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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.Google 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,9,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 ExcelData 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, PostgreSQLFast 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 processingBigQuery 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
62 Ratings
Score 8.8 out of 101
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Google BigQuery Reviews

Google BigQuery
62 Ratings
Score 8.8 out of 101
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Spencer Baselice profile photo
January 31, 2018

Review: "Google BigQuery, Ideal for Large Companies with Multiple Teams"

Spencer Baselice
Score 9 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
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November 21, 2017

Review: "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

Google BigQuery Review: "BigQuery is a game changer for OLAP"

Anatoly Geyfman
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.
Read Anatoly Geyfman's full review
Dmitry Sadovnychyi profile photo
February 23, 2017

Google BigQuery Review: "Fast and scalable database for big data"

Dmitry Sadovnychyi
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.
Read Dmitry Sadovnychyi's full review
No photo available
March 31, 2017

Google BigQuery Review: "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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Alex Andrews profile photo
October 26, 2016

Review: "Google BigQuery truly democratizes data"

Alex Andrews
Score 9 out of 10
Vetted Review
Verified User
Review Source
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.
Read Alex Andrews's full review
Csaba Toth profile photo
November 20, 2015

Google BigQuery Review: "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).


Read Csaba Toth's full review
Charles Chao profile photo
November 18, 2015

Google BigQuery Review: "Great for Interactive Analytics And KPI Reports"

Charles Chao
Score 9 out of 10
Vetted Review
Verified User
Review Source
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.
Read Charles Chao's full review
Reza Qorbani profile photo
February 19, 2016

Google BigQuery Review: "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.
Read Reza Qorbani's full review

Google BigQuery Scorecard Summary

Likelihood to Recommend (62)
8.8

Feature Scorecard Summary

Automatic software patching (3)
10.0
Database scalability (9)
8.6
Automated backups (6)
8.7
Database security provisions (6)
8.2
Monitoring and metrics (7)
5.4
Automatic host deployment (3)
9.2

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