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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...

New Directions for Digital Products (1 of 2)

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We already crossed Agriculture, Industrial, Information age. Now we are in digitization age. Many companies investing huge money in digitization. Mphasis - is betting on the digitization of Financial institutions Tech Mahindra - started research on Heath care digitization Infosys - focusing on Automation and artificial intelligence TCS - focussing on Machine learning WIPRO - is focusing on Big data and Hadoop What is digitization What we mean by digital. Digital data is distinguished from analog data in that the datum is represented in discrete, discontinuous values, rather than the continuous, wavelike values of analog. Thus, the digitization of data refers to the conversion of information into binary code, allowing for more efficient transmission and storage of data. A key differentiator of our current age from prior human history is that, as of the last decade, we not only convert data to a digital format, but we also create data in a digital format. Thus, we now h...