Your IP: Unknown · Your Status: ProtectedUnprotectedUnknown

What is a cloud data platform? Benefits and use cases

Data management is important for any business that deals with large quantities of information on a daily basis. Cloud data platforms show plenty of promise for becoming the new standard for corporate data storage. Their capability to handle both structured and unstructured data gives companies a competitive edge in data processing. Learn more about how cloud data platforms are changing the game in data management.

What is a cloud data platform? Benefits and use cases

What is a cloud data platform?

A cloud data platform is an online hub that stores business data from various sources. At the basic level, it uses cloud computing to give organizations a secure place to store and process their data. More advanced uses for these platforms include creating data lakes and forming the basis of a company’s entire data system.

In the past, companies usually stored data on physical servers at their own locations for ease and security. But as the amount of data companies handle grew and as technology improved, cloud data platforms became a more appealing option.

Simply put, cloud data platforms are useful for organizations that want to process their data better. An innovative tech company can use a cloud data platform as a basis for developing applications. And for companies that just want to store and access their data, cloud data platforms are a secure and convenient way to do that.

Benefits of cloud data platforms

Aside from functioning as an off-site data warehouse and data lake, a cloud data platform can improve operations in various ways. Here are just some of the benefits a cloud data platform can provide:

  • pros
    Lower data storage cost: A cloud data platform saves expenses for on-site hardware maintenance or extensive supervision. The cloud provider handles most of the resources spent on data storage, freeing up your time and resources.
  • pros
    Enhanced data security: Cloud data platforms have several layers of cloud security, ensuring your data is safe from unwanted access. This saves time and effort that would otherwise go into maintaining this security.
  • pros
    Reliable access: Modern cloud data platforms are accessible from almost any device with the right credentials. The accessibility can give an organization a competitive edge in data management and processing.
  • pros
    Better collaboration: Storing all enterprise data in one place increases collaboration between employees. They can easily work together, share data, and access necessary resources.
  • pros
    Real-time data processing: A cloud data platform reflects data in real time. This improves efficiency because employees can upload, process, and distribute data as needed. Even if you store large volumes of data on the cloud, any changes will always reflect immediately.
  • pros
    Expandable storage: If at any point a need arises to increase or decrease data storage space, it’s easy to adjust on a cloud data platform. These adjustments can be made in real time, and they won’t affect existing data.
  • pros
    Automatic backup: Cloud data platforms automatically back up data to protect it from data breaches or system failure. This data safety even in case of a cyberattack or some other threat.
  • pros
    Data engineering and analytics: Through cloud data platforms, data scientists can merge data from various sources. This gives them a comprehensive view and a lot of flexibility for valuable insights.

Drawbacks of using a cloud data platform

While there are many benefits to using a cloud data platform, it has some drawbacks that you should consider, especially for long-term use.

  • cons
    Need for training and adjustment: Switching to a cloud data platform requires training and adaptation before you can fully use its features.
  • cons
    Increasing storage and complexity: Managing and adapting to the evolving nature of cloud data can be complex, especially regarding storage capacity.
  • cons
    Limited control: Using a cloud server means less control over data storage and transfer – you’ll need to coordinate with the service provider.
  • cons
    Compliance with data regulations: Staying compliant with evolving data privacy laws can be challenging. It’s important to ensure your provider meets these regulations.
  • cons
    Vendor lock-in: Cloud data platforms generally follow long-term contracts and partnerships. That can make it difficult to switch services if problems arise.
  • cons
    Less data privacy: Using a cloud data platform means giving up some aspects of data privacy, as the cloud administrators have access to your data.

Use cases of cloud data platforms

Simply moving data to a cloud data platform is not enough to add value to an organization. Cloud data platforms work best with specific goals in mind, allowing companies to make the best out of their capabilities.

Some of the use cases for cloud data platforms include:

Maintaining a secure data lake

A data lake is an online repository of any type of information, like structured and unstructured data. Cloud data platforms can serve as secure data lakes, where your employees can store any data without the need to process or categorize it.

In this case, a cloud data platform functions as a secure repository for any enterprise data. This makes data storage easier and sets the stage for future processing.

Creating a centralized data warehouse

A cloud data platform can store unstructured data and then turn into a data warehouse once this data has been processed for a specific purpose. As a warehouse, the platform provides structured data for different uses, helping with insights and collaboration.

A data warehouse can continue to store unstructured data, even if you already use some of the storage space for processed information. You can simply choose to scale up your data storage size as needed.

Processing and analyzing data with machine learning

Cloud data platforms are now using artificial intelligence to speed up their data analytics processes. This AI capability allows analyzing large volumes of data as soon as it’s uploaded and turning it into actionable information.

Machine learning can also be set to focus on specific types of data, which normally would be difficult to find. This helps you access and analyze the data you need automatically, reducing the effort needed for manual analysis.

Speeding up application development

Cloud data platforms have tools that can help with creating applications, usually by improving storage and team collaboration. Real-time data storage can also help your company’s development teams work faster.

The data lake capability of cloud data platforms also gives developers access to all the data they may need on demand. This further reduces the time it takes to develop applications and can help support their functions after launch.

Processing IoT data and analytics

Cloud data platforms automate data analysis and insights, making it easy to include data from IoT devices. Since these often produce a lot of data, a cloud data platform can safely store it until it’s ready for processing.

Access to a large database on demand is crucial for organizations looking to understand customer behavior. By using machine learning models to process this data, you can gain valuable insights for future campaigns.

Architecture of cloud data platforms

A cloud data platform has four layers that help transform raw data into structured data. Understanding these layers can help your company better integrate a cloud data platform into your work.

Data ingest layer

This is the first layer where data enters the cloud data platform. The data ingest layer collects, uploads, and integrates data from various sources, such as IoT devices, applications, and external data systems.

Data storage layer

The data storage layer stores all the data that an organization has gathered. Most cloud data platforms’ storage space is devoted to this layer, and it can be expanded or shrunk as needed. The data in this layer can be a mixture of structured or unstructured data, depending on your company’s needs.

Data processing or analytics layer

This layer is where the data is transformed and analyzed. The data processing layer involves processing raw data to derive meaningful insights using methods like batch processing, stream processing, and complex analytics. This layer often uses AI algorithms to analyze and interpret data, helping you make data-driven decisions.

Data serving layer

The data serving layer stores all processed data and displays the output of the previous layers. It presents the data in a usable form to end-users through reports, dashboards, or direct integration into processes.

Conclusion

With how much data businesses generate, it’s important to have a strong system for storing and managing this data. With their secure storage and extensive integrations, cloud data platforms can be the best solution.

Aside from convenience, cloud data platforms allow you to gain valuable insights from your data. Not only does this improve your business performance, but it also ensures consistency in the quality of your data long-term.


We value your privacy

This website uses cookies to provide you with a safer and more personalized experience. By accepting, you agree to the use of cookies for ads and analytics, in line with our Cookie Policy.