We use BigQuery at our organization to access daily transactional data from our POS solutions, which are used to sell products to our clients. We gather the most essential information for our clients and upload it to our data lake using BigQuery.
Right now, we are downloading raw Google Analytics four data to BigQuery and then manipulating it. It's mostly used for behavioral data in online datasets.
We use Cloud SQL for our web applications. Previously, we used Microsoft Cloud, but we transitioned due to cost benefits. We find Google Cloud Platform (GCP) to be more cost-effective. For BigQuery, we store data in a message queue similar to Kafka, and when an event occurs, that data is triggered to be inserted into a BigQuery table through subscriptions.
Ford has a customer-facing application. We use BigQuery to keep multiple metric records of Ford motors. Ford uses BigQuery to store huge customer data regarding a vehicle's uptime, downtime, velocity, etc. They save this data using BigQuery instead of the normal database.
Senior Manager.Marketing Strategy & Analysis. at Publicis Sapient
Reseller
Top 20
2024-08-19T22:03:44Z
Aug 19, 2024
My primary use case for the solution is as a powerful tool for handling and analyzing large datasets. The transition to GA4, which uses an event-based measurement framework, necessitated a more robust solution for detailed reporting and data analysis. It serves as both the storage and querying framework for this data.
BigQuery allows you to quickly analyze logs from your systems to identify the severity of issues. It integrates well with other Google Cloud services, such as Cloud Logging, where you can easily manipulate various data types and analyze all logs.
It is a pivotal component in enterprise data architecture, and crucial in data lake operations, whether supporting data warehouses or functioning as part of a broader data lake ecosystem.
BigQuery is a powerful tool for managing and analyzing large datasets. The versatility of BigQuery extends to its compatibility with external data visualization tools like Power BI and Tableau. This means you not only get query results but can also seamlessly integrate and visualize your data for better insights.
We have a cloud solution that runs in a centralized mode for a few hundred senior managers who require diverse reports, ranging from daily operational details to more substantial analyses, such as sales trends, movie ticket sales clustering, and reporting.
Data Engineer at a wellness & fitness company with 51-200 employees
Real User
Top 10
2023-11-03T15:37:37Z
Nov 3, 2023
In our workflow, we initiate the process by fetching data, followed by a preprocessing step to refine the data. We establish pipelines for seamless data flow. The ultimate objective is to transfer this processed data into BigQuery tables, enabling other teams, such as analytics or machine learning, to easily interpret and utilize the information for various purposes, whether it's gaining insights or developing models.
Senior Managing Consultant at Abacus Cambridge Partners
Real User
Top 10
2023-09-26T12:06:51Z
Sep 26, 2023
In the current landscape where organizations prioritize cloud solutions like Google Cloud, BigQuery plays a pivotal role in delivering scalability, flexibility, and numerous benefits for data management and analysis for our clients.
I use it to deal a lot with marketing, specifically Google Ads, YouTube, and Google Analytics. But mostly, I utilize it for its capabilities to sync directly up with Google ads transfers.
Vice President - Data Engineering and Analytics at a financial services firm with 10,001+ employees
Real User
Top 10
2023-02-21T13:42:00Z
Feb 21, 2023
The primary use case of BigQuery is within banking applications in the CDP. The front-end system pushes data, specifically mobile and net banking data, into BigQuery for processing and analysis. It involves significant data and requires specialized tools to utilize it fully. For example, we use AMS reports, breaking the data into various layers rather than using it in a single database.
Machine Learning Enginee at a retailer with 201-500 employees
Real User
2022-08-22T20:41:36Z
Aug 22, 2022
We use BigQuery as a data source. We mainly use it to do some transformations. Once we collect query data from it, we use other services to do model training or predictions. We don't really utilize all the features provided by BigQuery. We mainly use some basic data transformation options. It also provides some machine learning models.
This is a cloud solution from Google that is completely cloud based. BigQuery is similar to Snowflake in the way it manages data analytics. It has a proprietary way of storing and accessing data in its own data store and is 100% managed without you needing to install anything. There is no need to arrange for any infrastructure to be able to use this solution. Go onto BigQuery.com, create an account and you will get a console on your webpage in that browser where you can create databases, pipelines and transformations.
Data Engineer at a financial services firm with 10,001+ employees
Real User
2022-05-09T17:53:28Z
May 9, 2022
We use BigQuery to store data in a table and query it. Data storage can be either an internal native table or an external table where the external source will point to Google Cloud Storage or Google Drive. Wherever we can have external storage, we can have a table built pointing to that external storage and query the tables. In BigQuery, we can query the table or even do DML operations, like insert, delete, etc.
BigQuery is an enterprise data warehouse that solves this problem by enabling super-fast SQL queries using the processing power of Google's infrastructure. ... You can control access to both the project and your data based on your business needs, such as giving others the ability to view or query your data.
We use BigQuery at our organization to access daily transactional data from our POS solutions, which are used to sell products to our clients. We gather the most essential information for our clients and upload it to our data lake using BigQuery.
Right now, we are downloading raw Google Analytics four data to BigQuery and then manipulating it. It's mostly used for behavioral data in online datasets.
We use Cloud SQL for our web applications. Previously, we used Microsoft Cloud, but we transitioned due to cost benefits. We find Google Cloud Platform (GCP) to be more cost-effective. For BigQuery, we store data in a message queue similar to Kafka, and when an event occurs, that data is triggered to be inserted into a BigQuery table through subscriptions.
Ford has a customer-facing application. We use BigQuery to keep multiple metric records of Ford motors. Ford uses BigQuery to store huge customer data regarding a vehicle's uptime, downtime, velocity, etc. They save this data using BigQuery instead of the normal database.
My primary use case for the solution is as a powerful tool for handling and analyzing large datasets. The transition to GA4, which uses an event-based measurement framework, necessitated a more robust solution for detailed reporting and data analysis. It serves as both the storage and querying framework for this data.
BigQuery allows you to quickly analyze logs from your systems to identify the severity of issues. It integrates well with other Google Cloud services, such as Cloud Logging, where you can easily manipulate various data types and analyze all logs.
We use the product as a data warehouse to store metrics data.
We use BigQuery to perform data warehouse migration for clients willing to move to GCP from their on-premise solution.
It is a pivotal component in enterprise data architecture, and crucial in data lake operations, whether supporting data warehouses or functioning as part of a broader data lake ecosystem.
BigQuery is a powerful tool for managing and analyzing large datasets. The versatility of BigQuery extends to its compatibility with external data visualization tools like Power BI and Tableau. This means you not only get query results but can also seamlessly integrate and visualize your data for better insights.
We have a cloud solution that runs in a centralized mode for a few hundred senior managers who require diverse reports, ranging from daily operational details to more substantial analyses, such as sales trends, movie ticket sales clustering, and reporting.
In our workflow, we initiate the process by fetching data, followed by a preprocessing step to refine the data. We establish pipelines for seamless data flow. The ultimate objective is to transfer this processed data into BigQuery tables, enabling other teams, such as analytics or machine learning, to easily interpret and utilize the information for various purposes, whether it's gaining insights or developing models.
In the current landscape where organizations prioritize cloud solutions like Google Cloud, BigQuery plays a pivotal role in delivering scalability, flexibility, and numerous benefits for data management and analysis for our clients.
We use BigQuery for analytics and reporting.
I use it to deal a lot with marketing, specifically Google Ads, YouTube, and Google Analytics. But mostly, I utilize it for its capabilities to sync directly up with Google ads transfers.
We use BigQuery for data warehousing.
The primary use case of BigQuery is within banking applications in the CDP. The front-end system pushes data, specifically mobile and net banking data, into BigQuery for processing and analysis. It involves significant data and requires specialized tools to utilize it fully. For example, we use AMS reports, breaking the data into various layers rather than using it in a single database.
My company uses BigQuery as a data warehouse.
This is a cloud-based data warehouse.
Our company uses the solution as a data warehouse for implementing machine learning use cases and queries.
We use BigQuery as a data source. We mainly use it to do some transformations. Once we collect query data from it, we use other services to do model training or predictions. We don't really utilize all the features provided by BigQuery. We mainly use some basic data transformation options. It also provides some machine learning models.
This is a cloud solution from Google that is completely cloud based. BigQuery is similar to Snowflake in the way it manages data analytics. It has a proprietary way of storing and accessing data in its own data store and is 100% managed without you needing to install anything. There is no need to arrange for any infrastructure to be able to use this solution. Go onto BigQuery.com, create an account and you will get a console on your webpage in that browser where you can create databases, pipelines and transformations.
We use BigQuery to store data in a table and query it. Data storage can be either an internal native table or an external table where the external source will point to Google Cloud Storage or Google Drive. Wherever we can have external storage, we can have a table built pointing to that external storage and query the tables. In BigQuery, we can query the table or even do DML operations, like insert, delete, etc.