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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 Skills You need for Data Science Engineers

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Data science job is not straight forward coding job. But coding is part of it. You need both Technical and business skills to be successful.

Responsibilities

  • Dealing with internal customers
  • Getting data from multiple data sources
  • Dealing with Admins of lot other databases
  • Preparing reports with Tableau
More: R for Data science with real time examples

Qualifications:

  • Lot of coding skills needed
  • Positive attitude
  • Innovative way of problem solving
  • Degree in engineering
  • Lot of business knowledge

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