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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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Hyperion: How to Learn as Alternative for Mainframe

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Oracle Hyperion is a reporting tool. Its applications are Capital management, Asset planning, Workforce planning and more.

#Hyperion Career for Mainframe programmers:
Photo Credit: Srini

Books to Read on Hyperion

The Oracle Hyperion Financial Reporting 11 covers all basics to learn financial reporting using Hyperion tool.

The popular contents are

  • Explore Grids and the Point of View
  • Create Functions and Formulas
  • Master Conditional Formatting and Conditional Suppression
  • Create Dynamic Books and Batches
  • Import Reporting Content into MS Office with Oracle Smart View

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How to Fix datetime Import Error in Python Quickly

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Big Data: Top Cloud Computing Interview Questions (1 of 4)

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