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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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Data mining Real life Examples

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Data mining is a process to understand about unused data and to get insights from the data. You need a quick tutorial and examples to perfect with this process. The best example is the Backup data business use case to mine the data for useful information. The backup data is simply wasted unless a restore is required. It should be leveraged for other, more important things. This method is called Data Mining Technique . --- For example, can you tell me how many instances of any single file is being stored across your organization? Probably not.  But if it’s being backed up to a single-instance repository, the repository stores a single copy of that file object, and the index in the repository has the links and metadata about where the file came from and how many redundant copies exist. By simply providing a search function into the repository, you would instantly be able to find out how many duplicate copies exist for every file you are backing up, and where they are coming from. ...
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