Author: Prashant Sharma

Snowflake data lake vs. Data warehouse is a common question that business owners come across during data management. In the highly competitive business world, businesses are looking for ways to cost-effectively and quickly gather insights from the petabytes of data stored. 

The two widely used big data storage solutions include data lakes and data warehouses. The two terms data lake and data warehouse are often used interchangeably; however, they are slightly different. In this comparison guide, we’ll reveal the main differences between the two data storage solutions that allow you to store and compute data. 

What Is Snowflake Data Lake?

Snowflake’s cloud-built architecture supports your data lake strategy to meet specific business needs. The in-built Role-Based Access Control (RBAC) and Data Access Control (DAC) provide quick data access, query performance, and complex transformation. As the data is transformed through native SQL, governing and monitoring the access security becomes easy. 

Another unique feature of Snowflake is the Massively Parallel Processing (MPP) that allows you to securely and cost-effectively store data. The robust architecture can handle data workloads of diverse formats in a single SQL query. Furthermore, a data lake easily transforms structured, semi-structured, and unstructured data from storage on a single architecture. 

There are two ways you can utilize Snowflake:

• Either deploy Snowflake as your central data repository to supercharge performance, security, querying, and performance. 
• Or you can store the data in Google Cloud Storage, AWS S3, or Azure Data Lake to speed up data analytics and transformation. 

What Is Data Warehouse?

In simple words, a data warehouse is a system used for data analytics and reporting. It acts as a central repository to store large amounts of data gathered from different data sources. In a data warehouse, you can find highly transformed, structured data pre-processed and designed to serve a specific purpose. 

However, before choosing a data warehouse, it’s vital to understand its architecture. 

• Source Layer: The warehouse collects structured, unstructured, and semi-structured data relevant to the business needs. 
• Staging Area: In the next layer, the warehouse extracts and cleanses data to structure it in a specific format. 
• Data Warehouse Layer: It consists of a relational database management system that stores the clean data and the metadata. 
• Data Marts: All the information related to specific functions of an enterprise is stored in the data mart. 
• Analysis Layer: It supports access to integrated data to meet business needs. The entire data undergoes analysts to find hidden patterns or issues. 

No matter which data management solution you choose, it’s important to understand the right storage, management, and data analysis criteria. If you want to understand which is better for you: data lake or data warehouse, contact the data experts of Inferenz. 

Head-to-Head Comparison Between Data Lake & Warehouse

According to a GlobeNewswire report, the data warehouse market size will cross USD 9.13 billion by 2030. On the other hand, the data lake market is all set to cross USD 21.82 billion by the end of 2030. That said, it is clear that data lakes are becoming more common to store data compared to warehouses. 

But before you choose, let us compare the two data storage solutions — data lake and data warehouse — based on different factors. 

Storage 

A data lake stores raw data in its native format and is only transformed when it has to be used. On the other hand, a data warehouse stores data after its extraction from transactional systems. All the data in the warehouse is clean and transformed as per business needs. 

Data Capturing 

Data lakes collect and store real-time data in raw and unprocessed data formats. They capture all forms of data, irrespective of their formats or sources. Conversely, data warehouses capture only structured information and store them in specific schemas. 

Data Timeline 

Cloud data lake consists of raw data, which has no current use. In the future, data analysts can access and analyze the data to gather insights. Conversely, a data warehouse contains processed data. Hence, the source is particularly captured, analyzed, and used to serve the specific purpose in real-time. 

Users 

Data lake generally suits users with knowledge of advanced analytical tools. Data scientists, data engineers, and analytical data engineers use their big data tools to work on varied large datasets. However, a data warehouse is suitable for operational users as it can answer business-specific questions quickly. 

Tasks 

As a data lake contains information from disparate sources, it is suitable for data analytics. Users can access large volumes of data and seek in-depth data insights. On the other hand, data warehouse primarily focuses on some predefined business questions. In short, a data lake can help users with multiple tasks, while a data warehouse generates specific reports. 

Schema Positioning 

Data lake follows a schema-on-read strategy, while data warehouse follows a schema-on-write strategy. The “Schema-on-Read” structure means schema is defined after data storage in a data lake. Conversely, the “Schema-on-Write” structure means schema is typically defined before data storage in a data warehouse. 

Which Is Better: Snowflake Data Lake Vs. Data Warehouse?

The right choice between a data lake and a cloud data warehouse will depend entirely on business needs. For instance, if you’re an eCommerce company with multiple departments, data warehouses can be a good option to get all important data at a single location. 

On the other hand, if you’re a social media company where the data is usually unstructured, a data lake can be a good choice. Often, many businesses use both storage options to build data pipelines. 

A data lake and a data warehouse combination will help you collect, store, transform, and analyze business data under a single platform. If you’re still confused between Snowflake data lake vs. Data warehouse, get in touch with the experts of Inferenz. 

FAQs About Data Lake Vs. Warehouse 

How is Snowflake different from other data warehouses? 

Snowflake enables faster, more flexible, and easier-to-use data storage, processing, and analytic solutions than other data warehouses. 

Is Snowflake a database or ETL?

Snowflake supports ELT and ETL, and it works effectively with various data integration tools, including Talend, Tableau, Informatica, etc.

What are the benefits of a data lake over a data warehouse? 

Data lake helps in real-time decision analytics as it utilizes large quantities of coherent data and deep learning algorithms. 

Data warehouse architecture is a complex information system that contains commutative and historical data from different sources. In other words, it is the structural representation of the concrete functional arrangement on which the enterprise data warehouse is constructed.

Enterprise data warehouses and their architecture vary depending on the elements of the organization’s situation. The three most common architectures include the basic, staging area, and the combination of the staging area and data marts.

In this guide, we’ll help enterprises understand the complete architecture, types, and best practices related to cloud data warehouses in detail.

Characteristics Of Data Warehouse

Data virtualization and warehouse are often used interchangeably; however, they are different from each other. The process of data warehousing involves the extraction and electronic storage of data for ad-hoc reporting and queries. On the contrary, data virtualization means accessing, managing, and retrieving critical business data. Another aspect of data virtualization is that it does not collect or duplicate the data in a physical repository.

Before we explain the three main types of data warehouse architecture, here are the key data warehousing characteristics.

Subject-Oriented

A data warehouse is subject-oriented as its purpose is to render information regarding the theme rather than the company’s ongoing business operations.

The subjects, in this case, can be anything from sales and marketing to distribution. One of the main purposes of a data warehouse is to focus on data modeling and analysis to make informed decisions.

Integrated

All the similar data from the different databases are integrated into a standard unit of measure. The data stored in the warehouse is collected from disparate sources like relational databases, flat files, mainframes, etc.

Time-Variant

The data warehouse has an extensive time horizon than operational systems. This is because all the data stored in the warehouse is recognized within a particular period. Another unique aspect of a data warehouse is that once the information is inserted, it can’t be changed or updated.

Non-Volatile

The non-volatile nature of data warehouses means that previous data is not erased, whereas only new information is inserted into it. The historical data is analyzed to help you understand what and when the changes happened.

Types Of Data Warehouse Architecture

The global data warehouse market is expected to cross $51.18 billion by 2028, implying companies prefer storing their data in a single source of truth. But before you choose any data warehouse, it’s vital to understand its architecture. It generally consists of three tiers:

Single Tier Architecture

A single-tier data warehouse architecture aims to minimize the amount of information stored. Its objective is to remove data redundancy. However, it is not frequently used.

Two-Tier Architecture

Two-layer architecture aims to separate physically available sources and data warehouses. However, this is not expandable and has connectivity problems due to network limitations.

Three-Tier Architecture

One of the widely used modern data warehouse architectures is a three-layer structure.

• Top Tier: Top tier comprises the client-side front-end of architecture. This tier uses all the transformed and logically applied information for different business processes.
• Middle Tier: The OLAP (Online Analytical Processing) server is implemented using either MOLAP (Multidimensional Online Analytical Processing) or ROLAP (Relational Online Analytical Processing) model. The layer acts as the mediator between the database and the end user.
• Bottom Tier: In this layer, the data is cleansed, transformed, and loaded using the back-end tools.

A modern data warehouse can store both structured and unstructured volumes of data. If you want to integrate a data storing solution or migrate data from a traditional database to the cloud, contact the experts of Inferenz today.

Data Warehouse Architecture Best Practices

Below are some best practices you’ll need to follow to design the data warehouse architecture.

• Follow the top-down and bottom-up approaches to design a data warehouse.
• Ensure that the data is processed accurately and quickly when consolidated into a single version of the truth.
• Thoroughly develop the complete data acquisition and cleansing process for the data warehouse.
• Design metadata architecture that eases metadata sharing between different data warehouse components.
• Consider the 3NF data model to ensure that the data model is integrated and not only consolidated.

Manage Your Data Better With Inferenz Experts

Different types of data warehouses store, centralize, and query large volumes of data from multiple sources. In the data warehouse structure, the metadata plays a crucial role. It determines the source, values, features, and use of data. Only a well-designed data warehouse is the foundation of a successful BI or analytics program. It will help you improve data quality, speed up data retrieval and analysis, and enhance overall decision-making.

Many companies are increasingly choosing modern data warehouses and cloud data warehouses. If you want to integrate the modern data warehouse solution into your business, contact the Inferenz data experts today. The professionals will help you improve decision-making and bottom-line performance by understanding the data warehouse architecture.

FAQs

What is OLAP in data warehousing? 

In simple terms, OLAP is software that performs multidimensional analysis at high speeds from a data mart, warehouse, or other centralized data storage.

What are the 4 components of a data warehouse? 

The components of a data warehouse system are a central database, metadata, access tools, and ETL (extract, transform, load) tools.

What is the main purpose of a data warehouse? 

The data warehouse resembles a central data repository that can be analyzed to make more informed business decisions. Data flows from relational databases, transactional systems, and other sources to the data warehouse to help analysts use the right information for internal business operations.

Data science vs. cloud computing is a long-running debate gaining immense popularity in the business world. Even though both technologies are interrelated and work with data, they are slightly different. With the help of data science, you can gather essential analysis from vast amounts of data. On the other hand, cloud computing allows you to analyze the collected data and store it in the cloud infrastructure.

With technology becoming important in the business world, organizations are utilizing the power of tech to store and process data. Instead of choosing one technology, organizations employ services of both sectors to use the data stored in the cloud.

What Is Data Science And Cloud Computing?

Before exploring the differences between cloud computing and data science, here is some basic information.

Cloud Computing

Cloud computing, in simple words, determines the method of hosting remote server networks on the Internet. The main aim of these servers is data storage, management, and processing. Some popular cloud computing platforms with high revenue include Amazon Web Services (AWS), Microsoft Azure, Google Cloud, IBM, SAP, Oracle, etc.

There are multiple benefits that the cloud provides to organizations, including:

• Makes business tasks agile
• Cost-effective and flexible
• High scalable and reliable
• Improves operation efficiency

Cloud services mainly include deployment and service models. The deployment type models include:

• Private cloud: It is a privately outsourced data center infrastructure with high security.
• Public cloud: It refers to a cost-effective model generally available on the Internet.
• Hybrid cloud: It blends both private and public cloud types. However, the risk of a security breach is high in this case.

Meanwhile, the different cloud servers include:

• Infrastructure as a Service (IaaS)
• Platform as a Service (PaaS)
• Software as a Service (IaaS)

Data Science

Data science is another important technology that helps enterprises better centralize and utilize their data stored in the cloud database. It involves transforming, inspecting, cleaning, and modeling data available to extract vital information. Once the big data is gathered and stored by cloud computing, the role of data scientists and data analytics starts. All the helpful information is extracted after removing irrelevant forms of data and stored in the cloud to make business decisions.

Below are a few ways why organizations should pay more attention to the field of data science.

• Reduces unnecessary costs by removing duplicated information.
• Makes smart and quick business decisions with the information gathered.
• Analyzes customer preferences to avail of customized services or products.

Verdict: Data science depends on cloud computing, and organizations looking to improve their business operations must focus on incorporating both technologies with Inferenz experts. We have a team of data analysts who can help you maximize the value derived from stored data.

Key Differences Between Data Science And Cloud Computing

Now that we have covered the basics of the two booming technologies, here are the differences between the two.

• Data science is dependent on cloud computing, whereas cloud computing is independent of data analytics.
• The aim of data science is to work on improving a particular organization, whereas cloud computing has solutions to data-intensive computing.
• Data scientists inspect, clean, transform, and model data to make it useful; meanwhile, cloud computing focuses on data storage and retrieval.
• Technologies involved in data science are Python, Apache Spark, etc., whereas IaaS, PaaS, and SaaS are involved in cloud computing.
• Some examples of cloud service providers include Microsoft Azure data cloud, Amazon Web Services, IBM cloud, etc., whereas data science involves Apache, MapR, etc.

Which Is Better: Cloud Computing Or Data Science

Data science vs. cloud computing is a widespread debate for organizations. However, it is not simple to figure out which technology is better. With the growth of big data, every organization needs to focus on adopting both technologies to gain better insights from the data.

Whether you choose the best cloud computing platform or want to migrate data to the cloud, Inferenz experts can help you with the task. Our data analysts and data scientist experts work together to help you understand the differences between data science vs. cloud computing and get the maximum value of data stored in clouds.

FAQs On Cloud Computing Vs. Data Science

What should you choose: data science or cloud computing? 

Both data science and cloud computing have close relationships. Companies are increasingly storing large chunks of data in a traditional database. Data science is a technique that helps in aggregating, cleaning, preparing, and analyzing the involved data. Meanwhile, cloud computing provides continuous and dynamic IT services to structure data. With that in mind, data science and cloud computing are essential for business success.

Is cloud computing part of data science?

Cloud computing and data science go hand in hand. A data scientist analyzes different types of datasets stored in the cloud to make smart business decisions. Hence, it won’t be wrong to say that data science depends on cloud computing. The best cloud for data science you can choose for your organization is Amazon Web Services.

What are the benefits of data science?

Some of the major benefits of data science include the following:

• Automating business tasks
• Complex data interpretation
• Business intelligence
• Improved business prediction
• Helps in marketing and sales

Cloud market share has witnessed tremendous growth in the past years. The cloud computing market is booming exceptionally and covers a vast and complex ecosystem of technologies, services, and products. Given the rise of cloud computing adoption, many public and private cloud solutions compete to get the maximum market share. 

Understanding the complex global cloud market is increasingly difficult for consumers and enterprises. If you’re confused about the same, this guide is for you. In this cloud computing article, we will walk you through different types of cloud services, the global cloud infrastructure market leaders, and how the cloud market growth rates remain so far.

Understanding Three Main Types of Cloud Computing Services

Before we explore the leading public cloud solutions and service providers — Amazon AWS, Microsoft Azure, and Google Cloud Platform — you must know the three major types of cloud computing services. 

Infrastructure as a Service (IaaS) 

As the name suggests, the Infrastructure as a Service cloud type focuses on cloud infrastructure. IaaS gives you on-demand access to computing resources, including but not limited to servers, storage, and networking. The best part about IaaS is that it allows you to scale hardware resources depending on processing and storage needs. 

Software as a Service (SaaS) 

With the help of Web or API, you can access the provider’s cloud-based software. Unlike other service types, you don’t have to install, upgrade, or manage the software application on a local device. Instead, you can use the provider’s application to store and analyze your data, whereas the provider will handle the upkeep of the application. 

Platform as a Service (PaaS) 

In PaaS, the cloud computing service type helps access the cloud environment where you can develop, manage, and host applications. Additionally, the consumers can leverage the range of tools through the platform for development and testing. The provider is responsible for the cloud service type’s security, backups, operating system, and underlying infrastructure. 

If you’re planning to choose the best cloud services, feel free to contact Inferenz experts. Not only do our experts help enterprises choose the right cloud platform, but we also help them migrate data to the cloud safely.

Cloud Market Share in 2022

The public cloud market that comprises Software as a Service, Infrastructure as a Service, and Platform as a Service showed 29% positive growth in 2021. IDC (International Data Corporation) market research indicates that the three types of services generated $408.6 billion in revenue in the same year. The top 3 cloud computing platforms with the maximum cloud market share include AWS, Azure, and GCP.

AWS 

Statista report reveals that Amazon Web Services holds the maximum cloud market share with 34% of the world’s SaaS, IaaS, and PaaS cloud spending. In Q1 2022, the largest cloud, AWS cloud, generated $18.5 billion in revenue growth, making it the clear market leader. 

Microsoft Azure 

Azure is giving tough competition to Amazon Web Services in terms of cloud worldwide market share. The Statista report indicates that Microsoft Azure holds a 21% share in Q3 2022. Microsoft’s cloud service Azure offers Intelligent Cloud services with a tremendous growth rate of 26% to $20.9 billion. 

Google Cloud Platform 

As of Q3 2022, the GCP market share covers 11% of the worldwide cloud service market. Throughout the past years, the public cloud service platform’s revenue growth has consistently increased by 45%. That puts the platform third in the list of leading cloud service providers, after AWS and Azure. Another report by Canalys reveals that Google cloud services spending exceeds US$50 billion, indicating that GCP cloud is making its way to the top. 

These top three cloud providers (AWS, Azure, and GCP) hold 64% of the cloud market share, indicating that enterprises prefer them over other platforms.

Choose The Leading Cloud For Your Business

Cloud offerings have dramatically changed the whole IT market. With the leading cloud providers holding the maximum market share, consumers should have in-depth knowledge of every option available on the market. 

Considering the market share, the pricing models of top cloud providers, and the benefits of cloud computing platforms will help you choose the best cloud platform. 

If you’re still confused about which cloud service to choose, contact the Inferenz experts. The experienced team can help you understand which provider has the maximum public cloud market share and will expand in 2023 and beyond. 

FAQs 

What are the top five cloud platforms?

The market is dominated by five cloud platforms: Amazon Web Services, Azure, Google Cloud Platform, IBM, and Alibaba. 

What does the PaaS market look like?

According to the Gartner report 2019, the PaaS market is expected to generate $20 billion. The market will continue to grow with 550 cloud platforms and 360 vendors. However, compared to the IaaS and SaaS markets, PaaS grows less, with only 10 out of 360 vendors offering 10+ services. 

Which provider has the largest share of public cloud infrastructure?

The data from Gartner on the worldwide IaaS market shows annual revenues of $32.4 billion. The expected growth was 31.3% from $24.7 billion in 2018. According to the Gartner report, the market is dominated by Amazon Web Services, with a share of 47.8%.

Google Cloud vs. AWS detailed comparative analysis will help you choose the best cloud computing platform. In the modern digital world, cloud computing is on the rise. Enterprises must leverage advanced technology by selecting the best cloud service provider. 

The top two cloud providers – AWS and Google Cloud Platform – stand neck to neck in the virtual public cloud world. This article on Google Cloud and AWS comparison highlights and elaborates on the major differences between the cloud providers to determine who is the market leader.

Differences Between Google Cloud Vs. AWS

AWS Vs. Google Cloud Market Share

When we compare the major cloud providers – Google Cloud Platform and AWS – based on market shares, Amazon Web Services is leading with around 34% of the public cloud market share. On the other hand, Google Cloud is making its name among thriving cloud communities and has a market share of 11%, as predicted by Statista. 

Even though GCP is tremendously progressing, it still lags behind AWS regarding cloud infrastructure market share. 

Verdict: Hence, we can conclude that AWS dominates the public cloud market and is one of the best cloud computing solutions. 

Google Cloud Vs. AWS Services

Google Cloud and AWS are the two top cloud giants that offer many services. AWS, the leading cloud provider, provides 200+ services, whereas GCP is catching up with over 60 services. All the services are categorized under computing, storage, database, and networking. 

Computing Services 

Services	Google Cloud	AWS
IaaS	Google Compute Engine	Amazon Elastic Compute Cloud
PaaS	Google App Engine	AWS Elastic Beanstalk
Serverless Functions	Google Cloud Functions	AWS Lambda
Containers	Google Kubernetes Engine	Amazon Elastic Compute Cloud Container Service

 

Database Services 

Services	Google Cloud	AWS
RDBMS	Google Cloud SQL	Amazon Relational Database Service
NoSQL: Indexed	Google Cloud Datastore	Amazon SimpleDB
NoSQL: Key–Value	Google Cloud Datastore, Google Cloud Bigtable	Amazon DynamoDB

 

Networking Services 

Services	Google Cloud	AWS
Virtual Network	Virtual Private Cloud	Amazon Virtual Private Cloud (VPC)
Elastic Load Balancer	Google Cloud Load Balancing	Elastic Load Balancer
DNS	Google Cloud DNS	Amazon Route 53
Peering	Google Cloud Interconnect	Direct Connect

 

Storage Services 

Services	Google Cloud	AWS
Object Storage	Google Cloud Storage	Amazon Simple Storage Service
Cold Storage	Google Cloud Storage Nearline	Amazon Glacier
Block Storage	Google Compute Engine Persistent Disks	Amazon Elastic Block Store
File Storage	ZFS/Avere	Amazon Elastic File System

 

Verdict: Clearly, AWS provides more services and products compared to Google Cloud services. Hence, AWS global cloud is the winner here. 

Google Cloud Vs. AWS Pricing

Amazon Web Services and Google Cloud pricing models are based on the machine type they offer. Here is a simple comparison between AWS cloud and GCP. 

• AWS Small Instances: The basic instance of AWS includes 8 GB of RAM and two virtual CPUs that will cost around US$69 per month. 
• GCP Small Instances: Compared to AWS, GCP provides you with 8 GB of RAM, and two virtual CPUs, at US$25 per month. Hence, with an approximate 25%, GCP is much cheaper than AWS. 
• AWS Largest Instance: It includes 128 virtual CPUs and 3.84 TB of RAM, costing you around US$3.97 per hour. 
• GCP Largest Instance: Unlike small instances, the largest instance of Google storage services costs more. You’ll have to pay US$5.32 per hour for 160 virtual CPUs and 3.75 TB of RAM. 

Verdict: Google Cloud service provider comes out as the winner as GCP’s per-second model is much cheaper than AWS’ per-hour model billing. If you want to choose a basic instance, going ahead with the Google Cloud Platform is ideal.

Google Cloud Vs. AWS: Availability Zones

Every organization wants to choose a cloud provider that offers robust services with minimal outage possibility. Availability zones and regions directly impact the likeness of outages and robustness. 

Being the first of its kind, Amazon Web Services has had enough time to expand its service and network. It has already expanded its infrastructure to 21 geographic regions worldwide. All in all, AWS has 66 availability zones, while 12 are on the way. 

On the contrary, GCP has 61 zones worldwide and has been made available in 20 geographic regions. 

Verdict: Clearly, AWS is the winner here. 

AWS Vs. Google Cloud: Security 

Security is one of the most critical parameters organizations consider when choosing a cloud provider. Even though AWS and Google Cloud provide cutting-edge security features, the former takes a more simplistic approach. 

Customers of AWS are responsible for the cloud security of their own data, applications, user accounts, etc. On the contrary, GCP’s shared responsibility model is a little complex. Therefore, it is less preferable for beginners. 

Verdict: The right choice between the two depends on the user’s needs. 

Google Cloud Vs. AWS: Which Is Better

Google Cloud and AWS cloud computing platforms are the leading choices for enterprises migrating data to the cloud. However, the choice between Google Cloud and AWS will depend on your business requirements. 

When you compare the cloud platforms from Google and Amazon, it’s vital to understand their features. Let us compare them here.

Features/Pros Of Google Cloud 

• Wide network access 
• Rapid elasticity 
• Resource pooling 
• Better UI to improve user experience 
• Constant addition of more languages and operating system 
• Provides an on-demand self-service 

Cons Of Google Cloud 

• Lacks features compared to using Amazon Web Services 
• Complex to start 
• No free tier option is available 

Features/Pros Of AWS

• Provides hybrid capabilities 
• Enables you to deploy applications in multiple regions worldwide 
• The low total cost of ownership compared to private/dedicated servers 
• Offers centralized management and billing 

Cons Of AWS 

• Launching multiple app instances is complex 
• Lengthy and complicated AWS deployment process 
• Non-tech people find AWS a little challenging 

Verdict: Undoubtedly, both cloud storage and computing service providers are good. However, you should understand the different services your organization needs and compare prices before choosing one.

Choose The Best Cloud Computing Services For Business

If you intend to migrate data and use cloud services, the three top providers you’ll come across include Amazon Web Services (AWS), Microsoft Azure, and Google Cloud. Amongst all, GCP and AWS have dominated the cloud space. 

Compared to traditional on-premise deployment, the two cloud service providers are preferred as they offer an extensive range of products and services. At Inferenz, our data experts can help you seamlessly migrate data from on-premise to the cloud. 

If you are still confused about Google Cloud vs. AWS debate, contact our experts right away!