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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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Career Opportunities to Write Algorithms

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Many participants in the Analytics seminar expressed opportunity in preparing algorithms for predictive analytics.
opportunities

You Need Algorithms Why

Using these algorithms, businesses can make better data-driven decisions by extracting actionable patterns and detailed statistics from large, often cumbersome data sets.

Many business people small to big expecting some kind of algorithms. So that they can save their precious time in predictive analytics.

As per IBM What are Good Benefits of Right Algorithm

  • Transform data into predictive insights to guide front-line decisions and interactions. 
  • Predict what customers want and will do next to increase profitability and retention. 
  • Maximize the productivity of your people, processes and assets. 
  • Detect and prevent threats and fraud before they affect your organization. 
  • Measure the social media impact of your products, services and marketing campaigns. 
  • Perform statistical analysis including regression analysis, cluster analysis and correlation analysis.

Summary

Algorithm making is a step by step process. The key advantages are useful to end users and taking less time in processing of application.

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