Cloud-native data platforms are modern solutions designed specifically for the management and processing of data in cloud environments. These platforms leverage the flexibility and scalability of cloud computing to enable efficient and reliable data storage, retrieval, and analysis.
Cloud-native data platforms play a crucial role in the era of digital transformation, as they provide the necessary infrastructure for companies to stay competitive. With the evolution of application development, traditional data management tools have become insufficient for handling the vast amounts of data generated by cloud-native applications. This is where cloud-native data platforms come in, offering shared solutions that can effectively handle the challenges posed by big data.
Moving Towards a Cloud-Native Data Platform
Now, let's delve into the intricacies of this innovative approach and explore its origins in the ever-changing landscape of technology.
Age of Legacy Applications and Storage
In the past, legacy applications relied on servers with disk volumes for data storage. However, as markets evolved and services changed, there was a noticeable shift towards clouds and infrastructure-as-a-service (IaaS). This shift involved mapping virtualized servers, also known as virtual machines (VMs), to disk partitions (vDisks) in a 1:1 relationship. Storage vendors then pooled disks from various nodes, implemented redundancy measures, and provisioned them as virtual logical unit numbers (LUNs).
Subsequently, the era of Hyper-Converged technology emerged, further enhancing this process by pooling disks from multiple nodes. While real security measures weren't necessarily required in this solution, isolation was relied upon to ensure that only the relevant server communicated with its designated LUN. This process is commonly referred to as zoning.
New Era of Modern Applications and Data
As the technological landscape continues to evolve, we find ourselves in the era of platform-as-a-service (PaaS), where the focus has shifted from managing virtual machines to managing applications. This shift also applies to data management, as elastic and distributed applications no longer rely on internal storage. Instead, they leverage a range of persistent and shared data services to store various data types, including objects, streams, logs, and records.
These advanced data services and NoSQL technologies revolutionize the storage landscape by integrating resilience, compression, and optimized data layouts directly into the data services. As a result, traditional features such as redundant arrays of independent disks (RAID) and deduplication become outdated and, in some cases, even counterproductive.
Benefits of the Cloud-Native Data Platform
Although not directly linked, the benefits of integrating microservice architecture with Big Data applications are enormous.
1. Scalability
One of the greatest benefits of using the microservice architecture of its applications is the balance it offers. Although not quite like cloud computing, it is common for both to be used together. The traditional monolith app does not have the flexibility of applications built into microservices. With each stand-alone service, the servers can be up and down with resources as needed. This is especially important for its systems, which are usually utility pigs, as they handle data with high volume and speed.
2. Data Consistency and Quality
Big Data increases the speed and volume of data processed simultaneously on a server. It also enhances the diversity and authenticity of uncertain data. As data volume grows, it is essential to monitor data quality. For example, the data error in the Nasdaq exchange has caused quite a stir recently, as the introduction of test data into live systems has significantly impacted several technology companies' stock prices. Two notable examples: Amazon prices dropped 87%, while Zynga rose 3,292%. In this case, the error may be directly related to the quality of the data.
Apps built into microservices are easier to maintain, test, and scale than monolithic applications.
3. Ease of Code Modification
Microservice frames allow different staff working with different coding languages to modify codes. This will benefit your organization, especially by combining diversity and strengthening your collection of talents.
Compute the streaming data that is used for real-time analytics. Click to explore about our, Big Data Platform
Major Components of a Cloud Native Platform
Cloud native is changing the usual way of software development. The software development process is so fast that the products are delivered quickly. It also provides easy-to-manage applications, as each application is treated as a microservice.
1. Microservices
Microservices are a way to develop a single application as a set of small services, each running through its process and communicating using lightweight protocols such as HTTP.
2. Containerization
Containers make it possible to classify applications into smaller, lightweight applications that share the operating system kernel. Generally measured in megabytes, containers use much fewer resources than virtual machines and start faster. Docker has become the standard of container technology. The most significant benefit they give is portability.
3. DevOps
DevOps is about culture, collaborative processes, and automation that aligns development and operational teams with having a shared focus on developing customer knowledge, responding quickly to business needs, and ensuring that innovation aligns with safety and performance requirements.
4. CI/CD
Continuous integration (CI) and continuous delivery (CD) is a set of operating systems that allow application development teams to deliver code changes consistently and reliably. CI technology aims to develop a consistent and automated way to build, package, and test applications. With the consistency of the integration process, teams are likely to make code changes over and over again, leading to better interaction with software quality.
In recent years, increasing cloud native data platforms have been deployed via cloud-based orchestration frameworks such as Kubernetes. This is because the containers offer effective shipping and fast duplication, as well as exaggerating the natural benefits of cloud computing in terms of service costs and expandable scaling. However, before Fluid happened, the Cloud Native Computing Foundation (CNCF) site did not have a native component to help these applications that need data efficiently, securely, and easily access data in traditional cloud computing.
Applications that are developed and deployed using cloud-based technologies are known as Cloud-Native Applications. Click to explore about our, Why Cloud Native Applications?
Manage Data Warehouse or Data Lake in Cloud Native
Managing a data warehouse or data lake in a cloud-native environment offers numerous advantages for organisations looking to optimize their data management strategies. Cloud-native data platforms provide the necessary infrastructure and tools to efficiently store, process, and analyze large volumes of data in real-time.
Cloud Native Benefits for Data Warehouse Management
A data warehouse is a centralized repository that stores structured data from various sources, enabling organizations to perform complex analytics and generate valuable insights. Traditionally, data warehouses were built on on-premises servers, requiring significant upfront investments in hardware and maintenance. However, with the rise of cloud computing and the advent of cloud-native data platforms, organizations can now leverage the scalability and flexibility of the cloud to manage their data warehouses more efficiently.
Cloud-native data platforms offer several benefits for managing data warehouses in a cloud-native environment.
1. Elastic Scalability
Allowing organizations to easily scale up or down their data storage and processing capacity based on their needs. This eliminates the need for upfront investments in hardware and ensures that organizations only pay for the resources they actually use.
2. Built-in Data Integration and Transformation Capabilities
They provide seamless connectivity with various data sources, enabling organizations to ingest data from multiple systems and transform it into a unified format for analysis. This eliminates the need for complex data integration processes and enables organizations to quickly access and analyze their data.
3. Advanced Data Governance and Security
They provide robust access controls, encryption, and auditing capabilities to ensure that data is protected from unauthorized access or breaches. This is especially important for organizations that deal with sensitive and regulated data, such as personally identifiable information (PII) or financial data.
4. Real-Time Analytics and Streaming Data Processing
They enable organizations to ingest and process data in real-time, allowing for immediate insights and faster decision-making. This is particularly beneficial for organizations that operate in fast-paced industries, such as e-commerce or financial services, where real-time data analysis is essential for staying competitive.
Cloud Native Benefits for Data Lake Management
Organizations can also leverage cloud-native data platforms to manage data lakes. A data lake is a centralized repository that stores both structured and unstructured data in its raw form. It provides a scalable and cost-effective solution for storing and processing large volumes of data, enabling organisations to perform advanced analytics and machine learning on diverse data sets.
1. Facilitate Tools and Infrastructure
Cloud-native data platforms provide the necessary tools and infrastructure for efficient data lake management. They support distributed file systems like HDFS or Amazon S3, enabling seamless storage and processing of large volumes of data without concerns about system failures or limitations.
2. Data Processing Framework Integration
Cloud-native data platforms also integrate with powerful data processing frameworks like Apache Spark or Apache Flink. These frameworks enable organizations to perform complex data transformations and conduct advanced analytics on their data lakes. With the ability to leverage these frameworks, organizations can extract valuable insights from their data, uncover patterns, and make data-driven decisions.
3. Distributed File System and Data Processing Framework
The combination of distributed file systems and integrated data processing frameworks creates a comprehensive solution for managing data lakes. Organizations can store and process both structured and unstructured data efficiently, ensuring that they can make the most of their data resources. Whether it is performing real-time analytics or running machine learning algorithms, cloud-native data platforms provide the necessary capabilities to unlock the full potential of data lakes.
4. Data Governance and Security
Cloud native platforms offer advanced features for data governance and security. With robust access controls, encryption, and auditing capabilities, organizations can ensure that their data is protected from unauthorized access or breaches. This is particularly crucial for organizations dealing with sensitive and regulated data, as compliance with data privacy regulations becomes increasingly important.
Data Stack Excellence with Cloud Native
Adopting cloud-native data platforms is key to maximising the potential of data management. These platforms offer scalability, data integration capabilities, advanced governance and security features, real-time analytics, and efficient data lake management. By leveraging these benefits, organisations can optimize their data management strategies, gain valuable insights, and drive business success in today's data-driven world.
- Cloud-native network functions
- CNCF Cloud Native Continuous Integration (CI) and Dashboard
- Cloud-native architecture
- cloud native applications
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