jnaapti

AI curriculum

Course outline for Modern Data Platforms

Data platforms combine storage and processing components with different responsibilities. Compare their roles and design a small batch-and-stream architecture for an analytical workload.

Technology
Data Engineering and Databases
Duration
4 hours
Modules
2 · 2 hours each
Level
Prerequisites listed

About Modern Data Platforms

Data platforms combine storage and processing components with different responsibilities. Compare their roles and design a small batch-and-stream architecture for an analytical workload.

Modern Data Platforms Course Objectives

  • Distinguish lake, lakehouse, and analytical database roles.
  • Explain Spark, Kafka, and Hadoop ecosystem responsibilities.
  • Design storage and processing boundaries for a workload.

Pre-requisites

  • Familiarity with SQL and data pipelines.
  • Basic command-line or programming experience.

Lab Setup

  • Computer with a local programming runtime and container support where available.
  • Use small datasets and local tools or supplied processing traces; no cloud cluster is assumed.

Detailed Course Outline

Proposed modules

Module 1: Platform processing models

  • Data lakes
  • Lakehouses
  • Spark
  • Kafka

Practical outcome: Map batch and streaming processing responsibilities.

Module 2: Storage and analytics

  • Hadoop ecosystem
  • Object storage
  • Analytical databases

Practical outcome: Select storage and query components for a sample workload.

Practical exercise

  • Design a small analytical platform and run or inspect a local batch-and-stream example.

How we train

Contact us for full course details, including duration, delivery options and lab requirements.

Ask about exercises, instructor feedback and the prior knowledge you need. Tool-specific courses marked provisional may become modules in a broader course.

Discuss your learning goals