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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 Tips You Need to Create Mobile Apps

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Before you start creating mobile applications, you need to understand two things. There are two mobile application types. Namely Native and Web applications.
Mobile applications are two types.Those are Native and Web applications. 
Mobile applications
Photo Credit: Srini

Native applications

  • Each mobile operating system you need to create one version. Native applications are platform dependent. The disadvantage is you need to create multiple versions.
  • Develop apps only on that platform
  • These apps not portable

Web applications

  1. The web applications are platform independent. They work for all mobile operating systems. These applications are browsable using any popular browsers.
  2. These apps are browsable from any popular web browsers

4 Top Mobile Platforms

  1. iOS 
  2. Android 
  3. Windows Phone 7 
  4. Blackberry OS 

4 Top Tablet Platforms

  1. iOS 
  2. Android 
  3. Blackberry OS 
  4. Windows 8

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