Data warehousing. Streaming and batch ingest of files and APIs. Implementing business logic, combining data from different sources, …
We use BigQuery to store extremely granular data within our organization. This data is then aggregated to provide very detailed reports to …
Used to deploy this solutioning to the client by shifting away from traditional data warehouse to cloud data warehouse. It resolves the …
As a Data Analyst at my previous company, I dealt primarily with large datasets. Being able to retrieve that data was an important aspect …
Google Big Query is used by product and services department at my organization. It is used to maintain the various services like …
BigQuery (along with Airflow) has become a critical part of our technology stack. It is being used to support the ingestion of large …
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 …
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 …
It is our main data warehouse. It contains raw data and aggregated data. This is also used for aggregation by running scheduled queries on …
Google BigQuery has become our data warehouse for the entirety of the systems that we use. It is incredibly efficient and ridiculously …
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 …
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 …
Our marketing team and product development team BigQuery. This is my favorite software for storing information in the cloud, I use it both …
BigQuery is in use across the entire organization in various departments and businesses for multiple purposes. It is used to store mass …
BigQuery is a user platform designed to ingest, process and output large volumes of data. We use it to ingest data from Google Analytics …
We use BigQuery as our data warehouse. Meaning, we use BigQuery (BQ) for storing our data, aggregating it and creating pipelines to push …
Google BigQuery has become the de facto analytics warehouse for our organization. It has allowed us to scale effectively into massive …
[It's being used for] extracting patterns from big amounts of data (millions of rows) with complex aggregations.
Google BigQuery is used for data warehouse as a ML analytics engine company wide specifically for consumer behavioral analytics with data …
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 …
I evaluated and presented introduction to Google BigQuery for the Fresno Google Developer Group technology meetup and also at Google …
We use BigQuery in our engineering team to do fast analytical queries and generate many reports for the management team. Many of those …
We are Big Data company who dealing with petabytes of data and we generate hierarchal data that can be used for Advertising companies. Our …
In our organization, Google BigQuery is for storing very large data which is created within seconds. We log each and every event done by …
Database scalability (27)
Automated backups (22)
Database security provisions (22)
Monitoring and metrics (23)
Queries (Hourly Flex Slots)
per 100 slots
Queries (Annual Flat Rate)
per 100 slots
Entry-level set up fee?
- No setup fee
- Free Trial
- Free/Freemium Version
- Premium Consulting / Integration Services
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.
Frequently Asked Questions
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.
Reviewers rate Database scalability highest, with a score of 9.9.
The most common users of Google BigQuery are from Mid-size Companies and the Computer Software industry.
Companies can't remove reviews or game the system. Here's why
- Standard SQL
- RDBMS-like features
- Python library support
- Python library authentication simplification
- multi-transaction ACID compliance
- Extremely powerfull
- Need more partitions than just dates
- Would like to chose which partition we insert into
- Transparency in terms of cost
- Utilisation of the data warehouse and suggestion on the sizes
- Easy to use and integration with other components
- UiUX features can be improved further in terms of navigating from one folder to another
- Huge plus point if you have idea running SQL scripts.
- The ability to store and manage multiple data warehouses is a big plus point which helps a lot for growing businesses.
- Easy integration with tools like Data Studio and Google Analytics which provides great data warehouse and data management solutions.
- Can't use it out of Google's cloud platform which is a minus point if you want a local setup.
- Can be a little expensive to manage.
- A little difficult to manage someone with less technical expertise as it requires you to have SQL knowledge of joins, CTEs etc.
- Google BigQuery is column based, therefore it has high speed and easily accessible.
- As I work with inventory related data, it gives me real time updates which helps to resolve many blocks which could cause problems if delayed.
- Being serverless, it is easy to handle large size data.
- Google BigQuery charge according to the quality of the code. So if it is long and lengthy and not the most efficient it can be costly.
- The UI/UX is little difficult to use at the beginning on a small screen because of the layout.
- Performance at scale.
- Console interface is a little clunky.
- 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.
- 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.
- Query performance is awesome.
- Fully managed.
- Can be used for all batch jobs or aggregations.
- Query pricing is still higher if we don't take flat pricing which is high.
- Storage pricing is also counted.
- It do not handle external dependencies.
- 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.
- 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.
- 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.
- 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.
- 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
- 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 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 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.
- 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 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.
- 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.
- 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'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.
- 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
- 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.