AI, data & software instructor
Meriam Mbindyo
Instructor for AI, data, DevOps, Agile and software modules, with experience across Paris-based IT and business schools.
Artificial intelligenceMachine learningData mining
Module content
Course description
3-Day Intensive Course for Technical Professionals
3 Intense Days
7 Hours per Day (Split into two 3.5-hour sessions)
Learning Path Visual
Your hands-on journey from infrastructure to monetization:
Day 1: Architecting for Value — Data Value Chains & Infrastructure Setup
Map your data’s monetization potential and build the architecture to support it — from pipelines and APIs to governance frameworks.
Day 2: Engineering the Flow — Pipelines, Products & Insights
Design and implement pipelines that turn raw data into reusable assets. Create and expose data products using real tools and cloud infrastructure.
Day 3: Monetization Engines — APIs, Marketplaces & Security at Scale
Build and launch monetizable APIs, integrate billing and licensing controls, and distribute products across marketplaces with privacy and compliance by design.
Course Overview
Data is the new oil — but only if you can refine and monetize it. This workshop equips engineers, developers, and cloud architects with the technical and strategic skills to design scalable data architectures and turn them into revenue-generating platforms.
From backend infrastructure to API deployment, this course walks you through the entire lifecycle of data monetization using open-source and cloud-native tools.
You’ll learn how to:
-
Architect for value: design pipelines, APIs, and scalable infrastructure
-
Build and expose data products and services
-
Integrate monetization mechanisms: billing, licensing, usage tracking
-
Navigate privacy, compliance, and governance concerns
-
Deploy production-ready data services with modern DevOps patterns
This course bridges data engineering, API productization, and business model integration — giving you both the code and the context to drive value from data.
What’s Inside Each Day
Day 1 — Architecting for Value: Data Value Chains & Infrastructure Setup
-
Identify monetization-ready data across the organization
-
Map direct and indirect monetization strategies (internal, external, hybrid)
-
Set up infrastructure: Docker, Airflow, Spark clusters, cloud functions
-
Understand storage layers: Data lakes vs. warehouses (Delta Lake, BigQuery)
-
Implement data governance frameworks (GDPR, DMBOK)
-
Manage catalogs and metadata (Apache Atlas, OpenMetadata)
Tools: Apache Spark, Docker, Airflow, Delta Lake, BigQuery
Focus: Architecture • Infrastructure • Value Mapping
Day 2 — Engineering the Flow: Pipelines, Products & Insights
-
Design batch and real-time data pipelines (Kafka ➝ Spark ➝ BigQuery ➝ API)
-
Transform raw data into monetizable assets: enriched datasets, insights, ML features
-
Visualize and share: Kibana dashboards, Power BI tiles
-
Publish data products: FastAPI/Flask endpoints, API documentation
-
Package outputs for portability (Parquet, Arrow, JSON API)
Tools: Kafka, Spark, dbt, Power BI, Kibana, FastAPI
Focus: Pipelines • Productization • Delivery
Day 3 — Monetization Engines: APIs, Marketplaces & Security at Scale
-
Build and launch monetization-ready APIs (FastAPI + Swagger + billing)
-
Enable usage-based pricing, quotas, and access control (Stripe, OAuth2, JWT)
-
Integrate data marketplaces (Snowflake Marketplace, Dawex, Azure Data Share)
-
Enforce privacy, licensing, and IP policies (OpenPolicyAgent, GDPR tags)
-
Deploy full-stack services with observability, rate-limiting, and metering
Tools: FastAPI, Swagger, Stripe APIs, OAuth2, OpenPolicyAgent, Snowflake
Focus: Monetization • API Security • Licensing
Course Goals
By the end of this course, you’ll be able to:
-
Architect systems for scalable data monetization
-
Create and expose data products and APIs for internal or external use
-
Build pipelines that align with business value and reuse
-
Integrate billing, metering, and licensing into data services
-
Deploy compliant, secure, monetizable data workflows at scale
-
Understand and apply data governance frameworks across platforms
Who Should Take This Course?
-
Data engineers expanding into product and revenue-focused architecture
-
Backend developers building API-first services from data pipelines
-
Cloud architects implementing scalable, secure data platforms
-
DevOps professionals automating deployment of monetizable data workflows
-
ML engineers preparing data for external or multi-tenant delivery
-
CTOs and tech leads designing data business models
Class Reference: TID-020
Form Updated on: 06/16/2025 (Version 1)
Last Modified on: 06/16/2025
Program Note
This course is actively updated with new APIs, governance standards, and monetization frameworks to reflect the fast-moving data economy.
Links to resources for presentations or summaries:
Hadoop BI effort gets more out of big data at Yellow Pages
Managing Hadoop projects: What you need to know to succeed
What is Cassandra (Apache Cassandra)? – Definition from WhatIs.com
Apache Hive & Hadoop – Hortonworks
How to become a Data Scientist for Free
What is JDBC driver? – Definition from WhatIs.com
What is Open Database Connectivity (ODBC)? – Definition from WhatIs.com
Will the R language benefit from Microsoft acquisition?
Apache Flink: New Hadoop contender squares off against Spark | InfoWorld
What is the Confluent Platform? — Confluent Platform 2.0.0 documentation
5 Ways in Which Big Data Can Help Leverage Customer Data
Hadoop as a Service: 18 Cloud Options
Hadoop Mock Test – TutorialsPoint
Droit de l’environnement et pratique notariale
DBMS
Which in-memory DBMS best fits your company’s needs?
Which relational DBMS is best for your company?
Redis open source DBMS overview
MySQL open source RDBMS overview
Evaluating the different types of DBMS products
Make the right choice between Hadoop clusters and a data warehouse
What is MySQL? – Definition from WhatIs.com
What is NoSQL (Not Only SQL database)? – Definition from WhatIs.com
Unstructured Data: InfoGraphics – Big Data News
What are primary, super, foreign and candidate keys in a DBMS?
A Practical Guide to Data Warehousing in Oracle, Part 2 — DatabaseJournal.com
Analytics
DBMS 2 : Database management and analytic technologies in a changing world
Guide to big data analytics tools, trends and best practices
What is recommendation engine? – Definition from WhatIs.com
Analytics, Data Mining, and Data Science
What is sensor analytics? – Definition from WhatIs.com
A Cheat Sheet on Probability – Data Science Central
Why I Will Never Have a Girlfriend | Tristan Miller
Big Analytics Roundup (February 8, 2016) | The Big Analytics Blog
Directed acyclic graph – Wikipedia, the free encyclopedia
Application programming interface – Wikipedia, the free encyclopedia
Real-time operating system – Wikipedia, the free encyclopedia
Organic data growth and gaining access to the data
How data virtualization tools work
Adding a data virtualization layer to IT systems: Three questions to ask
Spark
Apache Spark Key Terms, Explained
Apache Spark Key Terms, Explained
Spark user survey suggests growth beyond Hadoop
What is Apache Spark? – Definition from WhatIs.com
Why Apache Spark is the Heir to MapReduce in the Hadoop Ecosystem
Brief pédagogique en français
TID-020/ Introduction à Big Data architecture est présenté ici en version synthétique française afin que les équipes pédagogiques puissent évaluer rapidement l'intérêt du module.
Le module s'inscrit dans la famille Analytics et intelligence artificielle. Il peut être adapté au calendrier de l'école, au niveau Tous niveaux, au volume horaire 21 h et aux modalités d'évaluation prévues.
Objectif d'intervention
Ce module vise à relier les outils data, IA et automatisation à des usages professionnels concrets.
Livrables et activités possibles
- cas d'usage, prompts, scénarios d'automatisation ou analyses data
- évaluation critique des résultats, limites et risques
- communication claire des choix techniques et business
Adaptation école
LC peut ajuster le déroulé, la langue d'enseignement, les supports, les exercices et les critères d'évaluation selon la promotion, le diplôme, le niveau d'autonomie attendu et les contraintes de planning.
Pour une version détaillée du syllabus en français, LC confirme le programme final après cadrage du niveau, des heures, du calendrier et des livrables attendus.
