BigQuery Reviews

The main use cases for this solution are statistical analysis of Google Analytics. I gather Google Analytics data and insert it into BigQuery, and then I do statistical analysis on top of that.

Regarding the room for improvement in BigQuery, it is pretty good as it is. I would try to improve the ease of connecting external data, as integrations would be an area in which I think BigQuery could improve.

The features I find most valuable in this solution are the ability to run and handle large data sets in a very efficient way with multiple types of data, relational as SQL data. It is kind of difficult to explain, but structured data and the ability to handle large data sets are key features.

The data integration capabilities in BigQuery were, in fact, an issue at the beginning. There are two types of integrations. As long as integration is within Google, it is pretty simple. When you start to try to connect external clients to that data, it becomes more complex. It is not related to BigQuery, it is related to Google security model, which is not easy to manage. I would not call it an integration issue of BigQuery, I would call it an integration issue of Google security model.

I use BigQuery for obtaining data. I execute different queries to gather raw data. Additionally, I use BigQuery in conjunction with Google Cloud Platform for managing large volumes of data.

BigQuery processes a substantial amount of data, whether in gigabytes or terabytes, swiftly producing desired data within one or two minutes. Data retrieval occurs efficiently on the dashboard utilizing BigQuery. 

Additionally, BigQuery's SQL querying capabilities are highly regarded and facilitate integration with SQL. Its deployment as a SaaS-based solution through Google Cloud Platform simplifies utilization. As part of GCP, it offers a streamlined experience without the need for installation.

We are migrating all the data to BigQuery from SaaS. We are creating projects and using it for data analysis in the healthcare environment, specifically for Medicare and Medicaid.

The use of SQL is valuable. I am very familiar with the flexibility of the environment. It is not just a cloud environment but also offers a nice interface. BigQuery has a very nice interface that you can easily learn if you know SQL. It is really fast because it can process millions of rows in just a matter of one or two seconds. It is fast.

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.

The solution is very useful nowadays for keeping a huge number of records. It's easy to maintain. Since it has a columnar storage data format, BigQuery is more useful to provide the analytical details. BigQuery can be used to perform some analytical activity over the data.

Nowadays, AI and data analytics projects use a columnar data approach. This will help achieve filtration and analyze data presented in a table format. It is very useful to provide the analytical data. Since it is provided over the cloud, its SLA value is more than 99.9%, and it provides high availability.

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.

It has not impacted our operational costs and productivity much; however, it offers a valuable solution for our use case.

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.

The platform has significantly improved the organization's ability to analyze detailed and scalable data. It efficiently handles large volumes of data, crucial for timely decision-making and in-depth analytics. However, the shift from free reporting tools to a pay-for-use model has introduced additional costs.

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.

One of the most significant advantages lies in the decoupling of storage and compute which allows to independently scale storage and compute resources, with the added benefit of extremely cost-effective storage akin to object storage solutions. Its unique architecture not only provides robust enterprise data warehouse capabilities but also seamlessly integrates with data lake functionalities.

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.

We have seen significant improvements in data management processes, particularly with integration capabilities that allow us to easily retrieve and manipulate data through simple queries. This enhances our workflow significantly.