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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.Need a reliable, in-expensive database? BigQuery is here to help!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.,5,Pricing has been very reasonable for us. The first 10 GB of storage is free each month and costs start at 2 cents per GB per month after that. For example, if you store 1 terabyte (TB) for a month, then the cost would be $20. Streaming data inserts start at 1 cent per 200 megabytes (MBs). The first 1 TB of queries is free, with additional analysis at $5 per TB thereafter. Meta data operations are free. Big Query helps reduce the bar for data analytics, ML and AI. BQ takes care of mundane tasks and streamlines for easy data processing, consumption. The most impressive thing is the ML and AI integration as SQL functions, so the need for moving data around is minimized. The visuals of ML models is very helpful to fine tune training, model building and prediction, etc.,FirebaseDive Deeper into your Firebase DataWe 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.,9,Helped us focus on better locations for restaurants, which increased our conversion rate and quality of restaurants and locations, which we believe has led to more reservations. Lowered our cost of restaurant acquisition with the sales team by about 20%.,Firebase,AWeber, ActiveCampaign, Mailchimp, Google AuthenticatorBigQuery- A big tool with a lot to offerWe 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.,7,BigQuery has been a great benefit for managing large datasets. We collect quite a bit of data in our line of work and it's difficult to find program that can easily manage that as well as have it accessible via cloud storage. The cost is high, even though you only pay for what you use. Ideally the cost would be lower for certain usage ranges but for now, it's difficult for a small company to justify the cost even with such large scale data.,9,9,Affinity, Hetman Photo Recovery, Adobe ConnectBigQuery = Big WinBigQuery (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.,9,Once up and running, we no longer have to worry about scale and managing infrastructure. Instead of spending our time on our analytics and bringing that value to our customers.,,Google App Engine, Google Cloud SQL, Google Compute EngineGoogle BigQuery, Ideal for Large Companies with Multiple TeamsBigQuery 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,7,It helps us to manage and understand the larger breadth of our Search Engine presence using data from our Search Console API. This helps us locate and track issues and improvements. Using BgiQuery also improves the access to multiple types of data that we use for tracking performance and monitoring changes in our competitive position in the SERPs. It allows us a faster, easier way to manage and store large databases without having to host them on a local machine or another cloud.,KNIME Analytics Platform,BrightEdge, Moz, Ahrefs Site Explorer, Majestic SEO, SEO PowerSuite, Adobe Analytics, Slack, Snagit, JIRA Software, Google Search Console, Google Analytics, Google Cloud Storage, SEOGadget for ExcelAnalyze data at lightening speed @BigQueryBigQuery 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.,9,By using BigQuery for visualization and personalization, we were able to achieve 5% higher conversion rate.,,Amazon Redshift, Azure SQL DatabaseGoogle BigQuery: Slow Learning CurveIn 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.,6,ROI is very small although it's positive. Still, I am not happy with this product as it took five to six days to understand the flow of the product and how to implement it into our project, whereas other database systems would have taken one to two days.,MySQL,MongoDB, React, GitHub, Bitbucket, JIRA Software, WooCommerce, OpenCart, Shopify, WordPressData Analysis on Steroids with Google Big QueryWe 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.,8,Generating remarketing audience from Google Analytics data. Reducing the cost for storage infrastructure Comparability with different data sources and more depth analysis and efficient decision making,Google Analytics, Google Cloud Storage, Amazon Web ServicesBigQuery is a game changer for OLAPWe 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.,10,We were able to reduce our investment in self-hosted Postgres and move our bigger data assets to BigQuery. Overall, we saved hundreds per month, thousands per year. We are able to search through very large datasets with BQ that were difficult to search with standard Postgres, even on very large servers. This gave us the ability to do ad-hoc data introspection easily. Because we already used Google Storage, it was easy to integrate BQ into our environment.,Amazon Redshift,Microsoft Azure, Google Cloud Storage, PostgreSQLGoogle BigQuery truly democratizes dataGoogle BigQuery has become the de facto analytics warehouse for our organization. It has allowed us to scale effectively into massive datasets when our internal, physical database could no longer handle these types of workloads. BigQuery is being used by numerous areas within our organization, including my team (Solution Architecture), our internal ETL team, as well as our Advanced Analytics team. BigQuery truly democratizes data access and processing power to anyone that can understand SQL, and has allowed our internal teams to increase the efficiency with which ad hoc analyses can be accomplished on very large datasets.,BigQuery is a highly optimized, columnar oriented database, and as such it exceeds when doing complex aggregations over massive datasets, i.e. computing n-tiles, statistics, sorting, etc. BigQuery is seamlessly integrated with the rest of the Google Cloud Platform stack, and as such it is extremely easy to move data in and out of BigQuery for analysis and storage. However, it also exposes very well defined APIs for inserting and streaming data in, and as such can be used easily with other on-premeses or cloud solutions. Because BigQuery is fully managed, there is no need to think about provisioning machines, optimizing memory/cores, 'vacuuming', etc. This increases the 'democratization' effect BigQuery can have, as a basic knowledge of SQL is all that is needed to get started.,BigQuery does impose quite a few limits on the higher end queries, although they are entirely understandable. For example, very large 'GROUP BY' clauses can sometimes fail with a "Resources Exceeded" error, as the distributed computational nature of BigQuery forces all of that data to be compiled on a single machine, and when that machine runs out of memory it throws the aforementioned error. You can increase your Billing Tier to complete these queries, though. When getting data out of BigQuery, there are also quite a few limits. For example, if you are returning a large result set, you are essentially forced to write the results to a table and then export that table to Google Cloud Storage to then be downloaded. However, during the export process, if the table is large, Google will split that table into many smaller blocks that need to be reassembled.,9,Google BigQuery has had enormous impact in terms of ROI to our business, as it has allowed us to ease our dependence on our physical servers, which we pay for monthly from another hosting service. We have been able to run multiple enterprise scale data processing applications with almost no investment Since our business is highly client focused, Google Cloud Platform, and BigQuery specifically, has allowed us to get very granular in how our usage should be attributed to different projects, clients, and teams. Plain and simple, I believe the meager investments that we have made in Google BigQuery have paid themselves back hundreds of times over.,Amazon Redshift and Microsoft SQL Server,Google Cloud Storage, Google Compute Engine, Google HangoutsFast and scalable database for big data[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,9,Faster data processingEvaluation of BigQuery from a Hadoop viewpointI evaluated and presented introduction to Google BigQuery for the Fresno Google Developer Group technology meetup and also at Google DevFest West conference. I tried several publicly available datasets, followed several sample queries, studied BigQuery specific instructions. ALso took a look at Google Genomics and its public datasets.,The web console provides extremely simple interface for test and try. REST API provides capability for integrating with software solutions. The web interface provides useful features like query history, named/saved queries, export results.,If accidentally the return dataset would be humongous (you forget to LIMIT), you cannot really stop a running query, and it'll probably be billed,10,If BigQuery fits your business needs (see best scenarios) it can yield great ROI. You maybe able to answer questions with ease which would take much more effort with other big data query technologies (HIVE, Spark, ...). You miay pay some costs. Following some best practices (how to construct and limit your queries) can decrease your costs.,Great for Interactive Analytics And KPI ReportsWe use BigQuery in our engineering team to do fast analytical queries and generate many reports for the management team. Many of those reports were not possible with our existing data platform because of the time needed to create those reports, and the compute resource required. Google BigQuery solved those problems and enabled our management to access KPI reports in much shorter time.,It's capable of scanning billions of records in a couple of seconds. It makes it possible to create hundreds of KPIs in less than an hour. Google BigQuery provides the compute power when you need it. For a startup company, BlueCava cannot afford the massive compute power required for the reports we'd like to create, and BigQuery makes this available. The best part, Google BigQuery is charged per query, and based on the size of data the query scans. No extra cost.,Documentation is not complete, sometimes not clear. Performance is unstable occasionally. Error message not clear.,9,Increased employee efficiency: interactive analytical queries can run much faster on BigQuery, problems are discovered/identified quicker. KPI reports are delivered to management much quicker. Overall reduced cost for BlueCava.,,MapR, Apache Spark, Apache Pig, Apache Hive, Couchbase ServerBigQuery can change your business!We are Big Data company who dealing with petabytes of data and we generate hierarchal data that can be used for Advertising companies. Our customer wanted to query this data via API and extract results where they can have access. Since we are dealing with Terabytes of data to query at any time, we either had option to build 247 infrastructure to support our API requests which would cost us hundreds of thousands of dollar to build, or use BigQuery! In Our use-case BigQuery not only saved us lots of money but also improve our delivery by almost 100x which was more than what our customers needed. Now our customers can query and extract their data via our API to BigQuery in matter of minutes while previously was in multiple hours!,No need to maintain any infrastructure Exteremly cheap while easy to understand pricing IT IS FAST!!!,Authorization is so simplified and hard to maintain different level of security to access to data,10,Absolutely positive ROI! Actually HUGE one in fact. We literally saved more than $1M by just using BigQuery,Snowflate, Amazon Redshift and Apache Spark,Apache Spark, Google App Engine, Google Cloud StorageBigQuery value through integration with Google Analytics PremiumBigQuery 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.,8,Streamlined the use of data from other Google products - particularly Google Analytics Premium Low storage costs allow for a large repository of information to be retained for historical analysis and reporting Management of BigQuery is simple and handled by 1 person internally to integrate with Google Analytics data,Hadoop, PostgreSQL, Microsoft SQL Server, Apache Hive and Cassandra,Tableau Server, Microsoft Power BI, Revolution R Enterprise, Microsoft SQL Server, Apache Spark, Google Analytics Premium, PostgreSQL
  3. Google BigQuery Reviews
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Google BigQuery
87 Ratings
Score 8.3 out of 101
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Google BigQuery Reviews

Google BigQuery
87 Ratings
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Score 8.3 out of 101

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Sam Lepak profile photo
May 08, 2019

Need a reliable, in-expensive database? BigQuery is here to help!

Sam Lepak
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.
Read Sam Lepak's full review
Evan Laird profile photo
May 04, 2019

Dive Deeper into your Firebase Data

Evan Laird
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.
Read Evan Laird's full review
No photo available
August 12, 2019

BigQuery- A big tool with a lot to offer

Verified User
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.
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No photo available
May 21, 2019

BigQuery = Big Win

Verified User
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.
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Spencer Baselice profile photo
February 25, 2019

Google BigQuery, Ideal for Large Companies with Multiple Teams

Spencer Baselice
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.
Read Spencer Baselice's full review
Gaurav Gautam profile photo
November 28, 2018

Analyze data at lightening speed @BigQuery

Gaurav Gautam
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.
Read Gaurav Gautam's full review
No photo available
February 15, 2019

Google BigQuery: Slow Learning Curve

Verified User
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.
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No photo available
November 21, 2017

Data Analysis on Steroids with Google Big Query

Verified User
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.
Read this authenticated review
Anatoly Geyfman profile photo
June 26, 2017

BigQuery is a game changer for OLAP

Anatoly Geyfman
Score 10 out of 10
Vetted Review
Verified User
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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.
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Alex Andrews profile photo
October 26, 2016

Google BigQuery truly democratizes data

Alex Andrews
Score 9 out of 10
Vetted Review
Verified User
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Google BigQuery has become the de facto analytics warehouse for our organization. It has allowed us to scale effectively into massive datasets when our internal, physical database could no longer handle these types of workloads. BigQuery is being used by numerous areas within our organization, including my team (Solution Architecture), our internal ETL team, as well as our Advanced Analytics team. BigQuery truly democratizes data access and processing power to anyone that can understand SQL, and has allowed our internal teams to increase the efficiency with which ad hoc analyses can be accomplished on very large datasets.
  • BigQuery is a highly optimized, columnar oriented database, and as such it exceeds when doing complex aggregations over massive datasets, i.e. computing n-tiles, statistics, sorting, etc.
  • BigQuery is seamlessly integrated with the rest of the Google Cloud Platform stack, and as such it is extremely easy to move data in and out of BigQuery for analysis and storage. However, it also exposes very well defined APIs for inserting and streaming data in, and as such can be used easily with other on-premeses or cloud solutions.
  • Because BigQuery is fully managed, there is no need to think about provisioning machines, optimizing memory/cores, 'vacuuming', etc. This increases the 'democratization' effect BigQuery can have, as a basic knowledge of SQL is all that is needed to get started.
  • BigQuery does impose quite a few limits on the higher end queries, although they are entirely understandable. For example, very large 'GROUP BY' clauses can sometimes fail with a "Resources Exceeded" error, as the distributed computational nature of BigQuery forces all of that data to be compiled on a single machine, and when that machine runs out of memory it throws the aforementioned error. You can increase your Billing Tier to complete these queries, though.
  • When getting data out of BigQuery, there are also quite a few limits. For example, if you are returning a large result set, you are essentially forced to write the results to a table and then export that table to Google Cloud Storage to then be downloaded. However, during the export process, if the table is large, Google will split that table into many smaller blocks that need to be reassembled.
BigQuery is extremely well suited to being a general purpose analytics data warehouse, i.e. if you have large datasets that you wish to extract insights from, and are comfortable with SQL, then BigQuery should be the only place those data live. BigQuery is also extremely well suited to driving enterprise-level dashboards on your actual data, decreasing the deviation of the summarized data from the raw. BigQuery is not as well suited to cases where you hope to return very large datasets, as it is optimized for aggregations.
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Dmitry Sadovnychyi profile photo
February 23, 2017

Fast and scalable database for big data

Dmitry Sadovnychyi
Score 9 out of 10
Vetted Review
Verified User
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[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.
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Csaba Toth profile photo
November 20, 2015

Evaluation of BigQuery from a Hadoop viewpoint

Csaba Toth
Score 10 out of 10
Vetted Review
Verified User
Review Source
I evaluated and presented introduction to Google BigQuery for the Fresno Google Developer Group technology meetup and also at Google DevFest West conference.
I tried several publicly available datasets, followed several sample queries, studied BigQuery specific instructions. ALso took a look at Google Genomics and its public datasets.
  • The web console provides extremely simple interface for test and try.
  • REST API provides capability for integrating with software solutions.
  • The web interface provides useful features like query history, named/saved queries, export results.
  • If accidentally the return dataset would be humongous (you forget to LIMIT), you cannot really stop a running query, and it'll probably be billed
It can be an extremely good fit if:
1. You have data in Google Cloud Storage
2. You don't want to deal with the hassle of spinning up a Hadoop cluster
or you have especially large dataset and you don't want to deal with scaling-out logic. Also, costs might be high.
It's not good for you if you have some specific algorithm which cannot be phrased in the BogQuery SQL flavor.
It maybe unnecessary if near-real-time results are not too important factor, and it doesn't matter if a query returns in 2-3 seconds or 20-30. If you already have some Hadoop infrastructure, HIVE or Spark, your existing solution might be cheaper.
There are best practices which can decrease your costs a lot (for e.g. how many columns your query involves, how well do you filter your data in the query).


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Charles Chao profile photo
November 18, 2015

Great for Interactive Analytics And KPI Reports

Charles Chao
Score 9 out of 10
Vetted Review
Verified User
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We use BigQuery in our engineering team to do fast analytical queries and generate many reports for the management team. Many of those reports were not possible with our existing data platform because of the time needed to create those reports, and the compute resource required. Google BigQuery solved those problems and enabled our management to access KPI reports in much shorter time.
  • It's capable of scanning billions of records in a couple of seconds. It makes it possible to create hundreds of KPIs in less than an hour.
  • Google BigQuery provides the compute power when you need it. For a startup company, BlueCava cannot afford the massive compute power required for the reports we'd like to create, and BigQuery makes this available.
  • The best part, Google BigQuery is charged per query, and based on the size of data the query scans. No extra cost.
  • Documentation is not complete, sometimes not clear.
  • Performance is unstable occasionally.
  • Error message not clear.
It is well suited for generating reports quickly or doing interactive analytical queries over a large data set that contains hundreds of millions or billions of rows. The largest table we used in BigQuery has close to 30 billion rows. It is not suited for ETC processes or data pipeline.
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Reza Qorbani profile photo
February 19, 2016

BigQuery can change your business!

Reza Qorbani
Score 10 out of 10
Vetted Review
Verified User
Review Source
We are Big Data company who dealing with petabytes of data and we generate hierarchal data that can be used for Advertising companies. Our customer wanted to query this data via API and extract results where they can have access. Since we are dealing with Terabytes of data to query at any time, we either had option to build 247 infrastructure to support our API requests which would cost us hundreds of thousands of dollar to build, or use BigQuery!

In Our use-case BigQuery not only saved us lots of money but also improve our delivery by almost 100x which was more than what our customers needed. Now our customers can query and extract their data via our API to BigQuery in matter of minutes while previously was in multiple hours!
  • No need to maintain any infrastructure
  • Exteremly cheap while easy to understand pricing
  • IT IS FAST!!!
  • Authorization is so simplified and hard to maintain different level of security to access to data
BigQuery can be used for trillions of records as well as hundreds of thousands but not sure if it's useful for small set of data. Also in cases that query response needs to be on milliseconds that might be not right solution. Custom UDFs are also supported but very limited and have ittheirs own challenges since it's not SQL and need to write in JavaScript.
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No photo available
March 31, 2017

BigQuery value through integration with Google Analytics Premium

Verified User
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.
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Google BigQuery Scorecard Summary

Likelihood to Recommend (87)
8.3
Usability (1)
9.0
Support (1)
9.0

Feature Scorecard Summary

Automatic software patching (5)
8.5
Database scalability (15)
8.6
Automated backups (11)
7.9
Database security provisions (11)
9.0
Monitoring and metrics (11)
7.8
Automatic host deployment (5)
8.1

About 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.
Categories:  Database-as-a-Service (DBaaS)

Google BigQuery Technical Details

Operating Systems: Unspecified
Mobile Application:No