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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 features in the design of data modelling (1 of 2)

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[Data modelling jobs career] The analogy with architecture is particularly appropriate because architects are designers and data modeling is also a design activity. In design, we do not expect to find a single correct answer, although we will certainly be able to identify many that are patently incorrect. Two data modelers (or architects) given the same set of requirements may produce quite different solutions. Data modeling is not just a simple process of "documenting requirements" though it is sometimes portrayed as such. Several factors contribute to the possibility of there being more than one workable model for most practical situations. First, we have a choice of what symbols or codes we use to represent real-world facts in the database. A person's age could be represented by Birth Date, Age at Date of Policy Issue, or even by a code corresponding to a range ("H" could mean "born between 1961 and 1970"). Second, there is usually more ...
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