{"id":10197,"date":"2020-07-22T19:32:27","date_gmt":"2020-07-22T19:32:27","guid":{"rendered":"http:\/\/ismiletechnologies.com\/?p=2391"},"modified":"2022-12-10T02:00:02","modified_gmt":"2022-12-09T20:30:02","slug":"bigquery-features","status":"publish","type":"post","link":"https:\/\/ismiletechnologies.com\/en_us\/dataops\/bigquery-features\/","title":{"rendered":"BigQuery Features"},"content":{"rendered":"

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Big Query\u00a0is a\u00a0fully managed, serverless data warehouse that enables scalable analysis over petabytes of data. It is a serverless Software as a Service (SaaS) that supports querying using ANSI SQL. In simple words, we can define the Big\u00a0Query as Google\u2019s serverless cloud storage platform designed for large data sets.\u202f Since storing and querying massive datasets can be time-consuming and expensive without the right hardware and infrastructure, Google provides\u00a0this\u00a0enterprise data warehouse that solves this problem by enabling super-fast SQL queries using the processing power of its infrastructure<\/p>\n

Google Big Query data warehouse features\u00a0<\/b><\/p>\n

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  1. Real-time analytics<\/b>-It\u2019s\u00a0possible to run ad hoc queries on petabytes data sets in seconds. It\u00a0eliminates\u00a0the time-consuming work of provisioning resources and reduces your\u00a0downtime\u00a0with a serverless infrastructure that handles all ongoing maintenance<\/li>\n
  2. Seamless Scalability<\/b>-BigQueryseparates storage and computes to enable elastic scaling that streamlines capacity planning for a data warehouse.<\/li>\n
  3. Cost Optimization<\/b>\u00a0\u2013\u00a0Big\u00a0Query\u2019s serverless architecture helps to only pay for the storage and compute resources you use. Big\u00a0Query\u2019s separation of storage and computing makes it easy to scale independently and endlessly on demand, leading to affordable, economical storage.<\/li>\n<\/ol>\n
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    1. Federated query and logical data warehousing<\/b>-\u202f BigQuery\u2019s powerful federated query can process external data sources in object storage (Cloud Storage), transactional-databases (Cloud Bigtable), or spreadsheets in Drive \u2014 all without duplicating data.<\/li>\n
    2. Security and privacy for business data and investments<\/b>-Google\u2019s cloud-wide identity and access managementprovides\u00a0protection and control access to encrypted projects and datasets. Big\u00a0Query makes it easy to\u00a0maintain\u00a0a strong security and governance foundation.<\/li>\n
    3. Multi-cloudcapability<\/b>– Big Query Omni managed infrastructure allows seamless data analysis across multiple clouds,\u00a0without leaving the big query interface.<\/li>\n
    4. ML and AI integrated<\/b>– Big Query ML with Vertex AI along withTensorflowallows you to train structured data models with SQL in minutes<\/li>\n
    5. Connected sheets<\/b>– With Connected sheets users cananalysemillions of rows of Big Query data represented on Google sheets, without any knowledge of SQL<\/li>\n
    6. BigQuery GIS<\/b>– This feature adds BigQuery serverless architecture with Geospatial analytics to provide you intelligence of location<\/li>\n
    7. BigQuery data transfer service<\/b>– It helps you transfer data from sources like Google Ads,Youtube,\u00a0Terradata, Amazon S3 etc\u00a0(and other first and\u00a0third-party\u00a0sources)\u00a0without knowledge of a single line of code.<\/li>\n<\/ol>\n

      \"big<\/p>\n

      The other features include<\/p>\n