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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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Top sub-modules in Cloud Computing Technology Architecture

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Top sub-modules in Cloud Computing Technology Architecture
#Top sub-modules in Cloud Computing Technology Architecture:
The main architectural characteristics of a cloud computing environment. One fundamental architectural aspect of a cloud is heterogeneity. A cloud must support the aggregation of heterogeneous hardware and software resources, as it happens with scientific experiments. The concept of virtualization is also a key aspect for clouds.

Through virtualization, many users may benefit from the same infrastructure using independent instances. Virtualization enables the first security level in the clouds, since it allows the isolation of environments. In clouds, each user has unique access to its individual virtualized environment.

Cloud Architecture
  1. Virtualization
  2. Heterogeneity
  3. Security
  4. Resource sharing
  5. Scalability
  6. Monitoring
Resource sharing is provided by clouds, since each resource is represented as a single artifact, giving the impression of a single dedicated resource. Scalability is mainly defined by increasing the number of working nodes. By definition, clouds offer the automatic resizing of virtualized hardware resources. Monitoring refers to the ability of watching the current status of virtual machines or services provided.

Each one of those architectural characteristics is standardized by specific standards (which are in another class of the taxonomy). Besides that, some architectural characteristics are important to scientific experiments, such as scalability and monitoring to control the execution.

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