Google BigQuery Reviews

92 Ratings
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Score 8.2 out of 101

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Reviews (1-18 of 18)

Tristan Dobbs profile photo
Score 10 out of 10
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Likelihood to Recommend

BigQuery is unlike anything we've used as a big data tool. It is perfectly suited to query large data sets quickly and to store those large data sets for any time use. It's perfect for storing data and using it for reports. Logging data is the perfect application for BigQuery, but transactional data is possible as well.
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Cameron Gable profile photo
Score 7 out of 10
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Likelihood to Recommend

We have several hundred terabytes of data and the size of our dataset is exponentially increasing. We needed a data warehouse that is highly scalable. We also serve a user base with several dashboards. BigQuery is great because it integrates nicely with Google Data Studio and other analytics products.
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Score 6 out of 10
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Google BigQuery is very well suited if your data is large and already in Google Cloud/GCP where the data itself is not simple structured data. It is less suited if you have well-defined data sets that may or may not exist in Google Cloud. Google BigQuery is also less suited if you have to analyze the data on a regular basis since the cost of accessing compute and storage adds up considerably.
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Score 7 out of 10
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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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Sam Lepak profile photo
Score 5 out of 10
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Likelihood to Recommend

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.
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Evan Laird profile photo
Score 9 out of 10
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Likelihood to Recommend

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.
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Spencer Baselice profile photo
Score 7 out of 10
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Likelihood to Recommend

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.
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May 21, 2019

BigQuery = Big Win

Score 9 out of 10
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Likelihood to Recommend

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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Score 6 out of 10
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Likelihood to Recommend

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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Gaurav Gautam profile photo
Score 9 out of 10
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Likelihood to Recommend

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.
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Anatoly Geyfman profile photo
Score 10 out of 10
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Likelihood to Recommend

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
Score 9 out of 10
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Likelihood to Recommend

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
Score 9 out of 10
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Likelihood to Recommend

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
Score 10 out of 10
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Likelihood to Recommend

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
Score 9 out of 10
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Likelihood to Recommend

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
Score 10 out of 10
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Likelihood to Recommend

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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Score 8 out of 10
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Likelihood to Recommend

- 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.
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Score 8 out of 10
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Likelihood to Recommend

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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Feature Scorecard Summary

Automatic software patching (8)
9.4
Database scalability (18)
9.1
Automated backups (14)
8.4
Database security provisions (14)
9.1
Monitoring and metrics (14)
8.5
Automatic host deployment (8)
9.3

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