Google BigQuery

Google BigQuery

About TrustRadius Scoring
Score 9.0 out of 100
Google BigQuery

Overview

Recent Reviews

BigQuery = Big Win

9 out of 10
April 26, 2021
BigQuery (along with Airflow) has become a critical part of our technology stack. It is being used to support the ingestion of large …
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Reviewer Sentiment

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Awards

TrustRadius Award Top Rated 2020

Popular Features

View all 6 features

Database scalability (29)

9.8
98%

Automated backups (24)

9.4
94%

Database security provisions (24)

9.2
92%

Monitoring and metrics (25)

8.3
83%

Reviewer Pros & Cons

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Pricing

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Queries (Hourly Flex Slots)

$4

Cloud
per 100 slots

Queries (On-Demand)

$5

Cloud
per TB

Queries (Annual Flat Rate)

$1,700

Cloud
per 100 slots

Entry-level set up fee?

  • No setup fee

Offerings

  • Free Trial
  • Free/Freemium Version
  • Premium Consulting / Integration Services

Features Scorecard

Database-as-a-Service

9.2
92%

Product Details

What is Google BigQuery?

Google's BigQuery is part of the Google Cloud Platform, a database-as-a-service (DBaaS) supporting the querying and rapid analysis of enterprise data.

Google BigQuery Technical Details

Deployment TypesSaaS
Operating SystemsUnspecified
Mobile ApplicationNo

Comparisons

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Frequently Asked Questions

What is Google BigQuery?

Google's BigQuery is part of the Google Cloud Platform, a database-as-a-service (DBaaS) supporting the querying and rapid analysis of enterprise data.

What is Google BigQuery's best feature?

Reviewers rate Database scalability highest, with a score of 9.8.

Who uses Google BigQuery?

The most common users of Google BigQuery are from Mid-sized Companies (51-1,000 employees) and the Computer Software industry.

Reviews

(1-25 of 29)
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Score 10 out of 10
Vetted Review
Verified User
Review Source
We use BigQuery as the company's Data Warehousing tool. The transactional information is handled mostly in Firebase and we inform BigQuery of each update or creation event from which we build the status and history tables. In addition, we use it to consolidate data from other external sources, such as Facebook, Analytics, Google Ads, among others.
  • Data Warehousing
  • Sporadic SQL queries without having to manage instances
  • Reporting directly consumed from views
  • A better output data exploration tool
  • Better handling of parquet files
  • An own application with a more comfortable UI than in the browser
An excellent SQL data management tool without having to manage instances or maintain clusters that need to scale. For a startup or organization that wants to implement a data warehouse logic, it is one of the fastest ways to implement, cheapest to maintain and simple to use that I know of in the market.
March 08, 2022

Data Powerhouse

Score 9 out of 10
Vetted Review
Verified User
Review Source
Google BigQuery adds to the data layer between Google Analytics and PowerBI. We have a lot of Web behaviour data being tracked in Google Analytics which needs to be pulled into a BI system for better reporting. With native PowerBI connector for GA, we were getting sampled data not giving us accurate results. Hence the need to move to Google BigQuery.
  • Google BigQuery can store a large set of dataset at most granular level
  • Query processing speed is exceptional compared to traditional SQL servers.
  • It is easy to setup and manage for someone with less technical knowledge on Data Engineering front
  • The syntax differences for using Google BigQuery can be annoying sometimes.
  • Cost is too high (pay per query) so you have to be careful while running any query
  • Configuration is fairly standard with limited customisation on data schema
If you are using Google suites like Analytics 360, Big Query can be a very useful tool for storing & querying your data because of its seem less integration within Google environment. Also if your organisation has large set of data and you want fast data processing, then only it makes sense to move to Google BigQuery otherwise there are cheaper alternatives available.
Richard Atkins | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Review Source
Data warehousing. Streaming and batch ingest of files and APIs. Implementing business logic, combining data from different sources, reformatting, reporting, and optimization automation
  • Standard SQL
  • Scale
  • RDBMS-like features
  • Python library support
  • Reliability
  • Python library authentication simplification
  • multi-transaction ACID compliance
Well; data warehousing transformation flexible ingestion
The web UI, general features, libraries, and integration with other products is continuously improving and already very strong
Score 10 out of 10
Vetted Review
Verified User
Review Source
Used to deploy this solutioning to the client by shifting away from traditional data warehouse to cloud data warehouse. It resolves the issue of transparency in terms of payment per month, utilization and on how to allow user level access to different folders. It also allows for full integration with other Google Cloud Platform's components like Compute Engine and PubSub.
  • Transparency in terms of cost
  • Utilisation of the data warehouse and suggestion on the sizes
  • Easy to use and integration with other components
  • UiUX features can be improved further in terms of navigating from one folder to another
I would say that Google BigQuery are well suited for all scenarios, be it small scale projects or big projects where you have to maintain a huge chunk of data, you will find good budget to go with it. Easy to use for someone who is not well versed with cloud platform too.
Google Support members are very helpful in resolving the issues and queries. Any questions or queries will be entertained at a timely manner with professionalism, as well as tips and improvement that can be done for the proposed solutioning. In the case that some functionalities are not present within Google BigQuery, they are more than happy to admit the limitation and would like feedback for improvement.
Score 10 out of 10
Vetted Review
Verified User
Review Source
We use BigQuery to store extremely granular data within our organization. This data is then aggregated to provide very detailed reports to our end users and employees.
  • Reliable
  • Fast
  • Extremely powerfull
  • Need more partitions than just dates
  • Would like to chose which partition we insert into
BigQuery is an extremely powerful tool to store granular data. We have tables with trillions of rows and BigQuery has proven to be extremely reliable over time. For organizations that require a very reliable datastore, BigQuery is an excellent choice. The price is also very reliable given the amount of data we store.
Score 10 out of 10
Vetted Review
Verified User
Review Source
Google Big Query is used by product and services department at my organization. It is used to maintain the various services like bookstore, market place, recreation etc. It is used to maintain the information about inventory, the various vendors and product details. Since it is serverless and can handle large datasets it gives us quick results , work with real time data and helps to handle transactional data.
  • Google BigQuery is column based, therefore it has high speed and easily accessible.
  • As I work with inventory related data, it gives me real time updates which helps to resolve many blocks which could cause problems if delayed.
  • Being serverless, it is easy to handle large size data.
  • Google BigQuery charge according to the quality of the code. So if it is long and lengthy and not the most efficient it can be costly.
  • The UI/UX is little difficult to use at the beginning on a small screen because of the layout.
Google BigQuery is suitable for scenarios where the dataset is large and needs to be analyzed based on real time data. Google BigQuer has been very useful when I was working on the inventory data for the bookstore. At the beginning of semester there was always high demand for school materials, this high demand caused a steady decline in the inventory. Getting updated real time helped us to restock the warehouse beforehand with products in higher demand and thereby led to higher sales.
Score 9 out of 10
Vetted Review
Verified User
Review Source
As a Data Analyst at my previous company, I dealt primarily with large datasets. Being able to retrieve that data was an important aspect of my job and Google BigQuery made it a lot easier for me. Having worked with the Operations team, I had to use the data in place to find out patterns in them and retrieve a large amount of sales data. At my former organization, BigQuery was used across the organization and it helped a lot to keep all our data at once place with easy access control.
  • Huge plus point if you have idea running SQL scripts.
  • The ability to store and manage multiple data warehouses is a big plus point which helps a lot for growing businesses.
  • Easy integration with tools like Data Studio and Google Analytics which provides great data warehouse and data management solutions.
  • Can't use it out of Google's cloud platform which is a minus point if you want a local setup.
  • Can be a little expensive to manage.
  • A little difficult to manage someone with less technical expertise as it requires you to have SQL knowledge of joins, CTEs etc.
One of the most important aspects while working with data warehousing solutions and analytics is the ability to handle large datasets. Google BigQuery is the best in business for that particular aspect. It is ridiculously fast while handling large data sets. Another aspect where it is well suited is the ability to integrate it with data visualization tools like Data Studio. It is fast, easy to use, and very reliable. The only aspect where I feel it is less appropriate where you have to pay more of inefficient scripts and that can hamper the growth of the company a bit.
April 26, 2021

BigQuery = Big Win

Score 9 out of 10
Vetted Review
Verified User
Review Source
BigQuery (along with Airflow) has become a critical part of our technology stack. It is being used to support the ingestion of large amounts of data, manipulating and consolidating that data, and then making it available for other aspects of our technology. The data is at a very large scale and more traditional data stores simply do not have the required performance. For example, some of the same processes if done using a more traditional relational database take hours whereas by utilizing the power of BigQuery take under 1 minute.
  • Performance at scale.
  • Console interface is a little clunky.
If you are dealing with very large data sets that require analysis or other manipulation, BigQuery is usually well suited for the task. It also has some built-in ML capabilities that may be of use to some people. If your data set is not very large and is relational in nature, then a more traditional data store is probably all you need, which can likely be used at a lower cost.
Score 8 out of 10
Vetted Review
Verified User
Review Source
Google BigQuery is being used to analyze click-stream data-set in conjunction with structured data-set. It is being used in the sales and marketing departments to essentially attribute new customer acquisition and existing case sales to specific sales representatives, sales divisions, and marketing campaigns. This attribution analysis using Google BigQuery tool has enabled my organization to measure return on investment of various sales and marketing initiatives. Such as training of sales representatives to help their customers adopt digital shopping tools, email, social, and online ordering banner campaigns to target specific customers and regions where we have distribution centers or stores and paid search ads. To accelerate the rate at which we are acquiring new customers who have never shopped before through our online food service business.
  • Google BigQuery serves as a complete big data warehouse solution to quickly access marketing and sales data in one place.
  • Google BigQuery enables analysts to pull correlated data streams by running SQL like queries, so they don't have to query multiple analytics tools.
  • Google BigQuery queries need to be optimized to avoid high costs when pulling data.
  • Google BigQuery needs knowledge of SQL coding to leverage its data analysis capabilities.
Google BigQuery works well for enterprise organizations that have sufficient IT resources to implement its integration and data governance requirements. For example, if an organization is a billion-dollar food distributor, and it wants to run quick queries against large data warehouses to pull correlated sales and marketing reports. So, it can show return on investment driven by training initiatives and marketing campaigns to lift new customer acquisition rates and incremental case purchases. Google BigQuery is less appropriate to use in small businesses where data volume is low, and IT resources are not enough to maintain data quality or run SQL queries. Example: If a company requires to report eCommerce sales from digital-only marketing campaigns where audience size is a few hundred customers, Google BigQuery may not be needed. Instead, Google Analytics or Adobe Analytics will suffice.
I rated the overall support for Google BigQuery as a mediocre five because it has limited support from Google. Instead, it is heavily dependent on an organization's IT resources such as SQL analysts and Data Architects to run big data reports or maintain data quality. Additionally, if errors occur during a run of complex SQL queries or when sending data to Google BigQuery from other sources, Google provides basic email support which needs to be complemented with internal data warehouse support to fix the root cause of the database errors. Finally, due to constraints on the amount of data an analyst can query or pay the additional cost when exceeding the limit, basic Google support is not sufficient to meet data needs without interruption.
Manjeet Singh | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Review Source
It is our main data warehouse. It contains raw data and aggregated data. This is also used for aggregation by running scheduled queries on it. With flat pricing, we are able to optimally use it for aggregation, storage, and exploration. BI tools use BigQuery for data exploration.
  • Query performance is awesome.
  • Fully managed.
  • Can be used for all batch jobs or aggregations.
  • Query pricing is still higher if we don't take flat pricing which is high.
  • Storage pricing is also counted.
  • It do not handle external dependencies.
BigQuery is best if we need a fully managed data warehouse with the fastest querying support. The high price may be a point of concern for some.
We use to get issues on BigQuery even though its not frequent. Retrying works most of times
Cameron Gable | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Review Source
BigQuery is used by our Data Science team to do complex queries on large datasets. We have hundreds of terabytes of data and needed a scalable solution that would be able to query our entire biological dataset. BigQuery plays a crucial role in our data lake made up of several Google Cloud data solutions.
  • Highly scalable data warehouse
  • Easily integrated into analytics tools like Data Studio
  • Easy to use with SQL support
  • Can be pricey. There are ways to lower costs but they aren't always straightforward.
We have several hundred terabytes of data and the size of our dataset is exponentially increasing. We needed a data warehouse that is highly scalable. We also serve a user base with several dashboards. BigQuery is great because it integrates nicely with Google Data Studio and other analytics products.
We haven't needed support as of yet.
Jose F. Gomez | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Review Source
Google BigQuery is being used as a data warehousing tool so that we can run analytics and calculate business metrics on our data. It’s very comprehensive and powerful yet provides tools to simplify recurrent queries on the data that makes working with it easy enough. We like that it’s a place where multiple people from interdisciplinary teams can converge on the data and work on finding ways to improve and better understand our business.
  • It provides a central data storage regardless of the data source.
  • It features functionality that makes it easy to store and re-run queries.
  • It can be overwhelming to non-technical users at first.
  • You can easily get confused as to what to do to start if not familiarized with the workflow.
Google BigQuery is an especially powerful tool for teams running a business that collects data in multiple tools and want to have a place to centralize all of the data for process analysis. Teams that want to learn the insights of every aspect of their processes and their performance can store their processes data in Google BigQuery and then create queries and store them as needed then run experiments or test scenarios and measure outcomes easily.
It’s Google, they’re big and well organized, the documentation is abundant and the scalability is amazing. The UX is good too, considering it’s a professional tool expected to be used by people with a specific technical background. Overall, it makes me feels good and secure that we know where to store the data, how to use that data and that the data is handled with utmost security and performance practices.
Score 10 out of 10
Vetted Review
Verified User
Review Source
Big Query is currently being used by several departments as well as IT to extract data, blend it with other data and to generate reports based on that data. It's being used to track customer journeys through our site, track different channel traffic conversions and to build out dashboards in Tableau.
  • The computing used by BigQuery is dynamically distributed across compute resources so that you do not have to manage compute clusters.
  • Big Query connects easily with Tableau so that you can analyze billions of rows in seconds using visual analysis tools without writing a single line of code.
  • Although BigQuery machine learning gives you the option to control your geographic data, it only applies to the US, Asia, and Europe. Further expansion of this option to other parts of the world would be beneficial.
  • You don’t need to install, provision, or set up anything with Big Query because it is managed. The downside being that you can’t use it outside of Google Cloud Platform.
I use it primarily in place of Google Analytics in Tableau since we use Tableau for all of weekly and monthly reporting and dashboards. One of the many advantages is being able to access unfiltered data and unaggregated data. This allows us to accurately capture measures such as unique users across many different time frequencies, which you can only do at the yearly level with Google Analytics.
BigQuery has exceeded our cloud data warehouse needs with features such as it inherits security features of Google Cloud Platform, it encrypts data by default for added security and Google Cloud DLP service supports BigQuery. Also, BigQuery natively supports streaming data, which is available for real-time analysis within a few seconds of the first streaming insertion into a table.
Score 8 out of 10
Vetted Review
Verified User
Review Source
Google BigQuery is used for data warehouse as a ML analytics engine company wide specifically for consumer behavioral analytics with data streams coming coming out of website as well as internal data sets.
  • It is easy to create and then execute machine learning models in BigQuery using SQL queries using BigQuery ML. Everyone knows SQL.
  • Google BigQuery is fully serverless/cloud based and can be up and running in few hours without need for any specific coding or integration if your data is already is Google Storage.
  • Google BigQuery executes the SQL statements very fast and can can be used for real-time analytics especially if you use Google infrastructure ( GCP).
  • Google BigQuery is great for large data sets where you need a familiar SQL interface but it is still slower than running the same SQL query on RDBMS, assuming your data is mostly structured.
  • It is expensive if you have a lot of data that needs to be queried each time the query is run due to the license metrics used in Google BigQuery.
  • Some of the SQL operations like table join are not optimized and can be slow compared to a full database.
Google BigQuery is very well suited if your data is large and already in Google Cloud/GCP where the data itself is not simple structured data. It is less suited if you have well-defined data sets that may or may not exist in Google Cloud. Google BigQuery is also less suited if you have to analyze the data on a regular basis since the cost of accessing compute and storage adds up considerably.
Online documentation was readily available and it was easy to connected with the product management team for Google BigQuery during Google Cloud Next 2019 event. We didn't have to open a SR/ticket through the usual support channel to get our issues resolved!
Tristan Dobbs | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Reseller
Review Source
Google BigQuery has become our data warehouse for the entirety of the systems that we use. It is incredibly efficient and ridiculously powerful, so it allows us to store all relevant data and query it to build dashboards for management and leadership uses. We connected it directly to a data visualisation tool and it has become the most useful part of our business.
  • BigQuery is ridiculously fast and has the ability to query absurdly large data sets to return results immediately.
  • BigQuery allows for storage of a massive amount of data for relatively low prices.
  • Easy to learn. BiqQuery uses SQL-like queries and is easy to transfer your existing skills to use.
  • BigQuery can be dangerous. The charges can rack up quickly if you don't construct your queries properly. Traverse too much data too frequently and you can cost yourself some money.
BigQuery is unlike anything we've used as a big data tool. It is perfectly suited to query large data sets quickly and to store those large data sets for any time use. It's perfect for storing data and using it for reports. Logging data is the perfect application for BigQuery, but transactional data is possible as well.
BigQuery can be difficult to support because it is so solid as a product. Many of the issues you will see are related to your own data sets, however you may see issues importing data and managing jobs. If this occurs, it can be a challenge to get to speak to the correct person who can help you.
Score 7 out of 10
Vetted Review
Verified User
Review Source
We use BigQuery to manage large datasets we collect in surveys and in regular work projects. Only one person is in charge of it as we are a small company. It works extremely well for my company because it is cloud-based and we do a lot of remote work, so I can access our data and manage things from anywhere. It's a great tool and makes all kinds of data processing and analysis much easier and faster.
  • Cloud storage- always a huge draw for small businesses who may or may not have a bricks-and-mortar office to work from. We can share data easily and access it from anywhere.
  • The user interface is excellent- easy to navigate and conduct whatever specific analyses you want
  • You pay for the data you process, so it's kind of a pay-per-use system. This is awesome for smaller companies who may not need excessive amounts of data processed per month but still need the powerful analytics of a program like BigQuery.
  • Even though the cost is pay-per-use, it's still expensive. This may make the program impractical for companies that won't use it frequently enough or for high-powered processing as it is meant for.
  • Sometimes it is difficult to import data from alternate sources and manage it. The integrations between BQ and other online cloud storage aren't always a smooth transfer.
BigQuery is a huge benefit to companies that work remotely, process large datasets, or need to easily manage those large datasets. It's a powerful tool with cloud storage and the ability to work with large scale datasets. It works well if your monthly usage varies because you can pay for the processing you do- not paying for a minimum that you don't meet. It's not going to be a great option for companies with smaller datasets or who could operate with a less powerful and cheaper system.
As with any large company, support isn't always easy to get to. There are plenty of online tutorials and they have online guides that aren't too difficult to find. For last minute issues, a call is the best option to figure out what your next step should be. it's not a terrible customer service system, especially if you spend an appropriate amount of time beforehand familiarizing yourself with the system. That'll expedite any further questions you have down the road and make the support a much bigger help.
Sam Lepak | TrustRadius Reviewer
Score 5 out of 10
Vetted Review
Verified User
Review Source
Our marketing team and product development team BigQuery. This is my favorite software for storing information in the cloud, I use it both personally and at work and I recommend it because it has allowed me to access my information very quickly, so far it seems to me that security is very good and not I have had problems with this aspect, although it can work very slow when the Internet connection is not very good, it allows to resume file uploads instead of restarting them every time the signal decreases.
  • How many pros can a person type? This storage program gives workers and students the reality of unlimited storage space. I have never came close to overfilling my google cloud storage because it's huge and the best. I can view anything I save on there from any of my internet devices which is very important.
  • Depending on how you have the program set up - either online or through an application that lives on your desktop, dragging and dropping files to and from Cloud Storage couldn't be any more uncomplicated. Plus, new users who meet certain criteria - like updating personal security, or share the program receive additional free online storage.
  • The array of tools is very impressive, intuitive to use, and well organized in the sense that you don't have to go looking for individual apps. They're all easily accessed via a single dropdown.
  • One issue with Google Cloud Storage is its price. For one to have that premium Google Cloud Storage, for the purpose of massive storage, he/she must have adequate cash. Otherwise, Google Cloud Storage is a safe and perfect online storage platform.
  • The only thing that can come to mind that would be annoying with this software was that sometimes when trying to share files on the Cloud with coworkers, it would just not share at all, or there would be a massive delay in when I shared them and when they received them. Other than that though, everything is perfect with this.
I recommend this platform for wide range of customers that have not super tight budget for their application hosting but want to stay away from bunch of low-level details of running and maintenance of application infrastructure. Google BigQuery is easy to use and its interface is very nice, it also has a wide range of servers, which makes its services are excellent. This software has allowed me to easily access my files and share them quickly and efficiently, it also allows other activities while loading and downloading files, therefore saving a lot of time compared to other similar applications.
Evan Laird | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
We use Google BigQuery to mine through further data that Firebase doesn't allow us to. It's been extremely scalable and robust for our SQL and backend developers to mine through and get detailed location data on our users so we can find out where our most active cities are in the United States.
  • Scalable.
  • Love that it uses SQL.
  • Low-cost.
  • Easy integration with Firebase.
  • UI/UX is a bit scary right away.
  • Takes a strong learning curve to get used to.
Suited to any company, small or large (as it's extremely scalable and low cost as it scales), that wants or needs to dive into data to make more data-driven decisions or back up decisions with user data. The team should have someone that is well versed in SQL though, as non-technical team members will be a bit lost.
Spencer Baselice | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User
Review Source
BigQuery is in use across the entire organization in various departments and businesses for multiple purposes. It is used to store mass data and analytics from web statistics to business data. It is a data warehouse of sorts where different teams are given access to the platform through a central user management base and each team's sandbox contains relevant data to their function.
  • BigQuery integrates well with other platforms, for instance, Knime and can be connected to other data visualization or manipulation programs.
  • It is easy to use with multiple users and teams and creating areas for users of different levels or types is fairly easy to manage.
  • Integrates well with Cloud and allows you to export large amounts of data.
  • The user interface is easy to use and enables SQL and data querying similar to a database.
  • Some of the SQL you can execute in a database is not exectuable in BigQuery which limits how much you can do right inside the platform. However, most of what you can do in a database is doable in BigQuery itself.
  • Charting and other data visualization working with the data inside of BigQuery could be an improvement
  • The legacy and non-legacy SQL was a little confusing and some of the SQL functions did not always allow us to do the things we wanted to do
BigQuery is well suited for organizations that use a lot of data across lots of teams or departments. It is perfect for those companies who need various data dumps or data storage areas for different parts of the company, where the data storage is flexible and easily accessible for everyone. It is also a cost-effective method from what I understand, so if your company needs to enable teams to have better access to larger amounts of data storage and databases BigQuery is a logical option.
Score 6 out of 10
Vetted Review
Verified User
Review Source
In our organization, Google BigQuery is for storing very large data which is created within seconds. We log each and every event done by any user. We also log data like payment status, order status, and user address details. Basically, all of the information is logged. To sort through it we are using BigQuery as it is fast and provides data to us within miliseconds.
  • It is faster than the product we use for our websites, MySQL.
  • Can query millions of rows within seconds and can give you the data very fast.
  • Documentation should be detailed. I had a very hard time learning it. My seniors are also facing so many hurdles while using this.
  • No proper flow is mentioned in the docs about how to use this product. We faced so many errors at different stages.
Google BigQuery is well suited for millions of records as you can run a query in milliseconds. It is less appropriate for small scale organizations which are dealing with a smaller amount of data.
Gaurav Gautam | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
BigQuery is a user platform designed to ingest, process and output large volumes of data. We use it to ingest data from Google Analytics and collate that data with other internal data systems by pushing them to BigQuery. BigQuery makes it possible to process a huge volume of data sessions and user levels in almost real-time and achieve personalization and optimized UX.
  • Processing of huge volumes of data enabled us to provide strategic insights by understanding the facts and realities.
  • Detailed Audience analysis enabled us to achieve better targeting for digital media and marketing campaigns
  • Personalization: We are able to achieve personalization by marrying, stitching, and processing huge volume of data.
  • SQL syntax is not exactly same as ANSI SQL so there is a learning curve. Traditional SQL queries cannot execute in BigQuery which limits portabiltiy of the code.
  • Limitation on visualization: We can improve visualization in data studio by bringing in the ability to support complex functions/formulas such as Tableau can do.
BigQuery's main strength is its ability to process huge volumes of data with lightning speed, and also perform personal detailed analysis on web analytics data or continuous streams of piles of data and then link it directly to data studio.
Score 8 out of 10
Vetted Review
Verified User
Review Source
We are the reseller of Google Analytics and with Google Analytics premium you get Big Query. You get 500$ credit to use in Big Query. Big Query is a great tool to get unsampled reports, that can be further used for different analysis also to build products on top of it. Big Query can help you to analyze user journey, enhanced eCommerce data for creating remarketing audience. You just need to know SQL and you can use Big Query to get whatever data you want. Big Query can be further utilized for your own purpose, you can upload your CRM data and map with Google Analytics data.
  • Big Query is fast and based on the cloud you can run your query on a huge dataset. Huge means data in TB's. This also reduces the company cost to build that kind of infrastructure to store data.
  • Not specific to Google Analytics but you can import data from different sources for analysis purpose and use the power of the cloud to run the query.
  • Not much time to learn - You don't need any special skills, just SQL and you can use Big Query for your use. Learning SQL is not a big task you can learn it in a week.
  • Big Query refrence schema and different sample query are available to practice on queries.
  • Google also provide sample dataset to use then purchase Big Query.
  • Though it is SQL some syntax are different but they are getting used to after you use for some time.
  • The legacy SQL is in beta state but can be used and you can run the query with simple SQL.
  • More documentation is needed for using User-defined functions in Big Query.
- If you are using Google Analytics and there is huge data that is getting streamed every day then you must have Big Query and use it for analysis. It is not only helpful for analysis but also for debugging your Google Analytics implementations.
- For analyzing a small dataset you don't need Big Query you can use normal MySQL on your own premises. Analyzing on Un-structured data is not possible with Big Query.
Anatoly Geyfman | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Review Source
We use BigQuery as our data warehouse. Meaning, we use BigQuery (BQ) for storing our data, aggregating it and creating pipelines to push data into BQ, and take aggregates out of BQ in order to push them into ElasticSearch. We use it across our whole organization, and most of our data pipelines are now natively using BQ. For us, BQ helped us scale beyond Postgres for very large data sets in a convenient and most importantly, inexpensive way.
  • BigQuery integrates exceptionally well with Google Storage. All you have to do is push a CSV to Google Storage, and add it to BQ. BQ will try to detect the schema and import the CSV as a table. The process is very quick.
  • There are lots of ways to interact with BQ. Besides the web interface, there are also SDKs you can use to interface with bigquery from your tools. Meaning, it's not just data stuck in the cloud.
  • BigQuery lets you search extremely large datasets, quickly. We have many 100m+ datasets loaded, and searching any number of fields through them is not only easy (SQL!) but fast as well (most queries finish < 30 seconds). It's not a real-time system, but for OLAP, it's unbeatable.
  • It would be awesome to have BQ be real-time. Right now it serves the OLAP use case very well, but interactive would be great too.
  • The user interface is not the best we've used.
  • We'd love to have the Standard SQL mode be on by default.
BigQuery is best of OLAP. It's not a real-time system, so you shouldn't expect it to search through your billion records in 2 seconds. We use it to store raw, unaggregated data. For this use case, it's perfect, since the storage costs are low and the performance is more than good enough. BigQuery is also great for building data pipelines. It has convenient SDK to get data in and out of it, and SQL to marshall the data any way you want.
Score 8 out of 10
Vetted Review
Verified User
Review Source
BigQuery is used to access the data collected through Google Analytics and merge that information with data from other business systems. The integration of BigQuery and Google Analytics made this an efficient approach to developing and maintaining data used in the organization's business intelligence reporting along with deep-dive analysis into advertising performance.
  • Quickly query summary metrics from time-series data
  • Integration with other Google Cloud products
  • Scalability to handle unpredictable changes in data volumes
  • Integrating with data outside of Google Cloud can be slow
  • Data and the related queries are structured differently than in SQL Server, so there is some training necessary before broadly adopting
  • Understanding the pricing of various queries can be a challenge. Figuring out what makes a query more expensive than another takes time.
Google BigQuery is well suited to applications where the data is coming from another Google Cloud product and the data will be used in frequent ad hoc queries. The performance of BigQuery on ad hoc queries makes it a good source for business intelligence applications. Additionally, automating repeated queries and common workflows with Google App Scripts is a good application with BigQuery.
Dmitry Sadovnychyi | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
[It's being used for] extracting patterns from big amounts of data (millions of rows) with complex aggregations.
  • Working with big data
  • Performance of both streaming and batch queries
  • Easy to use if you are familiar with SQL
  • Performance could always be improved
It works well for a big dataset starting from hundreds of GB. I wouldn't recommend using it for people with less than 100 GB in data – except when you expect to grow your dataset in the near future. It's also not really good to directly answer on live requests, it's much better to use it to pre-process some data, store it somewhere else, and serve it from there.