Google BigQuery Reviews

102 Ratings
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Score 8.6 out of 100

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Reviews (1-22 of 22)

Jose F. Gomez | TrustRadius Reviewer
January 18, 2020

THE tool you need to improve your business

Score 10 out of 10
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Pros and Cons

  • 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.
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Anonymous | TrustRadius Reviewer
February 15, 2020

A Robust Tool for Big Data Analysis

Score 7 out of 10
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Pros and Cons

  • 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.
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Tristan Dobbs | TrustRadius Reviewer
October 15, 2019

Google BigQuery is BIG for our company.

Score 10 out of 10
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Pros and Cons

  • 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.
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Richard Perroset | TrustRadius Reviewer
December 23, 2019

Big Advantages to BigQuery

Score 10 out of 10
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Pros and Cons

  • 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.
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Anonymous | TrustRadius Reviewer
December 20, 2019

Google BigQuery for analyzing large ML datasets using SQL

Score 8 out of 10
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Pros and Cons

  • 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.
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Anonymous | TrustRadius Reviewer
August 12, 2019

BigQuery- A big tool with a lot to offer

Score 7 out of 10
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Pros and Cons

  • 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.
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Sam Lepak | TrustRadius Reviewer
May 08, 2019

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

Score 5 out of 10
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Pros and Cons

  • 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.
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Spencer Baselice | TrustRadius Reviewer
February 25, 2019

Google BigQuery, Ideal for Large Companies with Multiple Teams

Score 7 out of 10
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Pros and Cons

  • 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
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Anonymous | TrustRadius Reviewer
February 15, 2019

Google BigQuery: Slow Learning Curve

Score 6 out of 10
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Pros and Cons

  • 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.
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Gaurav Gautam | TrustRadius Reviewer
November 28, 2018

Analyze data at lightening speed @BigQuery

Score 9 out of 10
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Pros and Cons

  • 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.
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Anatoly Geyfman | TrustRadius Reviewer
June 26, 2017

BigQuery is a game changer for OLAP

Score 10 out of 10
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Pros and Cons

  • 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.
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Alex Andrews | TrustRadius Reviewer
October 26, 2016

Google BigQuery truly democratizes data

Score 9 out of 10
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Pros and Cons

  • 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.
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Csaba Toth | TrustRadius Reviewer
November 20, 2015

Evaluation of BigQuery from a Hadoop viewpoint

Score 10 out of 10
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Pros and Cons

  • 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
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Charles Chao | TrustRadius Reviewer
November 18, 2015

Great for Interactive Analytics And KPI Reports

Score 9 out of 10
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Pros and Cons

  • 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.
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Anonymous | TrustRadius Reviewer
November 21, 2017

Data Analysis on Steroids with Google Big Query

Score 8 out of 10
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Pros and Cons

  • 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.
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Anonymous | TrustRadius Reviewer
March 31, 2017

BigQuery value through integration with Google Analytics Premium

Score 8 out of 10
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Pros and Cons

  • 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.
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Feature Scorecard Summary

Automatic software patching (11)
9.3
Database scalability (22)
9.3
Automated backups (17)
8.6
Database security provisions (17)
8.9
Monitoring and metrics (18)
8.8
Automatic host deployment (9)
8.9

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.

Google BigQuery Technical Details

Operating Systems: Unspecified
Mobile Application:No