Features of BigQuery

 Features of BigQuery

Following are some of the useful features of BigQuery:

1. Fully Managed, Serverless Insight

GCP that is Google cloud platform excels the industry in the ability to let you analyze data at the scale of the entire web, with the awareness of SQL and in a fully managed, serverless architecture where backend infrastructure is fully handled on behalf of you. One of the wonderful features of Google's big data analytics products is that they are able to scale automatically while you focus only on the business insight you want to uncover.

2. Fast Queries on Petabyte-scale Datasets

BigQuery is Cloud Platform's fully managed data warehouse that lets you frugally query massive volumes of data at a speed which anyone would expect from Google. Google does not charge daily but you have to pay as you go. Google provides pricing benefits and the scalability and security of Google's best infrastructure to power your business insights.

3. Unified Batch and Stream Processing

Google Cloud has Dataflow which is an innovative, fully managed service for developing and executing a huge range of data processing patterns which includes ETL, batch computation, and stream analytics. You can express your computation with no switching cost as you use a single tool and programming model for both batch and continuous stream processing flows.

4. Spark and Hadoop in the Cloud

Nowadays companies are standardizing on abundant open source tools which include Spark, Hadoop, MapReduce, Hive, and Pig, but this will soon see a natural transition to Cloud Dataproc. One reason for this would be while using Dataproc you should not worry about your data pipelines outgrowing clusters as it allows you to create and resize clusters quickly at any given point in time.

5. Managed Databases, Object Storage and Archival

Specific business questions which you may encounter in the future cannot be predicted in prior but can be solved if you have relevant data in hand when they occur. One should always preserve events and valuable metadata related to your business environment, by storing it economically to analyze later. You can choose from a variety of globally available storage products for your data, from managed SQL to NoSQL options, including Google's category-defining archival product.

6. The Next Stage of Machine Intelligence

In today's era, most of the companies are shifting towards big data analytics. Companies are willing to apply Google's heritage of machine learning and analytics at web-scale to real-world data relevant to their business. Cloud Platform enables modest-sized teams to aggregate and run machine learning workloads on a huge amount of data to do predictive analytics.

7. Tap Into Innovation

Google has excelled the industry with innovations in data science technologies such as MapReduce, BigTable, and Dremel and now Google is making the latest generation of its data science tools available to everyone, including market-leading programming tools and programming models.

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