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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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Scrum Vs Agile Methodology best explained with more details

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Life cycle of scrum with more details
#Life cycle of scrum with more details:
Scrum is part of the Agile movement. Agile is a response to the failure of the dominant software development project management paradigms (including waterfall) and borrows many principles from lean manufacturing. In 2001, 17 pioneers of similar methods met at the Snowbird Ski Resort in Utah and wrote the Agile Manifesto, a declaration of four values and twelve principles. 

These values and principles stand in stark contrast to the traditional Project Manager’s Body Of Knowledge (PMBOK). The Agile Manifesto placed a new emphasis on communication and collaboration, functioning software, team self organization, and the flexibility to adapt to emerging business realities.


How Does Scrum Fit With Agile?
The Agile Manifesto doesn’t provide concrete steps. Organizations usually seek more specific methods within the Agile movement. These include Crystal Clear, Extreme Programming, Feature Driven Development, Dynamic Systems Development Method (DSDM), Scrum, and others. While I like all the Agile approaches, for my own team Scrum was the one that enabled our initial breakthroughs. Scrum’s simple definitions gave our team the autonomy we needed to do our best work while helping our boss (who became our Product Owner) get the business results he wanted. Scrum opened our door to other useful Agile practices such as test-driven development (TDD). Since then we’ve helped businesses around the world use Scrum to become more agile. A truly agile enterprise would not have a “business side” and a “technical side.” It would have teams working directly on delivering business value. We get the best results when we involve the whole business in this, so those are the types of engagements I’m personally the most interested in.

What’s The Philosophy Behind Scrum?
Scrum’s early advocates were inspired by empirical inspect and adapt feedback loops to cope with complexity and risk. Scrum emphasizes decision making from real-world results rather than speculation. Time is divided into short work cadences, known as sprints, typically one week or two weeks long. The product is kept in a potentially shippable (properly integrated and tested) state at all times. At the end of each sprint, stakeholders and team members meet to see a demonstrated potentially shippable product increment and plan its next steps.


Scrum is a simple set of roles, responsibilities, and meetings that never change. By removing unnecessary unpredictability, we’re better able to cope with the necessary unpredictability of continuous discovery and learning.
(Ref: Scrummethodology)

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