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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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Skills needed for NoSQL Data engineer

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The following Skills are needed to become successful NoSQL data engineer.

Skills for NoSQL engineers
NoSQL Skills
Skills You Need to Become NoSQL Developer
  • 3 + years of practical experience with distributed data analysis systems using parallel processes such as Hadoop.
  • Experience working with batch processing/ real-time systems using various open source technologies like Solr, Hadoop, NoSQL, Spark, Hive, etc.
  • Knowledgeable about data modeling, data access, and data storage techniques
  • Experience in Linux or similar system is preferred
  • Experience on MySQL, NoSQL and storage software is a plus
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