...
Back

TID-020/ Introduction to Big Data architecture

Build Smarter Infrastructure. Deliver Real Value. This 3-day intensive workshop teaches you how to design scalable data platforms and monetize them through APIs, services, and marketplaces. Learn to package your data into business-ready products — with billing, privacy, and licensing built in from day one.

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:

Hortonworks Sandbox

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 Storm – Hortonworks

Apache Pig – Hortonworks

Apache Hive & Hadoop – Hortonworks

Apache Flume – Hortonworks

How to become a Data Scientist for Free

MongoDB NoSQL DBMS overview

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

R Basic Syntax

5 Ways in Which Big Data Can Help Leverage Customer Data

sqrrl – Google Patents

Welcome. The R Journal

Hadoop as a Service: 18 Cloud Options

Hadoop Mock Test – TutorialsPoint

Droit de l’environnement et pratique notariale

DBMS

DBMS Data Models

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

Data Warehouse Design

Make the right choice between Hadoop clusters and a data warehouse

What is MySQL? – Definition from WhatIs.com

flat file from FOLDOC

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

Fast analytics without coding

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

The Key to Data Monetization

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 Packages

Examples | Apache Spark

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

What is graph analytics? Definition from WhatIs.com

Requirements
  • Finish registration 2 weeks after contact
  • Access to a computer with internet and a working microphone
  • Basic Computer Literacy
Target Audiences
  • IT enthusiasts
  • Data engineers looking to evolve from pipelines to products
  • ML Engineers
  • Data analysts, business professionals, researchers, and anyone interested in Bid data architecture.
Features
  • Teaching Methods :
  • Theory: 40% Practical Work: Serious games, role-playing, simulations
  • Program Coordinator: Alexis André des Forges Instructor: Alexis André des Forges Contact Information Alexis André des Forges Email: linguistic.com@gmail.com
  • Format In-person via video conferencing (Visio) Customization options available Minimum: 1 session per week

Not sure if this course is right for you?

Take our *free pre-course quiz* to assess your current knowledge level and get personalized recommendations.

➡️ Start the Quiz Now

€55.00 Per Hour

Course Features

3 lessons
0 quiz
21 hours
All levels
English / French
56 students
Yes
July 09, 2025

Related Course

Seraphinite AcceleratorOptimized by Seraphinite Accelerator
Turns on site high speed to be attractive for people and search engines.