Filter Ratings and Reviews
Filter 99 vetted Google BigQuery reviews and ratings
Reviews (1-21 of 21)
October 15, 2019
Google BigQuery is BIG for our company.
Google BigQuery has become our data warehouse for the entirety of the systems that we use. It is incredibly efficient and ridiculously powerful, so it allows us to store all relevant data and query it to build dashboards for management and leadership uses. We connected it directly to a data visualisation tool and it has become the most useful part of our business.
- BigQuery is ridiculously fast and has the ability to query absurdly large data sets to return results immediately.
- BigQuery allows for storage of a massive amount of data for relatively low prices.
- Easy to learn. BiqQuery uses SQL-like queries and is easy to transfer your existing skills to use.
- BigQuery can be dangerous. The charges can rack up quickly if you don't construct your queries properly. Traverse too much data too frequently and you can cost yourself some money.
January 18, 2020
THE tool you need to improve your business
Google BigQuery is being used as a data warehousing tool so that we can run analytics and calculate business metrics on our data. It’s very comprehensive and powerful yet provides tools to simplify recurrent queries on the data that makes working with it easy enough. We like that it’s a place where multiple people from interdisciplinary teams can converge on the data and work on finding ways to improve and better understand our business.
- It provides a central data storage regardless of the data source.
- It features functionality that makes it easy to store and re-run queries.
- It can be overwhelming to non-technical users at first.
- You can easily get confused as to what to do to start if not familiarized with the workflow.
December 23, 2019
Big Advantages to BigQuery
Big Query is currently being used by several departments as well as IT to extract data, blend it with other data and to generate reports based on that data. It's being used to track customer journeys through our site, track different channel traffic conversions and to build out dashboards in Tableau.
- The computing used by BigQuery is dynamically distributed across compute resources so that you do not have to manage compute clusters.
- Big Query connects easily with Tableau so that you can analyze billions of rows in seconds using visual analysis tools without writing a single line of code.
- Although BigQuery machine learning gives you the option to control your geographic data, it only applies to the US, Asia, and Europe. Further expansion of this option to other parts of the world would be beneficial.
- You don’t need to install, provision, or set up anything with Big Query because it is managed. The downside being that you can’t use it outside of Google Cloud Platform.
October 18, 2019
Easily Managed Data Warehouse Delivered At Scale
BigQuery is used by our Data Science team to do complex queries on large datasets. We have hundreds of terabytes of data and needed a scalable solution that would be able to query our entire biological dataset. BigQuery plays a crucial role in our data lake made up of several Google Cloud data solutions.
- Highly scalable data warehouse
- Easily integrated into analytics tools like Data Studio
- Easy to use with SQL support
- Can be pricey. There are ways to lower costs but they aren't always straightforward.
February 15, 2020
A Robust Tool for Big Data Analysis
Google BigQuery is being used to analyze click-stream data-set in conjunction with structured data-set. It is being used in the sales and marketing departments to essentially attribute new customer acquisition and existing case sales to specific sales representatives, sales divisions, and marketing campaigns. This attribution analysis using Google BigQuery tool has enabled my organization to measure return on investment of various sales and marketing initiatives. Such as training of sales representatives to help their customers adopt digital shopping tools, email, social, and online ordering banner campaigns to target specific customers and regions where we have distribution centers or stores and paid search ads. To accelerate the rate at which we are acquiring new customers who have never shopped before through our online food service business.
- Google BigQuery serves as a complete big data warehouse solution to quickly access marketing and sales data in one place.
- Google BigQuery enables analysts to pull correlated data streams by running SQL like queries, so they don't have to query multiple analytics tools.
- Google BigQuery queries need to be optimized to avoid high costs when pulling data.
- Google BigQuery needs knowledge of SQL coding to leverage its data analysis capabilities.
December 20, 2019
Google BigQuery for analyzing large ML datasets using SQL
Google BigQuery is used for data warehouse as a ML analytics engine company wide specifically for consumer behavioral analytics with data streams coming coming out of website as well as internal data sets.
- It is easy to create and then execute machine learning models in BigQuery using SQL queries using BigQuery ML. Everyone knows SQL.
- Google BigQuery is fully serverless/cloud based and can be up and running in few hours without need for any specific coding or integration if your data is already is Google Storage.
- Google BigQuery executes the SQL statements very fast and can can be used for real-time analytics especially if you use Google infrastructure ( GCP).
- Google BigQuery is great for large data sets where you need a familiar SQL interface but it is still slower than running the same SQL query on RDBMS, assuming your data is mostly structured.
- It is expensive if you have a lot of data that needs to be queried each time the query is run due to the license metrics used in Google BigQuery.
- Some of the SQL operations like table join are not optimized and can be slow compared to a full database.
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.
May 04, 2019
Dive Deeper into your Firebase Data
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.
February 25, 2019
Google BigQuery, Ideal for Large Companies with Multiple Teams
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
August 12, 2019
BigQuery- A big tool with a lot to offer
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.
May 21, 2019
BigQuery = Big Win
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.
November 28, 2018
Analyze data at lightening speed @BigQuery
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.
February 15, 2019
Google BigQuery: Slow Learning Curve
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.
June 26, 2017
BigQuery is a game changer for OLAP
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.
October 26, 2016
Google BigQuery truly democratizes data
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.
February 23, 2017
Fast 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
November 20, 2015
Evaluation of BigQuery from a Hadoop viewpoint
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.
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
November 18, 2015
Great for Interactive Analytics And KPI Reports
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.
February 19, 2016
BigQuery 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!
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
November 21, 2017
Data Analysis on Steroids with Google Big Query
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.
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 Scorecard Summary
Feature Scorecard Summary
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 |
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| Mobile Application: | No |