What is a Cloud Data Platform?

August 22, 2022

In today’s global, highly competitive environment, the value of data has skyrocketed as a way for organizations to get ahead. Data, more than anything else, determines whether or not an enterprise will be able to successfully manage its own complicated IT infrastructure as well as develop new technologies just when they are needed to better serve its customers. 

Managing this data and gaining visibility into it is now a top priority for many companies. Unfortunately, many businesses still rely on old-fashioned storage methods with large facilities housing thousands of servers and hard drives all used to store their data. Although this may have worked in the past, there are now much more effective ways to manage data.

A cloud data platform offers splendid flexibility and scalability, making it a must for businesses. Now exactly, what is a cloud data platform? Continue reading to explore all about a cloud data platform, types, and benefits for businesses. 

What is a Cloud Data Platform?

what is cloud data platform

A cloud data platform allows enterprises to move their data from traditional storage to the cloud. It is an integrated software solution that enables businesses to collect, store, process, and use data in the cloud while offering an integrated view to manage and secure the data adequately. 

There are many benefits to utilizing a cloud data platform. These include rapid scaling and reducing IT hardware requirements. Instead of having massive facilities to manage all your data, everything you need is stored remotely, available to access whenever and wherever you want it.  

Furthermore, as your needs for data and analytics change, a cloud data platform makes it easy and fast to change how much of the cloud you are utilizing. Cloud data platforms are also useful because they increase access to data. In the end, analytics are only as valuable as they are visible. Data stored in the cloud can be made visible and accessible to everyone in your organization without impacting your costs. 

Types of Cloud Services

Cloud services are basically software or platforms hosted by third parties and are made available through the Internet. There are many cloud services available to support your business. Most likely, if you are reading this, your organization is already taking advantage of the opportunities and benefits that come from utilizing some form of cloud services. This could be a cloud-based email server, web host, or any one of several other cloud services. 

Cloud data services are specifically designed to provide a way to use the cloud as a way to store your data instead of using some form of local storage. However, you don’t just want to put all that data in some basic repository. You need to be able to analyze it in real time, synthesize it, and get actionable insights from it. That’s where a cloud data platform comes in.

With a cloud data platform, you can deliver intelligence from data across your enterprise and truly build your strategies and operations around those insights. There are basically three types of cloud services as mentioned below. 

IaaS

IaaS, or Infrastructure as a Service, provides on-demand computing, storage, and networking resources. Popular examples of IaaS include IBM Cloud, Digital Ocean, and Amazon Web Services. 

In IaaS, the cloud service provider manages the infrastructure including the network, server, data storage, and virtualization. The provider has the responsibility to handle outages, hardware issues, and repairs.

Users of IaaS environments get access through a dashboard or API and they can manage things such as operating systems, middleware, and apps. The users have to pay on a per-user basis. 

PaaS

Platform as a Service, or PaaS, offers a shared cloud platform with complete development and deployment. Some examples of PaaS include Microsoft Azure, AWS, and Cloud Factory.

PaaS environments provide on-demand access to ready-to-use cloud platforms for developing and maintaining applications. In this the hardware and application software are managed by the service provider and the users get access to the apps and the data used in the app. 

SaaS

SaaS or Software as a Service offers subscription software services using the internet. These services are managed by third-party vendors. Popular examples of SaaS include Dropbox and Salesforce. 

SaaS apps are mobile or web apps. In SaaS, the users manage the bug fixes, software updates, and other general software maintenance tasks. They can connect to the cloud application using API or a dashboard.  

Benefits of a Cloud Data Platform for Businesses

cloud data platform

For businesses seeking solutions to maximize their potential in the contemporary digital era, a cloud data platform is highly essential. The right platform can help businesses to manage data reliability, accessibility, and security. Here are some primary benefits of a cloud data platform for businesses. 

Accessibility

Cloud data platforms allow the creation of a data lake that democratizes data and helps in sharing anytime and anywhere among both remote and onsite users without having to worry about distance. 

Top-Notch Security

A top-notch cloud data platform helps businesses to reexamine their data privacy methodologies and processes. These platforms offer various layers of protection ensuring the protection of valuable data from malicious activities. Features like authentication, authorization, and encryptions help to ensure that the data is safe and secure. 

Better Data Reliability 

Cloud data platforms are specifically designed to ensure a high level of reliability. Businesses can access their important information and data without having to worry about system failures or data outages. 

Cost Savings

An effective cloud data platform simplifies the IT operating environment. Moreover, it eliminates the need to buy and maintain expensive IT licenses. Cloud data platforms offer operation expenses, which means you pay only for the services you use, making it less expensive and more flexible as compared to the in-house data center. 

Smooth Deployment

It allows hybrid and on-premises deployment of data in the cloud making it flexible to use. Businesses can choose the deployment method that best suits their operational needs. 

A cloud data platform quickly ingests structured or unstructured data to make the analytics stronger. Moreover, it scales easily and quickly to adjust to the evolving needs of data and analytics. 

Expandable Storage

It allows businesses to expand their storage to meet the requirements. It eliminates the need to buy and maintain hardware to store data and helps in cost saving in the long run.  

Regular Automated Backup 

Cloud platforms offer automated backup schedules, which means that the data is backed up at regular intervals. It allows businesses to stay assured that their data is safe. Moreover, it reduces the risk of data loss due to unforeseen events or human errors. 

Use Cases of a Cloud Data Platform

cloud data platform

Data is literally everywhere and reliable data is a requirement for businesses to compete successfully. A cloud data platform enhances the applicability of essential business insights by integrating unstructured and structured data into a simplified ecosystem, which allows analytics to be performed cost-effectively and quickly. Below are some of the primary use cases of a cloud data platform.

Data Warehouse

A data warehouse is used to collect, store, process, and manage enterprise-wide data for better decision-making. It collects data from various sources including CRM and ERP applications. Data warehouses allow organizations to access their data in a single unified repository for better reporting and analytics purposes.  

Data Lake

A data lake is a collection of structured and unstructured data that can be utilized for machine learning and analytics. It is a centralized repository that helps businesses to explore and analyze their complete data in one place. Data lakes are usually present in the cloud making them accessible to anyone and anywhere.  

Easy Access to Big Data Analytics

Big data analytics refers to the process of extracting valuable insights from complex and large data sets. Businesses use big data analytics to identify customer behavior, analyze market risks, explore trends, and amplify decision-making. Cloud platforms allow businesses to quickly analyze large volumes of data for better and faster insights. 

Quick Application Development

Cloud data platforms allow programmers and developers to quickly develop better applications using various frameworks, tools, and languages. By leveraging the splendid scalability of cloud resources, developers can develop scalable applications without buying expensive tools and hardware. 

Easy Access to IoT Analytics  

Internet of Things or IoT analytics is the process of gathering and analyzing data created by IoT devices. It provides businesses with valuable insights into their identity trends operations and ensures better decision-making on how to utilize resources effectively. 

Cloud Data Platform Architecture

cloud data platform

A cloud data platform architecture facilitates the flow of large volumes of data and how that data is processed and distributed across various departments and stakeholders for analytics, reporting, and other uses. It needs to be able to support a significant number of data forms. 

Architecture is the sum total of the standards, models, and rules that define how data is managed within an organization. One example of a cloud data architecture is a customer data platform architecture. This would represent the collection of all rules and policies around how customer data is stored and managed as it enters the organization.

There are four primary layers of a cloud data platform architecture. Each layer delivers unique capabilities in the cloud data platform.

Data Ingest Layer

This layer connects the source system to the cloud data platform. The data ingest layer ingests and processes incoming data, validates it, prioritizes sources, and directs it to the best storage location so that the data is ready for immediate access. It is connected with various data sources which allow real-time data ingestion. 

Data Storage Layer

After passing from the data ingestion layer, the raw data lands in the data storage layer. The data collected from multiple sources is stored and saved in this layer for when it is needed. The data storage layer is highly cost-effective and flexible. It allows enterprises to store large volumes of data in one place and retain it for long time periods. 

Data Processing or Analytics Layer

The processing layer in the cloud data platform reads data from the storage. It then validates, transforms, and applies business logic to the data. It makes raw data structured, meaningful, and useful for a variety of use cases. 

Data Servicing Layer

This last layer of the cloud computing platform architecture delivers the output of the data processing layer. It often includes a cloud data warehouse where data is stored. However, it might also include data lakehouses and data lakes. 

There are many other architectures depending on the source and category of the data. Today, there are many data architectures that include a complex combination of local storage and cloud services. When an issue comes up, it can be very difficult to identify exactly where the issue is and how to address it. That’s why multidimensional data observability is so helpful. 

Now the question is what is data observability? A Data Observability solution combines a variety of data quality and insight capabilities to develop a semantic understanding of the underlying data within an enterprise.

Acceldata helps you locate and isolate the root cause of issues so that you can fix problems quickly. Several of Acceldata’s customers have achieved success in eliminating unplanned outages using our solution. This is the power of truly comprehensive visibility into data, and it is what you’ll get when you use Acceldata as your cloud data platform.

Cloud Data Platform Market

The cloud computing market share has continued to grow in recent years and looks to have a great future. This is largely due to the fact that technology continues to advance, making cloud storage more affordable and more secure. The scalability of cloud technologies is what has drawn many organizations to them.  

The value of the global cloud computing market size was USD 483.9 billion in 2022 and it is expected to grow at a CAGR of 14.1% from 2023 to 2030. All that shows that the cloud data platform market is very strong. In a market of this size, it makes sense to take a closer look at what is causing all the buzz. Cloud computing and cloud data platforms have been clearly shown to be effective at increasing a company’s performance capabilities.

If you look at the cloud computing market share by company, you’ll see some of the biggest cloud providers at the top of the list. These include some well-known companies that you are most likely already familiar with. If you look at cloud computing market size by country, you’ll see that the U.S. is the highest spender in cloud computing, with China, The UK, and Germany following in that order. 

The customer data platform market is also on the rise as more and more enterprises realize the importance of managing and storing customer data in a way that actually produces actionable insights. That’s why the customer data platform market size is growing so fast. The global customer data platform market is also experiencing strong growth. 

So many companies are turning to cloud data platforms because they can help make sure that your decisions are all data-driven. Insights gained from platforms like Acceldata can help you prevent losses due to outages and improve your software development and release processes.

Data Platform Design

It’s very important that your data platform design is well-thought-out and serves your company’s specific needs. Six main layers make up the usual data platform architecture. These layers are Data Ingestion, Data Storage and processing, Data Transformation and modeling, Business Intelligence and analytics, Data Observability, and Data Discovery. These are the most common layers of data platform you’ll see when looking at how to build a cloud data platform. 

Companies often spend years building their programs. When designing cloud data platforms, GitHub is a software that you can use to create a repository to share code and documentation information with all members of the development team. GitHub is frequently used for this purpose and is also a cloud-based platform.

Finding the Right Data Cloud

Not every data cloud is created equal. When comparing various platforms, it’s important to find the solution that suits your needs. For instance, Acceldata’s data cloud platform is designed to specifically support data engineering. The observability our solution provides can help data engineers build and maintain high-performance, bulletproof data pipelines

On the other hand, you may be looking for a financial data platform or a customer data platform. All of these systems are designed to organize and store data as well as draw insights from the data so that you can make more informed decisions at your enterprise. Compliance is an important issue in data. If your goals are to focus on financial data or healthcare data, there will be specific regulations that your platform will need to comply with.

Designing Cloud Data Platforms

As we have discussed, companies often have a specific purpose in mind when they are designing cloud data platforms. Ensuring that their designs are secure is very important to them. That’s why they hire professionals who have received a data platform certification. These certifications are given out by many of the major cloud service providers and help developers to better learn how cloud technologies work. 

Ensure Data Quality and Reliability with Acceldata’s Cloud Data Platform 

The modern cloud data platform supports businesses by making data storage more scalable, visible, and effective. Let’s look at one cloud data platform example, Acceldata. It is a complete enterprise cloud data solution that is made up of 3 separate components. The first is Pulse. Pulse can provide rapid testing of new technologies, reduce costs by offloading unnecessary software, and eliminate unplanned outages using our advanced detection system. 

The second element is the Torch. Torch is a solution designed by Acceldata to use AI to automate data quality management and data reliability for all enterprise data systems, regardless of scale. 

Finally, there’s Flow. Flow is our data observability tool and can help your enterprise eliminate data downtime, optimize data engineering productivity, and align data pipelines with business objectives. 

These are the three main components that make up Acceldata’s Data Observability Platform. Our solution helps you see data clearer by providing you access to it at any point and time in the data lifecycle. Experience the difference of unprecedented observability provided by Acceldata.


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