Table of Contents

BigQuery Features

Big Query is a fully 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 Query as Google’s serverless cloud storage platform designed for large data sets.  Since storing and querying massive datasets can be time-consuming and expensive without the right hardware and infrastructure, Google provides this enterprise data warehouse that solves this problem by enabling super-fast SQL queries using the processing power of its infrastructure

Google Big Query data warehouse features 

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

big query

The other features include

  • NLP for easy access
  • Change data capture in real time
  • Simple storage architecture
  • Big Query Materialized view
  • BigQuery Sand box for testing

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Other features advantages

  • You can run SQL just after uploading the data
  • Simple deployment without clusters, virtual machines or keys
  • By default, big query data warehouse is deployed across multiple data centres, with multiple replication factor
  • You can easily manage permissions on data sets using access control list
  • For each query, cores used runs into thousands
  • It can analyse and process terabytes of data per second
  • Scales seamlessly with the usage

Big Query New UI features

  • Big Query has aggregate functions which allows you to get the average of data, string functions which allows you to generate string values and date functions for processing and formatting dates
  • Big Query has an SQL workspace to execute SQL queries, expand and collapse controls to hide the navigation menu,2 SQL pane tabs, auto-completion of keywords feature, split SQL panes for viewing side by side the SQL results
  • Better Intellisense- The best revamp in the SQL pane is the intellisense which enables quicker SQL writing
  • New footer menu consisting of Query history, job history and saved queries

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