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Data Architecture Fundamentals

Course Summary

The Data Architecture Fundamentals training course covers concepts around creating an architecture that ensures speed, reliability, and security.

The course begins by focusing on data sources and collections and how to transform data. Next, the course covers areas of storage, extraction, transformation, and loading into databases to make it easier for analysts to gain deep data insights. The course concludes with a lesson on Design Patterns in Data Engineering Architecture.

Learn techniques and tools for data collection, usage, processing, storage, and integration with different systems.
Data Analysts, DBAs, Systems Administrators, and Developers that want to level up their ETL skills.
Data Engineer - Data Scientist - Software Developer - System Administrator
Skill Level
5 Days
Related Technologies


Productivity Objectives
  • Manage data well.
  • Design flexible data management and integration systems.
  • Create a real-time data environment.
  • Secure your architecture.
  • Employ data as a service.

What You'll Learn:

In the Data Architecture Fundamentals training course, you'll learn:
  • Data Sources and Collection
    • Data from API
    • Data from Websockets
    • Data from file system (text, avro, csv, etc)
    • Data from (S)FTP
    • Data from cloud (bucketing)
    • Data from mounted drive
    • Data from SQL Database
    • Data from NOSQL Database
  • Transforming Data
    • Review of data formats and efficiencies
      • JSON
      • Avro
      • XML
      • Parquet
    • Consuming and transformation in stream:
      • Merges
      • Joins
      • Concatenations
      • Grouping of disparate data
      • Partitions
  • Tools Review
    • Python Pandas review
    • KSQL
    • Cloud solutions for in-stream transformation
    • Quick overview of other solutions:
      • Talend
      • Informatica
      • Pentaho
  • Storing Data
    • Considerations around design patterns
      • Replication
      • Replayability
      • Block Storage
      • Drive mounts
      • Data Lakes
    • Networking considerations:
      • VPC
      • Private/Public Ips
  • Design Patterns in Data Engineering Architecture
    • Collecting data:
      • Polling
      • Streaming
      • Batching
      • Event Driven architecture
    • Data Warehousing patterns
      • Enterprise Data Warehouses
      • Enriched and unenriched data patterns
      • Dimension and Fact Tables
      • Setting your schemas correctly
“I appreciated the instructor's technique of writing live code examples rather than using fixed slide decks to present the material.”


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