Cloud Data Warehouse - Jul 05 2021

Is Google BigQuery a Data Lake?

For businesses going all-in on being data mature, there are a host of services that make a lot of sense. These services can provide essential features and functionality, or make the process of working with data easier in many ways. Google provides quite a lot of solutions that fall strongly under these categories, and one of the most popular for businesses is BigQuery.

Is BigQuery a data lake? Is it a data warehouse? To understand this, you need to first grasp the concepts of data lakes and data warehouses, their unique features and characteristics, and what makes them different from each other.

While both are used to store data, data lakes, and data warehouses are very different. A data lake mainly contains raw and unprocessed data, data that does not yet have a clearly defined purpose and is waiting for further use. A data warehouse contains data that is already processed and structured. Data in a data warehouse already has a defined purpose.

Google BigQuery is officially classified as a data warehouse. In reality, it can be used for various use cases, including as a data lake and a data warehouse. It is a cloud-based, scalable, and cost-effective service that bundles specific features that lend themselves well to both use cases. Let us take a closer look.

Google BigQuery as a Data Lake

For many businesses, it can make complete sense to use BigQuery as a data lake. The data lake is the first point of storage after the collection of data, and the fact that BigQuery is a Google service and has seamless integration with a host of other Google services is a major point in favor of utilizing it for this use case. Another is the fact that BigQuery is scalable, serverless, flexible, and includes essential features for generating business intelligence.

The ability to take in raw, unstructured data and store is securely without losing any time defines the fortunes of many businesses. For such use cases, a data lake is an obvious solution. Typically, a data lake would house data from multiple sources and of multiple types without any prior processing. This can be set up easily by creating connectors that clear a path between the data sources and the data lake.

BigQuery becomes a sensible choice for this task as it is offered by a large, reputed organization, is fast and simple to use, can accommodate several different kinds of workflows, generate reports and dashboards, and has native support for extremely fast SQL queries. On top of that, you can enjoy seamless integration with several Google services that proliferate the internet, data from which can be easily piped into the data lake by using free, open-source connectors that work with Google APIs.

For the most part, BigQuery solves a crucial problem that businesses trying to build effective data lakes face – response time. Responding to market changes and stimuli the quickest is almost synonymous with competitive advantage in this day and age and this is where BigQuery can be a real help. Apart from an easy adoption process without roadblocks, it also brings machine learning and AI to the table.

Google BigQuery as a cloud data warehouse

BigQuery has been built as a cloud data warehouse and its feature set has been designed specifically for that use case. It offers democratized insights and information – rich dashboards and reports. It also helps businesses significantly cut costs while running analytics at scale when compared to alternative warehouse solutions. With fast SQL speeds, built-in machine learning, and a scalable cloud-based design, it is hard to argue its efficacy as a cloud data warehouse.

What sort of businesses implement data lakes in BigQuery?

Mostly, a few distinct types of businesses can really gain an advantage implementing data lakes in BigQuery. This includes businesses that require seamless onboarding of data through set-and-forget pipelines, businesses that gather large amounts of data from other Google services and associated services, and businesses who want to use one or more of the characteristic features of BigQuery to extend the functionality of their data lake.

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