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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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What is IBM InfoSphere DataStage

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It integrates data across multiple systems using a high-performance parallel framework, and it supports extended metadata management and enterprise connectivity.

IBM InfoSphere

Powerful, scalable ETL platform—supports the collection, integration, and transformation of large volumes of data, with data structures ranging from simple to complex.
  • Support for big data and Hadoop—enables you to directly access big data on a distributed file system, and helps clients more efficiently leverage new data sources by providing JSON support and a new JDBC connector. 
  • Near real-time data integration—as well as connectivity between data sources and applications. 
  • Workload and business rules management—helps you optimize hardware utilization and prioritize mission-critical tasks. 
  • Ease of use—helps improve speed, flexibility, and effectiveness to build, deploy, update and manage your data integration infrastructure. 
  • Rich support for DB2Z and DB2 for z/OS—including data load optimization for DB2Z and balanced optimization for DB2 on z/OS 
  • Ref: IBM

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