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BigQuery is Google’s fully managed, petabyte scale, low cost analytics data warehouse for business insights. BigQuery Analyze petabytes of data using ANSI SQL at blazing-fast speeds, with zero operational overhead that is No-Ops service (no infrastructure to manage and no need a database administrator) under secured governance and encrypted environment. - Integration with popular visualization tools - Flexible pricing model Pay only for what you use Flat-tier-pricing for dedicated use No pay for automatic cached query results - Support of Geo-spatial data types & functions and  Foundation for Business Intelligence and AI/ML. - I would strongly suggest to read the blog " Introduction of BigQuery " to have a context.  Load job Limitations :  1,000 import jobs per table per day 10,000 import jobs per project per day File Size (for both CSV and JSON) 1 GB for compressed file 1 TB for uncompressed  4GB for uncompressed CSV with newlines in strings 10,...

When I have heard first time  the word ‘BigQuery’ , it looked me if I need to write a long big SQL queries more than 20 or 30 lines or how will it do some wonders as its name or something else…? It caused a curiosity, I started to explore it and happy to share my understanding with you. BigQuery  is a Software as a Service  data warehouse solution  offered by Google under cloud services which is categorized within analytics vertical. Before diving into BigQuery, there are 2 more words which are relevant to have a better understanding of BigQuery — Data Lake and Data Warehouse. Data Lake: Image Source:  https://aws.amazon.com/big-data/datalakes-and-analytics/what-is-a-data-lake/ Data lake is a scalable and secure data platform that allows enterprises to ingest data, store, process (batch, streaming) and analyze (structured, semi-structured and unstructured data) of any type or any volume. Hence, it is a centralized repository that allows you to store all your str...