5 Reasons Why Cloud-Based Data Warehouses are Surging in Popularity Now

5 Reasons Why Cloud-Based Data Warehouses are Surging in Popularity Now | Data Science and Analytics | Emeritus

The technological landscape has seen a tectonic shift in the last decade as businesses adopted cloud computing en masse. In fact, the global market will be worth $2.1 trillion by 2030. Cloud computing involves delivering various computing services, including servers, storage, databases, and analytics, over the Internet. A company can either use a cloud-based data warehouse or a traditional data warehouse. The former is a data warehouse that is hosted on cloud computing infrastructure. In other words, businesses hire a cloud service provider (CSP) instead of building and running their infrastructure. According to a Market Research Future report, the data-warehouse-as-a-service market industry will grow from $2.68 billion in 2023 to $8.79 billion by 2030 at a compound annual growth rate of 21.9%. It is, therefore, important to take a deep dive into a cloud-based data warehouse and study the best examples of cloud data warehouses.

strip banner

How Does a Cloud-Based Data Warehouse Differ From Traditional On-Premises Data Warehouses?

As opposed to a cloud-based warehouse, some businesses use a traditional data warehouse and store and manage data on on-premises servers or data centers. Let’s look at some key differences between a cloud-based data warehouse and a traditional one:

Cost

A company incurs substantial capital expenditure for hardware, software licenses, and maintenance in a traditional data warehouse. On the other hand, a cloud-based data warehouse allows them to opt for a subscription model, which is more cost-effective for most businesses.

Scalability

It is more challenging to scale using an on-premises data warehouse because it involves purchasing and installing additional hardware. On the other hand, a company can enjoy on-demand scalability within cloud data warehousing solutions depending upon their needs and budget.

Infrastructure

A firm needs to set up the infrastructure within its physical premises, which requires a significant investment in hardware such as servers, storage, and networking equipment. However, it can avoid the hassle by hiring a Cloud Service Provider (CSP) such as Google or Amazon.

Accessibility

A company’s access is limited to the physical location of the data center, which can hinder remote access and collaboration, whereas a cloud-based data warehouse is accessible with the help of the Internet, providing greater flexibility for users to work from any part of the world.

Maintenance

An organization must maintain and update the hardware, software, and security of the data warehouse under a traditional setup. A CSP handles these tasks when a firm opts for cloud data warehousing solutions.

ALSO READ: How to Build a Successful Career in Data Science and Analytics?

What are the Key Benefits of Using a Cloud-Based Data Warehouse?

big-data-engineer-skills

1. Easy to Scale

A cloud-based data warehouse is primed to help a business scale up or down to meet its changing needs at the drop of a hat. It allows firms to be flexible, especially the ones that experience seasonal fluctuations in data volume or are unsure about their data storage needs.

2. Saves Costs

Cloud data warehouses operate on a pay-as-you-go model, which is typically more cost-effective than on-premises data warehouses. A company only pays for the resources used by them. Additionally, there is no need to maintain a staff to manage the data warehouse.

3. Offers Security

A cloud-based data warehouse is a sensitive spot, making security a top priority. Given this, every CSP spends millions of dollars to provide state-of-the-art security. They also have a team of security experts who monitor their systems for threats around the clock.

4. Easy to Integrate

It is easy to integrate a cloud-based data warehouse with other cloud-based services, such as machine learning, Business Intelligence (BI) tools, and data visualization tools. It allows companies to get the most out of their data.

ALSO READ: How Data Wrangling is Helping Businesses Make Better Decisions

What are the Different Types of Cloud Data Warehouses and Their Respective Strengths?

There are various types of cloud data warehouses, so it is essential to understand the nitty-gritty of each to pick the best option for your organization. Here are some popular ones:

Amazon Redshift

Amazon Web Services (AWS) is one of the biggest CSPs in the world. A key point in favor of Redshift is its ability to integrate with other AWS services. It is useful for analytical workloads and handling large datasets.

Google BigQuery

BigQuery offers a serverless architecture, erasing the need to manage infrastructure. It is among the best examples of cloud data warehouses. It is highly cost-effective with plans that use on-demand pricing.

Microsoft Azure Synapse Analytics

Azure Synapse Analytics is easy to integrate with other Azure services. It is also one of the best in cloud-based data storage and analytics. It offers support for both relational and non-relational data besides security features like Azure Active Directory integration.

Snowflake

A business can enjoy elasticity as Snowflake’s architecture separates storage and computing. It has support for diverse data types and semi-structured data with multi-cloud support (AWS, Azure, Google Cloud). It also optimizes query performance automatically.

ALSO WATCH: Information session on NYU Tandon School of Engineering’s Essentials of Cloud Security Program

How do You Choose the Right Cloud Data Warehouse Solution for Your Business?

1. Define Your Business Requirements

It is important to identify business goals, data storage needs, and analytical requirements to choose a platform that offers the right mix of cloud-based data storage and analytics. A firm must consider factors such as data volume, types of queries, and concurrency.

2. Optimize Performance

There are several types of cloud data warehouses, so it is imperative to evaluate the performance of each data warehouse for your specific use cases. They should be evaluated on features like indexing, partitioning, and query optimization.

3. Offers Security

It is crucial to check out the security features offered by each platform, including encryption, access controls, and authentication methods, because data is a precious resource. It must be checked whether the data warehouse complies with industry-specific regulations.

4. Provides Affordability

A company must obtain the pricing models of all cloud data warehouses so it can make an informed decision based on storage costs, computing costs, and data transfer costs. 

Can You Provide Examples of Successful Companies That Have Implemented Cloud-Based Data Warehouses?

python data structures

1. Netflix

The streamer migrated to Google BigQuery to enable faster data analysis and real-time insights. It needed a platform to analyze massive amounts of user behavior and preferences data to improve streaming quality. It also witnessed a reduction in its churn rate.

2. Airbnb

The company sought to manage and analyze data from millions of listings and bookings to optimize pricing, predict demand, and personalize guest experiences. It used Amazon Redshift to handle petabytes of data and gain insights into guest behavior and market trends.

3. Spotify

The company wanted to understand user listening habits and preferences to curate personalized playlists and recommend new music. It used Snowflake’s cloud data warehouse to analyze billions of data points in real-time and deliver highly personalized music recommendations.

4. Uber

The company used Microsoft Azure Synapse Analytics to optimize ride-hailing operations by analyzing data on driver availability, traffic patterns, and passenger demand. It allowed them to improve driver dispatch, predict demand surges, and dynamically adjust pricing.

ALSO READ: 5 Most Popular Cloud Computing Skills to Learn and Acquire in 2024

The rapid adoption of cloud technology is inevitable. It is, therefore, critical to learn how to leverage a cloud-based data warehouse at your organization. The only way to find the right cloud data warehouse is to develop skills designed to help you discern between various cloud warehouses. Emeritus offers a comprehensive list of courses to help elevate your skills to the next level. These courses are curated meticulously to cater to professionals seeking to unlock the potential of data science. They cater to everyone as our expert instructors bring a wealth of practical knowledge to the virtual classroom. Discover the power of data-driven decision-making and gain a competitive edge in your career by signing up for one of Emeritus’ data science courses today!

Write to us at content@emeritus.org

About the Author

Content Writer, Emeritus Blog
Mitaksh has an extensive background in journalism, focusing on various beats, including technology, education, and the environment, spanning over six years. He has previously actively monitored telecom, crypto, and online streaming developments for a notable news website. In his leisure time, you can often find Mitaksh at his local theatre, indulging in a multitude of movies.
Read more

Courses on Data Science and Analytics Category

Courses inAI and Machine Learning | Education Program  | Emeritus

Carnegie Mellon University School of Computer Science

Machine Learning: Fundamentals and Algorithms

10 Weeks

Online

Last Date to Apply: February 22, 2024

Courses inBusiness Analytics | Education Program  | Emeritus

Kellogg Executive Education

Business Analytics: Decision Making with Data

9 Weeks

Online

Starts on: February 27, 2024

Courses inCoding | Education Program  | Emeritus

MIT xPRO

Professional Certificate in Coding: Full Time

4 Months

Online

Starts on: February 27, 2024

US +1-606-268-4575
US +1-606-268-4575