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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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Overview Of Cloud Standards

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Cloud computing slowly becoming reality. So it has to address many concerns such as security, interoperability, portability, and governance at the earliest opportunity. This can be accelerated by compliance to guidelines and standards defined in consensus by the cloud providers.  Without addressing these concerns, users would be wary to tread this path in spite of its powerful economic model for business computing. Interoperability/integration Interoperability enables products/software components to work with or integrate with each other seamlessly, in order to achieve the desired result. Thus, it provides flexibility and the choice to use multiple products to achieve our needs. This is enabled by either integrating through standard interfaces or by means of a broker that converts one product interface to another. Cloud computing depends on compliance standards. Security Security involves the protection of information assets through various policies, procedure...
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