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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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IBM these are analytics solutions offering to different industries

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IBM analytics

Every industry has its own particular big data challenges. Banks need to analyze streaming transactions in real time to quickly identify potential fraud. Utility companies need to analyze energy usage data to gain control over demand. 

Retailers need to understand the social sentiment around their products and markets to develop more effective campaigns and promotions. Analytics solutions help organizations take control of big data and uncover the insights they need to make the best decisions.

IBM has Analytics Solutions in various lines:

  • Banks: Apply analytics to improve customer experiences and operational efficiency, and integrate risk into daily decision making.
  • Communication:Uncover insights about customers, network performance and market trends to make better business decisions.
  • Retail: Build lifetime customer relationships by meeting demands for innovative products while containing costs.
  • Education: Make more informed decisions to improve student performance and increase operational efficiency.
  • Energy Analytics: Transform your utility network and optimize customer operations with smarter energy systems.
  • Government: Gain insight into program performance, traffic patterns, public safety threats and more to better protect and serve citizens.
  • Healthcare: Anticipate, shape and optimize business and patient outcomes, and enable evidence-based, personalized medicine.
  • Industrial: Apply analytics in aerospace, defense, automotive, electronics, chemicals, petroleum, or industrial products companies.
  • Insurance: Deploy analytics at the point of impact to support better decisions about underwriting, claims and other areas of your business.
  • Life Sciences: Act on insights to drive growth, enhance relationships across the ecosystem and improve clinical development processes.
  • Media: Use analytics to provide a differentiated customer experience and drive operational transformation.
  • Transportation: Enhance services, manage capacity, and maximize the availability of assets and infrastructure.

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