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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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Five real ideas you need to get Success from failures

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The list looks so small. But these are so powerful. I hate that expression! And everyone seems to use it. Got fired from your job? It is what it is. Lost your savings? It is what it is. Put on 50 pounds? It is what it is.

failure

Never use It is what it is

  1. Stop saying “it is what it is.” The expression is a mantra for losers. 
  2. Be honest with yourself. Halfway through the tryout, you thought it wasn’t going well, so you stopped pushing yourself, didn’t you? 
  3. “Tryouts didn’t go well because you didn’t try your hardest. You gave up mentally, so you gave up physically.” 
  4. When you fail, figure out why ?
  5. It’s my fault. It’s always my fault—thats way I am in control.
Adopted from www.success.com

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