Google BigQuery

We are analyzing large volumes of data generated by IoT devices to derive actionable insights and improve decision-making and for monitoring purpose while sitting from different places around the globe. Google BigQuery is helping us in setting up automation of gear manufacturing process in factories so as to reduce human effort.

My company, Randstad, uses BigQuery as our data warehouse to store all our lead information and marketing metrics. It pulls numbers from various sources and then creates master data sources, which we use for the performance dashboards we present to internal stakeholders. More recently, we have been using Big Query to host our historical data from Google Analytics.

The scope of my use case is data governance relevant. My organization uses Google big query as the primary tool for storing, reading, updating and analyzing data. Due to the big size of the organization, we have an increasing number of data consumers and my specific use case is to provide a data control panel to make sure the data is being used properly according to the data Governance policies.

We use BigQuery majorly for two purposes. Our data engineering team develops trends based on collected data over BigQuery. That helps us strategize our feature rollouts. The second use case where we make use of BigQuery is in our tests dashboard. We collect test success and failure data and use BigQuery to categorise different failures, calculate failure rates and show trend for errors after weekly releases.

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.

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.

As a supplement solution to the main enterprise systems for reporting, it is mostly used for the R&D department. The aim was to query rather diverse and semi-structured data from various systems. Some of the sources were wide, some deep and a few were both. Other tools for storing and querying were tried as well.

Due to its lower cost, we use BigQuery as our primary database to store most of our data. We also use BigQuery to run periodic analytical tasks. We mainly use it for our WebSights product which collects and stores many user demographics and enriches IP traffic.

Google BigQuery is being used in unlocking real-time user data and boost data-processing power to perform more extensive business analytics. Along with other complimenting products like Dataplex, it has become a solid warehouse for the whole organization to make data-backed decisions.

We use Google BigQuery in conjunction with Bloomreach, this allows us to query the back end of the data without having to use the front end. The tool is fast to run queries and allows us to move the data to our other Data Warehouse environments quickly with little effort.

We use Google BigQuery as a data warehouse to pull data from analytics platforms such as Google Analytics. This allows us to create various tables containing the exact data various parts of the business need. We can then create dashboards for end-users internally. It especially answers our needs in terms of user behaviour and engagement. Our data capabilities are reinforced and much more scalable.

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.

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.

We tried to use Google BigQuery to analyze, perform and build various custom queries to our large set of geological historical data. To solve our needs in geological analysis of huge data, we looked around at what tools would allow us to optimally perform analytical work without capital expensenses and learning new tool.

We are analyzing loads of financial data and therefore had to find a solution which can handle the amount of data we have in our organization. The data insights generated by reports based on Google BigQuery are very valuable to all stakeholders involved. Google BigQuery helped a lot in our use case.

We are using Google BigQuery to store all events data of our app and Since our events data are recorded every second wise so it's a large data set of events that are easily handled by Google BigQuery since Google BigQuery has minimal charges for storage and mainly it will charge for running the query inside the Google BigQuery so it will be very easy for us to store a large database in Google BigQuery.

Our business uses Google BigQuery to analyze data from Google Analytics (both UA and GA4). The platform has an easy to understand layout that has improved a lot over the years. One of the key features that makes it user friendly is the ability to have side-by-side tabs of different code and output. This makes it easy to compare multiple versions of data. This platform is used to help us track are key web vitals that inform on us our sites performance. Because we have multiple different key variables that are stored in different locations, we often need to join data tables together or compare data between the different locations.

We use BigQuery as our data warehouse to store a large part of our data. We also use BigQuery to normalize, tie together, and prepare data for data visualization. It allows us to tether disparate data sources to create analyzable and comprehensive KPIs at granular and high-level layers.

Google BigQuery serves as our essential PaaS tool for streamlined data management and analysis. As a serverless solution, it offers automatic scaling, eliminating infrastructure hassles. Leveraging its advanced capabilities, we efficiently process large datasets through SQL queries. This empowers our organization with rapid, insightful decision-making, fostering a dynamic, data-driven approach that enhances overall operational efficiency and strategic planning.

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.

My organization is primarily concerned with training individuals to use store and analyze large amounts of data in a manner that is fast and accurate. Google BigQuery makes it possible to use the Cloud's infrastructure (hardware and software) to accomplish its data analysis goals. Being able to pay for the time and space that is utilized offers significant cost savings, especially for smaller (and mid-size) businesses and those that do not possess adequate resources for establishing a high-capacity infrastructure.

Google BigQuery allows querying multiple datasets within seconds using Sql. It also helps optimise queries to get results quickly.We can preview data without incurring costs. Google BigQuery is a fully managed, serverless, super fast data warehouse with no equivalent in the cloud space. It also creates graph using the data to help generate insights and view trends.

We use Google BigQuery to pull marketing data into a warehouse, run queries on the data to transform it into a more usable form, and then use the resulting tables as data sources for marketing reporting platforms. The problem it addresses is our need for a comprehensive data warehouse where we can store all our business and marketing data so that we can visualize and report on it later on.

Google Big Query was a contender for our proprietary database to be used as the cloud database to predict scoring models. We use machine learning to predict if someone is going to default on their loan, and use machine learning to determine how much money someone is eligible for. Google Big Query was an option considered for managing this data.

We use BigQuery as the company's Data Warehousing tool. The transactional information is handled mostly in Firebase and we inform BigQuery of each update or creation event from which we build the status and history tables. In addition, we use it to consolidate data from other external sources, such as Facebook, Analytics, Google Ads, among others.