data
    Pros and Cons
    • "It's similar to a Hadoop cluster, except it's managed by Google."
    • "It would be helpful if they could provide some dashboards where you can easily view charts and information."

    What is our primary use case?

    We are primarily using the solution to crunch data. Then, we are doing some ETL work on top of the data. 

    What is most valuable?

    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.

    What needs improvement?

    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.

    For how long have I used the solution?

    I've been using the solution for two or three years. 

    What do I think about the stability of the solution?

    This is a highly stable product. There are no bugs or glitches. It doesn't crash or freeze. 

    What do I think about the scalability of the solution?

    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. 

    How are customer service and support?

    We have yet to reach out to technical support. We haven't had any issues. 

    Which solution did I use previously and why did I switch?

    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.

    How was the initial setup?

    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.

    What's my experience with pricing, setup cost, and licensing?

    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. 

    What other advice do I have?

    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.

    Which deployment model are you using for this solution?

    Public Cloud

    If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

    Google
    Disclosure: I am a real user, and this review is based on my own experience and opinions.
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    PeerSpot user
    Mohamed Tahri - PeerSpot reviewer
    Head of Insights and Data Middle East at Capgemini
    Real User
    Expandable and easy to set up but needs more local data residency
    Pros and Cons
    • "As a cloud solution, it's easy to set up."
    • "We'd like to see more local data residency."

    What is our primary use case?

    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.

    What is most valuable?

    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. 

    What needs improvement?

    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.

    For how long have I used the solution?

    We've been implementing this solution since the inception of these products. We are Platinum Elite partners with most vendors.

    What do I think about the stability of the solution?

    The solution has a reputation for being stable. It's not a problem. 

    What do I think about the scalability of the solution?

    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.

    How are customer service and support?

    I've never dealt directly with technical support. I can't speak to how helpful or responsive they are. 

    How was the initial setup?

    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. 

    What's my experience with pricing, setup cost, and licensing?

    I don't deal with licensing aspects of the product. The licenses are always purchased by our clients. 

    What other advice do I have?

    I'd rate the solution seven out of ten.

    Which deployment model are you using for this solution?

    Public Cloud
    Disclosure: My company has a business relationship with this vendor other than being a customer: Implementer
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    PeerSpot user
    Machine Learning Enginee at a retailer with 201-500 employees
    Real User
    Top 20
    Able to expand with lots of functionality but needs better machine learning capabilities
    Pros and Cons
    • "The setup is simple."
    • "I noticed recently it's more expensive now."

    What is our primary use case?

    We use BigQuery as a data source.

    We mainly use it to do some transformations. Once we collect query data from it, we use other services to do model training or predictions. We don't really utilize all the features provided by BigQuery. We mainly use some basic data transformation options. It also provides some machine learning models.

    What is most valuable?

    In many functions, it's very similar to Spark Kubernetes. The cluster is good. It'll provide computation capabilities. 

    The setup is simple.

    It is stable. The performance is good. 

    It is a scalable solution. 

    We do not find the solution that expensive. 

    What needs improvement?

    Machine learning could be improved. There are some machine learning models in BigQuery; however, maybe more libraries can be provided. We'd like it extended into the Spark ML library. 

    I noticed recently it's more expensive now. I didn't compare them to others, however, and in our team, we don't consider the price of it much.

    For how long have I used the solution?

    I've been using the solution for several months.

    What do I think about the stability of the solution?

    It is stable and reliable. There are no bugs or glitches. 

    I'd rate the overall stability an eight out of ten. It offers a good level of performance. There are billions of accounts. 

    What do I think about the scalability of the solution?

    It's scalable. We don't need to worry about scalability issues in our case. For us, it's good enough.

    We have millions of customers and thousands of products. 

    How are customer service and support?

    I've never dealt with technical support. I can't speak to how helpful or responsive they are. We have a bigger team and tend to learn from each other.

    Which solution did I use previously and why did I switch?

    I also use Spark, which has similar functions. I've also used Databricks. 

    I've used BigQuery for a longer time, however, Databricks is easier when it comes to the setup of a complete solution. With BigQuery, we need to develop an intranet solution and set up services and then put them together.

    How was the initial setup?

    It is my understanding that the initial setup is very straightforward and simple. 

    What's my experience with pricing, setup cost, and licensing?

    The pricing is fine. 

    What other advice do I have?

    I'd rate the solution seven out of ten. It's a pretty good product overall. 

    Which deployment model are you using for this solution?

    Public Cloud
    Disclosure: My company has a business relationship with this vendor other than being a customer: Partner
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    ANANDA KANCHARLA - PeerSpot reviewer
    Program Manager at Avows
    MSP
    A fully-managed, serverless data warehouse with a useful machine learning feature
    Pros and Cons
    • "I like that we can synch and run a large query. I also like that we can work with a large amount of data. You don't need to work separately, as it's a ready-made solution. It also comes with a built-in machine-learning feature. Once we start inputting the data, it will suggest some things related to the data, and we can come up with nice dashboards and statistics from a vast amount of data."
    • "The price could be better. Compared to competing solutions, BigQuery is expensive. It's only suitable for enterprise customers, not small and medium-sized businesses, as they cannot afford this kind of solution. In the next release, it would be better if they improved their AI bot. Although machine learning and artificial intelligence are doing wonders, there is still a lot of room to enhance them."

    What is most valuable?

    I like that we can synch and run a large query. I also like that we can work with a large amount of data. You don't need to work separately, as it's a ready-made solution. It also comes with a built-in machine-learning feature. Once we start inputting the data, it will suggest some things related to the data, and we can come up with nice dashboards and statistics from a vast amount of data.

    What needs improvement?

    The price could be better. Compared to competing solutions, BigQuery is expensive. It's only suitable for enterprise customers, not small and medium-sized businesses, as they cannot afford this kind of solution. 

    In the next release, it would be better if they improved their AI bot. Although machine learning and artificial intelligence are doing wonders, there is still a lot of room to enhance them.

    For how long have I used the solution?

    I have been working with BigQuery for two and a half years.

    What do I think about the stability of the solution?

    BigQuery is a stable solution.

    What do I think about the scalability of the solution?

    BigQuery is a scalable solution. At present, we have about five different users using this solution. But BigQuery is handling the data of 3,000,000 customers.

    How are customer service and support?

    We subscribed to technical support from Google. Whenever my team finds an issue, they contact support. I did not get a chance to contact the support team because we never had any difficulties or glitches while configuring it.

    How was the initial setup?

    The initial setup is relatively straightforward. It's not simple, and it's not very complex. We are doing maintenance of our regular cloud services and working with some assistants and microservice architecture. I don't think we have ever set up in less than one day.

    What about the implementation team?

    We implemented this solution.

    What's my experience with pricing, setup cost, and licensing?

    The price could be better. Usually, you need to buy the license for a year. Whenever you want more, you can subscribe to it, and you can use it. Otherwise, you can terminate the license. You can use it daily or monthly, and we use it based on a project's requirements.

    What other advice do I have?

    On a scale from one to ten, I would give BigQuery a nine.

    Which deployment model are you using for this solution?

    Public Cloud

    If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

    Google
    Disclosure: My company has a business relationship with this vendor other than being a customer: Partner
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