TrustRadius Insights for Google BigQuery are summaries of user sentiment data from TrustRadius reviews and, when necessary, third party data sources.
Pros
Quick Data Analysis: Users appreciate the rapid query speed of Google BigQuery, enabling them to analyze massive datasets without long wait times. The fast query performance is a significant advantage highlighted by users for efficient data processing and analysis.
User-Friendly Interface: Many reviewers find Google BigQuery very user-friendly, allowing team members with varying levels of expertise to easily query data using simple language. The intuitive interface of Google BigQuery's editor and query builder is noted as helpful in quickly constructing new queries by users.
Seamless Integration: Users value the seamless integration of Google BigQuery with other tools like Google Cloud Storage and Data Studio, enhancing workflow efficiency and collaboration. This integration capability with various tools contributes to improved data management solutions according to users' feedback.
we are using Store all data that we have. It has proven to be a very good product where we have been easily able to migrate to from our Excel sheets and create a combined database where we store all our historical data across clients and different teams and provide a place where they can access the data easily.
Pros
Storage
Speed
Easeof use
Cons
Excel upload
Integration with third parties
More google sources
Likelihood to Recommend
if the data is not huge and we don’t need a lot of cloud storage features such as high processing, immediate retrieval of data, et cetera. Then they might be the solutions which are better
VU
Verified User
Manager in Information Technology (11-50 employees)
We have UI survey reporting database in Google BigQuery. It is meant to give insights of how the users or sales people like the user experience. We recieve files which finally gets loaded in gcp env. Querying tables In Google BigQuery gives fast insights with comparatively less time than other cloud dbs.
Pros
Compatibility with traditional ETL tools
Time travel
Columnar storage
An intuitive UI
Cons
Not very Easy Integration with spark
Data lineage tool kind feature is not there
Orchestration can be better
Likelihood to Recommend
I feel like Choosing it when we have Streaming data with pub sub playing a big role. Though streaming analytics can be a lil.challenging when you have real time insights needed fast.
Much suited for micro batches or batch data. You can create a big data warehouse store history. Batch is where I would prefer
We use Google BigQuery as our main analytics database, as a data warehouse. It addresses our need for having all our data, from different sources, be available for analysis and flexible querying.
Pros
Storing large amounts of data
Querying large of amounts of data
Serving as a unifying data source for all kinds of data from different sources
Zero devops or infrastructure work required
Extremely good value for money ratio
Cons
Query syntax is sometimes a little hard
Data exploration tooling could be better
Likelihood to Recommend
I would 10/10 use Google BigQuery as an analytics DB again and again. It's perfect for it, really easy to use both for sending and ingesting data and also for retrieving and querying the data. It also has lots of integrations in 3rd party products, ranging from visualization to AI, as it is a widespread solution. I would not use it for a production database, as it has relatively high latency and is priced per query, so it doesn't really work in that scenario.
VU
Verified User
Executive in Research & Development (11-50 employees)
Google BigQuery is helping us with data preparation and storage. It is also helping with performing ML or Machine Learning operations natively. Support comes as Google BigQuery ML and AutoML which is easy to use and work with as the data is lies within Google BigQuery itself. The storage cost is pretty low for upto 1TB. There are multiple public data sets that helps build use cases easily.
Pros
Easily Create external tables on cloud storage based data.
Generate Auto ML for forecasting information
Query billions of rows easily and quickly at low costs.
Cons
It can expand the support for more ML algorithms
Lower the cost for the queries and simplify it by moving out from slot pricing to TB scans.
Add support for additional 3rd party data integration and ETL support.
Likelihood to Recommend
It is well suited for analytical and ML use cases on large data sets with ease. Enabling external data integration and allowing queries with various data sources without having to move the data to Google BigQuery, thereby saving cost and increased efficiency of the analysis being done. The Google BigQuery ML and AutoML integration has helped a lot by which the capability to create model, train, Forecast and evaluate can be done in the same platform or interface without having to leave the Google BigQuery environment. The capability to create visualizations via Google BigQuery on Looker Studio takes away the need for starting from scratch and helps immediate analysis of the data being rendered in the query.
VU
Verified User
Professional in Information Technology (10,001+ employees)
My company relies on Google BigQuery to manage and analyze our vast datasets effectively. As a DevOps engineer, I recommend my colleagues to choose Google BigQuery over other alternatives. Google BigQuery serves as our central data warehouse. It ingests, stores, and
optimizes large volumes of data, allowing us to perform complex queries
efficiently. Whether it’s historical sales data, customer behavior, or
inventory records, Google BigQuery handles it seamlessly.
Pros
Scalability and Speed: Google BigQuery handles large-scale data processing with ease
Serverless Architecture , so no infra management
Geospatial Analysis
Integration with Ecosystem as my company uses Google cloud platform
Cost-Effective Pricing
Cons
Queries that haven’t been optimised for speed or return redundant data can become expensive. So, cost estimation feature would be great!
Google BigQuery lacks robust built-in data visualisation tools. Integration with GCP is seamless, but third party integration would be beneficial for visual dashboards.
In my opinion, Google BigQuery schema changes can sometimes be cumbersome, especially for large tables. Simplifying the process of adding, removing, or modifying columns could improve data management workflows.
Likelihood to Recommend
1.Google BigQuery stores and analyses massive datasets in my organisation, making it ideal for me , as i can manage Terrabytes of data with it.2.Google BigQuery can ingest and analyze streaming data feeds in near real-time, so it helps to make data-driven decisions very fast. As for less appropriate scenarios:- 1. For very small datasets (in the megabyte range), traditional relational databases or spreadsheets might be more cost-effective and easier to manage. 2. Also, in scenarios where frequent schema changes is needed, it becomes cumbersome.
VU
Verified User
Engineer in Information Technology (501-1000 employees)
We are using Big Query to store metric data of our chatbot. It helps to get all the data in a single place and its easy to manage. The data is generated by the chatbot every time so we needed a scalable, cost-effectiveness and fast data processing database, thats why we use big query for it. Its integration with other software are also easy to do with lots of documentation.
Pros
storage of structured data
query execution speed
volume of data stored and processed
availability and latency
Cons
cannot delete new data due to streaming, i have to wait some time to delete new data
the UI can be improved
not able to see all data in a single page
Likelihood to Recommend
Use it if you have to process large data and complex query in short time. The pay-as-you-go pricing model ensures cost-effectivenes. If you need low latency use it.
VU
Verified User
Engineer in Information Technology (51-200 employees)
We are using Google BigQuery to store and analyze our big-data and analytics for one of our major projects. We stream different types of data from different sources into BQ and use complex queries to join data from different sources. Data can be queried both programmatically from our application, or displayed using tools like Looker Data Studio.
Pros
Store large amounts of semi-tabular data
Allows complex and fast queries
Allows streaming of data from different sources
Cons
Unstructured data is complex to query
Costs can be high if using large data sets
It's hard to estimate costs as they depend on usage
Likelihood to Recommend
I would use Google BigQuery for storing data warehouse information, streaming from multiple sources, and displaying either in my application's dashboard, Looker Studio, or Grafana. It's very easy to stream data from Firebase to BQ, and very effective as well. It is hard to stream data from your main database, and requires some work, but I believe it is worth the effort.
We have used Google's big query to store and analyze vast amounts of data. In today's time, every organization requires real-time insights from the data. BigQuery can be Integrated with popular BI tools to visualize data and generate actionable insights, aiding in department decision-making processes. With BigQuery, we have a centralized repository for all organizational data, facilitating easy access for analysis and reporting.
Pros
Scale automatically to handle datasets of any size.
BigQuery can perform extremely fast SQL queries across vast datasets.
Pay-as-you-go model, BigQuery allows users to pay only for the data processed and stored.
Cons
It is challenging to predict costs due to BigQuery's pay-per-query pricing model. User-friendly cost estimation tools, along with improved budget alerting features, could help users better manage and predict expenses.
The BigQuery interface is less intuitive. A more user-friendly interface, enhanced documentation, and built-in tutorial systems could make BigQuery more accessible to a broader audience.
Likelihood to Recommend
For organizations looking to avoid the overhead of managing infrastructure, BigQuery's server-less architecture allows teams to focus on analyzing data without worrying about server maintenance or capacity planning. Small projects or startups with limited data analysis needs and tight budgets might find other solutions more cost-effective. Also, it is not suitable for OLTP systems.
We deal with massive datasets – customer transactions, website logs, sensor data from our products – all running into terabytes. Google BigQuery acts as our central data warehouse and ingests data from various sources, like CRM systems, marketing tools and also from internal applications. It's not just the marketing team or data scientists who leverage it. Sales uses it for customer segmentation and churn analysis. The product team relies on it for user behaviour analysis and identifying feature adoption trends. The speed of Google BigQuery is mind-blowing. I can run complex SQL queries on massive datasets and get results almost instantly.
Pros
Its serverless architecture and underlying Dremel technology are incredibly fast even on complex datasets. I can get answers to my questions almost instantly, without waiting hours for traditional data warehouses to churn through the data.
Previously, our data was scattered across various databases and spreadsheets and getting a holistic view was pretty difficult. Google BigQuery acts as a central repository and consolidates everything in one place to join data sets and find hidden patterns.
Running reports on our old systems used to take forever. Google BigQuery's crazy fast query speed lets us get insights from massive datasets in seconds.
Cons
Google BigQuery's built-in visualization tools are limited compared to dedicated BI tools. Expanding the options and allowing for more customization would help explore and present data insights.
Currently, it's hard to track where the data comes from and how it changes as it moves through the pipeline because it lacks data lineage capabilities. It's tough to ensure data quality assurance and regulatory compliance.
The current access control options are somewhat limited. Granular control over specific datasets or tables within a project would help manage access in collaborative environments.
Likelihood to Recommend
If you've already invested in the Google Cloud ecosystem and since Google BigQuery is part of the Google Cloud Platform (GCP), it easily integrates with other GCP services like Cloud Storage for data storage and Cloud Data Studio for data visualization. We only pay for the resources we use, unlike traditional data warehouses with fixed costs regardless of usage, thanks to its pay-per-use pricing model with no upfront investment and ongoing maintenance.
Analytics Powerhouse. Google BigQuery is the best solution if you want to find trends from your past data. It is a Data warehouse which has SQL and ML capabilities. We have been using Google BigQuery for analyzing our customers billing data and creating dashboards in Looker Studio which can be used by our Sales teams.
Pros
Data Warehousing
Data Analytics
Machine Learning
Cons
The UI and the whole Google BigQuery studio is full of clutter.
It's very hard to find error logs related to your application if the backend is Google BigQuery
It's hard to share specific tables with someone which has a different place than Cloud IAM.
Likelihood to Recommend
Google BigQuery is well suited if you have TB or PBs of data which needs to be analyzed with accuracy and then you need to find trends or create dashboards as it has seemless integration with Looker.
Google BigQuery is not well suited if your Database is very small. As the Google BigQuery architecture take similar time in small database which is counter intuitive.
VU
Verified User
Engineer in Information Technology (10,001+ employees)
