This is the part 5 of the series, Modernising a Data Platform and BigQuery concepts. In this part and next few parts, we will discuss about some of the key concepts of BigQuery for Data warehousing professionals.

In first 4 parts of the series we have focussed on concept of Modernisation,  Datawarehouse modelling & fundamentals,  Characteristics of a modernised data platform and the  architecture that drives the big data analytical platforms.

BigQuery is a modernised Datawarehouse solution on Cloud that offers wide range of benefits and is an answer to quite a few pain points that a traditional Datawarehouse poses to a user.

Its ability to process the queries faster, provision to storage petabytes of data for a relatively lower cost, Serverless architecture, No Ops facilities that helps in eliminating maintenance and operational overhead for the users, compatibility with other renowned technologies and ease of migration and ML capabilities are a few important characteristics that makes BigQuery a comprehensive solution for a modern Datawarehouse.

BigQuery does not deviate by a great deal from the conventional Dwh concepts such as data marts, data lake, tables, views and Grants/accesses, however it is much more organised. The Data Marts in Traditional Datawarehouse are called as datasets in BigQuery, The Data Lake which is a raw data storage option which is synonymous to Google cloud storage and Google Drive can be directly queried from BigQuery with its external data source integration capabilities. Google’s Identity and Access management controls the accesses to Bigquery datasets at a very granular level.

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