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Amazon Redshift vs. Google BigQuery : Data Warehouse Comparsion analysis


Redshift vs BigQuery : Data Wareshouse Comparison Analysis | Smileshark

Written by Hyojung Yoon


Hello! Today we're going to explore the world of cloud data warehouses, specifically Amazon Redshift and Google BigQuery. If you're curious about how companies store, analyze and manage massive amounts of data in today's data-driven world then the answer lies in Cloud data warehouses like Amazon Redshift and Google BigQuery.


In this article, we'll first take a quick look at what a data warehouse is and what its characteristics are, and then do a comparative analysis of Redshift and BigQuery, two leading cloud data warehouse solutions. Let's get started!

 
 

What is a Data Warehouse?

The importance of data today

People and machines produce vast amounts of data every day. It enables businesses, governments, and individuals to make informed choices and discover new insights. The importance of data is only growing as our reliance on technology increases, and data has become a key part of modern life. Understanding and utilizing data can help you make better decisions, increase efficiency, and make the most of the opportunities it presents.

Data Warehouse and Cloud Data Warehouse

What is a Data Warehouse?
What is a Data Warehouse?

Data warehouse is a portmanteau of the words Data and warehouse and refers to a structured and integrated environment of technology that aims to accumulate and integrate data in a time series to support efficient decision-making from a strategic perspective. In other words, it is a database that integrates, coordinates, and manages data from each of the distributed databases within an organization and converts them into a common format for efficient decision-making.


Cloud data warehouse is a centralized, large-scale storage solution that enables organizations to store and analyze vast amounts of data in the cloud, making it easy to access and secure. Cloud data warehouses reduce an organization's infrastructure costs, increase flexibility, and are easily scalable.


Here's an analogy to help you understand.

Data Warehouse and Cloud Data Warehouse
Data Warehouse and Cloud Data Warehouse

If you think of books as data, you can think of your bookshelf, the place where you store and organize your books, as a "data warehouse" for your books. Let's say you want to share this collection of books with your friends and want them to be able to access your books anytime, anywhere. To do this, you decide to move your entire book collection to an online platform like an e-library. This is similar to a cloud data warehouse, where the data(in this case, books) is stored on the internet and can be easily accessed from anywhere.


So, a cloud data warehouse is like an online bookshelf where all your data(books) are stored and organized for easy access, analysis, and sharing with others. Just like you can easily search for the book you want in an online library, a cloud data warehouse allows you to quickly search and analyze large amounts of data to find useful information and insights.


Features or Data Warehouse

Features

설명

Subject Oriented

​- Topic-driven rather than task-driven (data is classified and stored according to the organization's work topics)

- Data structures that are not tied to a specific business function or application

Integrated

- Ensure data consistency with data modeling

- Ensure data uniformity through enterprise-wide data standardization

- Perform a series of transformations to integrate data as it is acquired

Time Variant

- Data is retained for long periods of time, storing snapshots of values at every moment in time to enable analysis and prediction of past and present trends.

- Necessary for trend analysis of work changes or development over a period of time

Non-volatile

- After initial data load, no data refresh process exists

- There is only data loading and data access

- Even if data changes occur, they are reflected in the form of a snapshot without updating the change requirements

 

Benefits of Cloud Data Warehouse

1. Scalability

Cloud data warehouses are elastic, so they can seamlessly scale up or down as your business needs change.

2. Flexibility

Cloud data warehouses support a variety of data types, including structured, semi-structured, and unstructured data, and make it easy for businesses to integrate data from different sources.

3. Agility

Cloud data warehouses can quickly process and analyze large amounts of data in near real time. This enables faster, more agile decision-making by providing the insights you need to make informed decisions.


4. Cost Optimization

Cloud data warehouses can be more cost-effective than traditional on-premises data warehouses because businesses don't have to invest in expensive hardware, software, and maintenance.

5. Enhanced security

Cloud data warehouses come with advanced security measures to protect your data from unauthorized access and potential data breaches. These include encryption, firewalls, and regular security updates to keep your data safe.


6. Collaboration

Cloud data warehouses enable collaboration between teams by allowing multiple users to access and analyze data anytime, anywhere.

 

Comparison of Cloud Data Warehouses


Amazon Redshift

Google BigQuery

Ease of Use & Setup

Easy to set up and use

Slightly easier due to severless architecture

Data Storage

Optimized for structured data

Handles both structured and unstructured data

Real-time Analytics

Limited real-time analytics support

Robust real-time analytics support

Pricing​

Pay-as-you-go, based on nodes and hours of usage

Pay-as-you-go, based on data storage and query volume

Scalability

​Scalable with a managed cluster architecture

Scalable with serverless architecture

​Security

Robust security features

Robust security features

Integration

Seamless integration with AWS services

Seamless integration with Google Cloud Platform services

Machine Learning

Integration with Amazon SageMaker

Integraion with BIgQuery ML

1. Amazon Redshift

Provide a Cloud Data Platform focued on leveraging customer data
Amazon Redshift

Features and Advantages

  • Fully managed cloud data warehouse at petabyte scale

  • Seamless integration with other AWS services

  • Easily store all processing results in an S3 data lake

  • 10x faster performance than traditional DW with columnar storage, massively parallel query execution, and more

  • Secure and manageable option for organizations with high security and compliance needs

  • Use when you need to perform complex queries quickly or need regular batch processing

Differences from traditional DW

  • Store and analyze your data in a single S3 to get insights you can't get by analyzing individual data alone

  • Pay-as-you-go pricing model where you only pay for the resources you use

  • Analyze not only structured data but also semi-structured and unstructured data


2. Google BigQuery

Analyze data optimized···Supports a variety of infrastructure environments
Google BigQuery

Features and Advantages

  • Fully managed cloud data warehouse at petabyte scale

  • Built-in BI engine, powerful machine learning capabilities available through SQL extensions

  • Easy to use and set up with a highly scalable serverless architecture

  • Seamless integration with other Google services

  • An attractive option for businesses looking for a powerful and easy-to-use data warehouse

  • Use when you need real-time analytics for large datasets

Differences from traditional DW

  • Relatively easy to analyze data because you only need to know the SQL language

  • No need to install and operate as a cloud service

  • Can utilize powerful computing resources without investing in infrastructure

 

Conclusion

Amazon Redshift and Google BigQuery are both powerful cloud data warehouses, each with their own strengths and benefits. Redshift excels at scalability and data warehousing for AWS users, while BigQuery shines with its serverless architecture and seamless integration with other Google Cloud services.


If you don't have any technical background, think of it as choosing between two flavors of ice cream - they're both delicious in their own right, but it depends on your preferences and the occasion.


Similarly, the best choice for your business will depend on your needs, budget, and existing infrastructure. Understanding the differences between data warehouses and choosing the one that best suits your needs will enable you to make an informed decision that will help your business grow.


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