Cloud Data Warehousing with Bigquery

Cloud Data Warehousing with Bigquery

11 August 2021

Cloud Data Warehousing with Bigquery

Overview

Since the organisations are moving to a more data driven approach the need of data storage analyzation and aggregation have become a major concern.

It seems very difficult for organizations to manage a huge amount of data which has been being generated rapidly. Here the concept of data warehousing comes to rescue.

Data warehousing:

A data warehouse is a place where data from different sources are collected and aggregated. Different analytics operations are performed on data to reach the business need of the organisation. This technique is being used by organisations to solve data related concerns in one place.

Introduction to Cloud based data warehouse – BigQuery

Google Cloud offers an enterprise data warehouse in the form of Bigquery. As the name suggests Bigquery is used for Big Data solutions. It is a completely managed, serverless,highly scalable and not transactional database.

The above properties and SQL like query language makes it user friendly because users don’t need to worry about the underlying architecture of bigquery because the whole architecture is hosted and managed under strong infrastructure provided by google.

The Flex of using Bigquery:

  • Bigquery gives highly scalability with its data
  • Serverless
  • Completely managed
  • SQL like language provides user friendly experience
  • Cost Efficient

Cloud Data Warehousing with Bigquery

Scalability:

The Google Bigquery provides a highly scalable environment by running multiple parallel nodes within various regions. This allows bigquery to execute trillions of rows of data in just seconds. It is a petabyte scale database which allows a low latency data processing mechanism and therefore it suits for numerous enterprise needs requiring a high data dependency and the scalability adds more flexibility to it.

Serverless architecture:

In most of the data warehouse services the organisation administration needs to set up a server environment and hence a lot of resources are engaged with the security, flexibility, reliability and performance part rather than the data insight part. Bigquery serverless model provides a mechanism of running various parallel computation machines to get rid of this constraint and focus more on the data insight part without worrying about the infrastructure.

Completely managed:

The users don’t need to understand the underlying architecture provided by Google as it is completely managed by Google as well as the resources to be used according to the requirement is also auto managed which makes it even more simpler for business needs.

SQL like query language:

Access to BIgquery language does not require any new query language as the engine supports standard SQL query language which makes it easier for new users or different technology users to adapt with the services of bigquery quickly.

Pricing:

The pricing for Bigquery is mainly applied for query processing and data streaming. ONly those resources costs which were used rest the resources are either not charged or a very minimal amount is charged.

We can understand this by- The data changed in storage within the last 90 days are billed but the data only stored without any changes within 90 days are billed very less(only for storage).

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