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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 science these IT skills you need to learn to get job

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The most lucrative analytics skills include MapReduce, Apache Pig, Machine Learning, Apache Hive and Apache Hadoop.

Machine learning, big data, and data science skills are the most challenging to recruit for and potentially can create the greatest disruption to ongoing product development and go-to-market strategies if not filled.
In demand IT skills
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Great in demand it skills...
  1. Big Data (Information Technology): 3,977%
  2. Node.js (Design): 2,493%
  3. Tableau (Research and Analysis): 1,581%
  4. NoSQL (Information Technology): 1,002%
  5. Apache Hadoop (Information Technology): 704%
  6. HTML5 (Information Technology): 612%
  7. Python (Research and Analysis): 456%
  8. Oracle (Sales): 382%
  9. JSON (Information Technology): 318%
  10. Salesforce CRM (Sales): 292%

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