In this post, I will talk about Google’s BigQuery service for big data analysis.

Overview

BigQuery is a serverless, highly scalable, cost-effective, enterprise-grade modern data warehouse offering on Google Cloud Platform. It allows analysts to use ANSI SQL to analyze petabytes of data at fast speed with no operational overhead.

Key Features

FEATURES

ServerlessNo-Ops model. Google manages all the resource provisioning behind-the-scene.Fast SQLSupports ANSI SQL with sub-second query response time and high concurrency.Managed StorageOnce the data is loaded to BigQuery, it is stored & managed by BigQuery in effective way.Data Encryption & SecurityData encrypted at rest and integrated with Cloud IAM for security.BigQuery MLEnables data scientists and data analysts to build, train and test ML models inside BigQuery using the SQL syntax.BigQuery GISEnables location intelligence by allowing analysis and visualization of geospatial data inside BigQuery.Flexible pricing modelOn Demand and Flat rate pricing. For latest pricing model, please refer official documentation

For the latest full list, please refer to the official documentation

How to Access BigQuery?

There are multiple ways to interact with BigQuery:

• Web UI
• BQ command-line tool
• BigQuery API Client Libraries
• BigQuery REST API
• Third Party tools

Interacting With BigQuery Using bq

Prerequisites

This post assumes the following:

1. We already have a GCP Project and BigQuery API enabled.

2. Google Cloud SDK (gcloud). If you don’t have, then refer to my previous blog - Getting started with Google Cloud SDK.

BigQuery Operations

1. Create a dataset

bq mk bq_dataset

1. List datasets

bq ls

1. Output:

datasetId
--------------
bq_dataset

1. Create table in a dataset

bq mk \
--table \
--expiration 3600 \
--description "This is my BQ table" \
--label env:dev \
bq_dataset.first_table \
col1:STRING,col2:FLOAT,col3:STRING

1. Examine a BigQuery table Note: I will examine a table from public dataset.

bq show bigquery-public-data:covid19_jhu_csse.summary

1. Output

Table bigquery-public-data:covid19_jhu_csse.summary

Last modified              Schema              Total Rows   Total Bytes   Expiration   Time Partitioning   Clustered Fields      Labels
----------------- ----------------------------- ------------ ------------- ------------ ------------------- ------------------ --------------
07 Jun 10:06:41   |- province_state: string     254940       41005062                                                          freebqcovid:
                |- country_region: string
                |- date: date
                |- latitude: float
                |- longitude: float
                |- location_geom: geography
                |- confirmed: integer
                |- deaths: integer
                |- recovered: integer
                |- active: integer
                |- fips: string
                |- admin2: string
                |- combined_key: string

1. Running a query

bq query --use_legacy_sql=false \
'SELECT
date,
country_region,
SUM(confirmed),
SUM(deaths)
FROM
`bigquery-public-data.covid19_jhu_csse.summary`
GROUP BY
date,
country_region
HAVING date = "2020-05-31"
AND
country_region IN ("India", "US")'

#big data #sql #data analysis