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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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Robotics These Skills You Need

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Robotics is a combination of multiple skills. Out of those many skills similar to B.Tech Electronics skill sets. I am sharing for your quick reference the complete skillset.


These skills are very much needed to become a Robotics Developer


PROGRAMMING

  • Mat lab - Familiarity with command-line and external functions using MATLAB library; import/export of data; graphing/plotting functions & data; rudimentary animation
  • Python, C / C++ familiarity
  • ROS- Robot Operating System (ROS) - Optional (Good to know)
  • Program Constructs- Sequencing, Selection, Iteration & Recursion
  • Data Organization- Arrays, Lists, Pointers

COMPUTERS

  • Tools Productivity: SW (MS Office - Excel / Word / PowerPoint / Project)
  • Operating Systems
  • Windows or Apple-OS - use of personal laptop computer Linux or Ubuntu

MATHEMATICS

  • Linear Algebra Inversion, Eigenvalues, Null-Space
  • Linear Differential Eq. Matrix-Algebra & -Manipulation
  • Basic Calculus Derivatives, Gradients, Chain Rule
  • Numerical Integration Basic Computational Implementation, e.g. Runge-Kutta 4
  • Fourier Analysis

Newtonian Physics

  • Newton-Euler Mechanics (Forces, torques, mass/inertia, Equations of motion) System State Degrees of Freedom & Constraints to fully describe a system’s behavior mathematically.

CONTROLS

  • Control Systems, Controls Fundamentals (transfer functions; bode plots; stability-margin; time-response of LTI systems; PID compensators).

Basic Electronics

  • Electronics- Basic experience with practical circuits (elements, interactions, PCBs) Mechanisms- Some design and fabrication experience (Concept -> CAD -> Fabrication) Documentation -Basic skills in document structuring and technical writing.

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