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What are some of the Google BigQuery features?

by Nathan Zachary

What are some of the Google BigQuery features?

Enterprise Data Warehouses (EDW) is necessary for businesses to collect and analyze data. Earlier, companies with enough IT budgets would set up on-premise EDWs. These EDWs stored and analyzed transactional business data. However, industries collect data from various sources, such as user habits and mobile and IoT feeds. These advances in technology and the increase in the need to store and analyze data have compelled industries to use cloud data warehouses. Google’s BigQuery is one such service offered by google cloud. According to Google, it is serverless, scalable, and cost-effective. As a result, BigQuery is becoming more and more popular. Therefore, Google BigQuery Training may boost your career as a cloud practitioner. 

Top 9 features of Google BigQuery

Below you can see the fundamental nine features of BigQuery. Your Google bigquery training will teach you how to utilize these features efficiently. 

  1. Serverless: With servers, comes to the responsibilities of managing, updating, and securing data. However, with Google Big Query, all the management happens behind the scenes. You can focus on collecting and analyzing the data. 
  2. Multicloud: You can analyze data across multiple clouds from one interface. It is flexible and fully managed and provides a seamless data analysis experience. With the cross-cloud transfer, you can also combine data or train models across clouds. 
  3. Natural Language Processing: You can use Data QnA effortlessly to access the NLP data insights. You can analyze petabytes of data and embed them where users work.
  4. ML and AI integration: You can train and execute models quickly using SQL. BigQuery ML, Vertex AI, and TensorFlow are the main components that enable you to do this. 
  5. Business Intelligence (BI) Foundations: BigQuery enables seamless data integration, transformation, analysis, and reporting. The BI engine can accelerate BI workloads. You can also turn on the in-memory analysis service to get fast query responses.
  6. Spreadsheet Interface: Even if you don’t have SQL knowledge, you can use the Connected Sheets to analyze billions of rows of BigQuery data. Familiar tools such as tables, formulae, and charts are available for getting Big Data insights quickly. 
  7. Real-time analytics: You can use the high-speed streaming insertion API of BigQuery for analyzing data in real-time. You can use Datastream, Pub/Sub, and Dataflow to send data into BigQuery. 
  8. High availability: You will automatically get highly durable redundant storage in multiple locations. These will have high availability and require no extra costs or setup.  
  9. Standard SQL: You can use the ANSI: 2011 compliant Standard SQL dialect with BigQuery. The ODBC and JDBC drivers are also available for you at no extra cost. These can ensure that your current applications interact seamlessly with the BigQuery engine. 

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In conclusion, Google’s BigQuery is a secure and scalable platform with machine learning tools. You can collect and analyze petabytes of data across multiple clouds effortlessly in real-time. Moreover, it has NLP and BI tools to improve your overall business. These features make it popular among companies; consequently, BigQuery experts are high in demand. Therefore, you can stand out in the highly competitive IT field with Google Big Query Training.

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