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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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Big data real role to help Real estate business

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How big data helps real estate is trending today. When people buy real estate and its dependencies you can get from analytics

Advantages of Big-data in Real estate

  1. Study the data from real estate consume
  2. Understand the buyers
  3. Loan dependencies and role of consumers
  4. Sale activities by agents
  5. Sales boost

Role of Big Data

Real estate agents need to check lot of data sources to identify sales pitch and formula to boost sales. The first point is agents should understand the requirements of consumers or buyers of real estate.

References

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