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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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Big data benefits in Education field- A data driven approach

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Netflix can predict what movie you should watch next and Amazon can tell what book you'll want to buy.

With Big Data learning analytics, new online education platforms can predict which learning modules students will respond better to and help get students back on track before they drop out.

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That's important given that the United States has the highest college dropout rate of any OECD (Organisation for Economic Co-operation and Development) country, with just 46% of college entrants completing their degree programs. In 2012, the United States ranked 17th in reading, 20th in science, and 27th in math in a study of 34 OECD countries.The country's rankings have declined relative to previous years.

Many students cite the high cost of education as the reason they drop out. At private for-profit schools, 78% of attendees fail to graduate after six years compared with a dropout rate of 45% for students in public colleges, according to a study by the Pew Research Center.

Among 18 to 34 year olds without a college degree, 48% of those surveyed said they simply couldn't afford to go to college. Yet 86% of college graduates say that college was a good investment for them personally.

The data tells us that staying in school matters. But it also tells us that finishing school is hard. Paul Bambrick-Santoyo, Managing Director of Uncommon Schools, Newark and author of Driven By Data: A Practical Guide to Improve Instruction, has shown that taking a data-driven approach does make a difference.

During the eight years in which Bambrick-Santoyo has been involved with the Uncommon Schools, which consist of seven charter schools focused on helping students prepare for and graduate from college, the schools have seen significant gains in student achievement, reaching 90% proficiency levels on state assessments in many categories and grade levels.

Using a data-driven approach can help us teach more effectively. At the same time, technology that leverages data can help students with day-to-day learning and staying in school.

Netflix and Amazon present us with offerings we're more likely to buy, delivering a more personalized and targeted experience.

Pandora figures out our music tastes and recommends new music to listen to. In the future, this kind of personalized experience won't just be used just for entertainment and shopping, but for education as well.

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