BigQuery Reviews

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.

This is a solution from Google that is 100% cloud-based, based on GCP. BigQuery is similar to Snowflake in the way it manages data analytics. It completely decouples storage from Compute. It has a proprietary way of storing and accessing data in its own data store and is 100% managed without you needing to install or deploy anything. There is no need to arrange for any infrastructure in order to use this solution. Go to BigQuery.com, create an account and you will get a console on your browser where you can start creating the end to end data platform - databases, data warehouses, roles, users, ETL / ELT pipelines and write transformations - all via the workspace.

Although BigQuery in completely managed on cloud, one has to use many services of BigQuery and GCP in order to create the end-to-end data setup. BigQuery acts as the core Data Warehouse mechanism, but it needs additional services like - Google Cloud Dataflow, Cloud pub/sub, Cloud BigTable, Cloud DataPrep, Cloud DataProc, Cloud SQL. Being different from the traditional way of setting up end-to-end data engineering platform, the learning curve for BigQuery is a bit steeper. Google BigQuery ecosystem can surely make the ecosystem a bit more leaner.  

We use the product as a data warehouse to store metrics data.

At the enterprise level, the pricing for storing data in BigQuery is practically free.

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.

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.

BigQuery's managed nature ensures that it's always up-to-date and maintained by Google on its cloud platform. This aspect makes it an ideal choice for organizations seeking cloud-based solutions instead of on-premises ones.