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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...

How Micro-services differ from SOA

Here you will know the differences between microservices and SOA. Both are different architectures.

Micro-services these are Differ to SOA


1. Micro-services 

  • Microservices are interconnected using simple API
  • You can develop highly scalable and modular applications
  • Service-based architecture
  • It is distributed architecture
  • Here, security is a big challenge. Since there is no middleware
  • Functional services, basically this kind
  • No coordination between services.


Service Oriented Architecture

2. SOA

  • Service-based architecture
  • It is distributed architecture
  • Security is good
  • It is an infrastructure kind of service

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