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Last Updated: Mar 27, 2024
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Prerita Agarwal
Data Specialist @
23 Jul, 2024 @ 01:30 PM

Introduction

BigQuery is a fully managed firm data warehouse. It offers inbuilt features such as business intelligence, ML, and geospatial analysis. The features in it help you manage and analyze your data. BigQuery lets you use SQL queries to answer your firm's questions.

In this article, we will learn BigQuery in detail. The complete series of Big Query is divided into three parts, you may visit Basic Concepts of Big Query and Advanced Concepts of Big Query.

 

Big Query

Need of BigQuery

Need of Big Query

An important question arises here: why do we need BigQuery? So let's discuss this in our next session.

  • BigQuery is mainly used for analytical querying.
     
  • BigQuery is fast & easy to use on data of any size.
     
  • BigQuery can transform and analyze data.
     
  • A user can use seamless data integration using BigQuery.
     
  • BigQuery uses ANSI SQL to provide JDBC drivers.
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Features of BigQuery

Features

Let's now discuss some salient features of BigQuery. 

  • Petabyte Scale.
  • Real-time analytics.
  • Flexible Pricing Models.
  • Data Security.
  • Standard SQL.
  • AI Support.

How to use BigQuery?

Let's now learn how to use BigQuery. Follow the below steps.

Step 1: First we must download the dataset into the system.

Step 2: Next we have to upload and store the dataset in Google BigQuery.

Step 3: Use BigQuery to query data stored in Google BigQuery.

Step 4: Now, add the dataset to Google Cloud Storage.

Step 5: Finally, we can use BigQuery with a dataset in Google Cloud Storage. 

Advantages of BigQuery

Advantages

Now we will discuss some advantages of using BigQuery. Let's have a look at it.

  • Google BigQuery is based on column design. Thus, it has high speed and is easily accessible.
     
  • Being serverless, BigQuery is very easy to handle large-size data.
     
  • It is easy to use and integrates with other components.
     
  • The clarity in terms of cost.
     
  • It reports directly consumed from the views of the client.
     
  • Access the Data You Need on Demand.

Disadvantages of BigQuery

Disadvantages

If there are pros to something, then there must be cons also. Let's now discuss some cons of BigQuery.

  • It works well with simple tables, which leads to data model difficulty.
     
  • The UI/UX is a little difficult to use at the starting phase on a small screen.
     
  • Queries that are not executed return redundant data.
     
  • It lacks tooling support outside the GCP ecosystem.

Frequently Asked Questions

Is a google cloud platform SDK for BigQuery available?

Yes, Google's SDK package has a group of client libraries. The Command line tools for GCP products and services are available here. We've also combined the Google SDK with Docker. It provides a "run anywhere" solution. 

Can we use Google BigQuery with standard SQL?

Yes, you can use standard SQL. SQL constructs are supported by Google BigQuery, Amazon Redshift, and others. There may be some limits for the exact use case, but SQL is commonly available.

In BigQuery, how is data encrypted?

BigQuery will encrypt all data in transit and at rest. This is done by default. At the same time, there is typically a performance penalty of up to 50% for that level of encryption. Google has overcome that hurdle so that you will not notice the impact of end-to-end encryption in BigQuery.

Conclusion

We have discussed the topic of BigQuery in this article. In detail, we have seen the need, features, pros, and cons of BigQuery.

We hope this blog has helped you enhance your knowledge of BigQuery. If you want to learn more, check out our articles.

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Happy Learning!

Topics covered
1.
Introduction
2.
Need of BigQuery
3.
Features of BigQuery
4.
How to use BigQuery?
5.
Advantages of BigQuery
6.
Disadvantages of BigQuery
7.
Frequently Asked Questions
7.1.
Is a google cloud platform SDK for BigQuery available?
7.2.
Can we use Google BigQuery with standard SQL?
7.3.
In BigQuery, how is data encrypted?
8.
Conclusion