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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 IT Skills 2015

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IT spending for security technologies will increase 46% in 2015, with cloud computing increasing 42% and business analytics investments up 38%. Enterprise investments in storage will increase 36%, and for wireless and mobile, 35%. IT spending for security technologies will increase 46% in 2015, with cloud computing increasing 42% and business analytics investments up 38%. Cloud computing initiatives are the most important project for the majority of IT departments today (16%) and are expected to cause the most disruption in the future. IDG predicts the majority of cloud computing’s disruption will be focused on improving service and generating new revenue streams. These and other key take-aways are from recent IDG Enterprise research titled Computerworld Forecast Study 2015. The goal of the study was to determine IT priorities for 2015 in areas such as spending, staffing and technology.  Computerworld spoke with 194 respondents, 55% of which are from the executive IT role...
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