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14 Top Data Pipeline Key Terms Explained

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 Here are some key terms commonly used in data pipelines 1. Data Sources Definition: Points where data originates (e.g., databases, APIs, files, IoT devices). Examples: Relational databases (PostgreSQL, MySQL), APIs, cloud storage (S3), streaming data (Kafka), and on-premise systems. 2. Data Ingestion Definition: The process of importing or collecting raw data from various sources into a system for processing or storage. Methods: Batch ingestion, real-time/streaming ingestion. 3. Data Transformation Definition: Modifying, cleaning, or enriching data to make it usable for analysis or storage. Examples: Data cleaning (removing duplicates, fixing missing values). Data enrichment (joining with other data sources). ETL (Extract, Transform, Load). ELT (Extract, Load, Transform). 4. Data Storage Definition: Locations where data is stored after ingestion and transformation. Types: Data Lakes: Store raw, unstructured, or semi-structured data (e.g., S3, Azure Data Lake). Data Warehous...
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How to achieve Virtualization in cloud computing real ideas

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In order to run applications on a Cloud, one needs a flexible middleware that eases the development and the deployment process.

Middleware Approach to Deploy Application on Cloud

  1. GridGain provides a middleware that aims to develop and run applications on both public and private Clouds without any changes in the application code. 
  2. It is also possible to write dedicated applications based on the map/reduce programming model. Although GridGain provides a mechanism to seamlessly deploy applications on a grid or a Cloud, it does not support the deployment of the infrastructure itself.
  3. It does, however, provide protocols to discover running GridGain nodes and organize them into topologies (Local Grid, Global Grid, etc.) to run applications on only a subset of all nodes.
    Elastic Grid infrastructure provides dynamic allocation, deployment, and management of Java applications through the Cloud. 
  4. It also offers a Cloud virtualization layer that abstracts specific Cloud computing provider technology to isolate applications from specific implementations

Virtualization in CLOUD Computing

With the rapid expansion of Information Technology (IT) infrastructures in recent years, managing computing resources in enterprise environments has become increasingly complex.

In this context, virtualization technologies have been widely adopted by the industry as a means to enable efficient resource allocation and management, in order to reduce operational costs while improving application performance and reliability.
  1. Generally speaking, virtualization aims at partitioning physical resources into logical resources that can be allocated to applications in a flexible manner.
  2. For instance, server virtualization is a technology that partitions the physical machine into multiple Virtual Machines (VMs), each capable of running applications just like a physical machine. By separating logical resources from the underlying physical resources, server virtualization enables flexible assignment of workloads to physical machines.
  3. This not only allows workload running on multiple virtual machines to be consolidated on a single physical machine but also enables a technique called VM migration, which is the process of dynamically moving a virtual machine from one physical machine to another.

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