Data

Data Architecture and Data platforms​

architecture data

Duration

2 Days

Languages

French

Trainer(s)

Alban RISSON​ - Lead Data Architect Astrakhan

As a constantly evolving field, Data architecture requires a high degree of specialization. In a changing context, keeping up to date with the latest developments and prototyping as early as possible, with the support of Cloud suppliers, is a must for the platform architect.

So, with Data Mesh, the acceleration of the digital economy no longer leads us to believe that centralized storage will be the norm, but how do we prepare for it and how do we build it today? 

This training covers the role of the Data Architect in all its dimensions (enterprise architecture, functional and logical architectures, platform, solution and technique) and shows how to build a scalable architecture. We also study architectures based on Data Mesh and Data Gateways to show what architecture rules a Data Office can set for the rest of the enterprise in the construction of any software product.

Target Audience

  • Data Architects
  • Data Platform Managers
  • Data Engineers

Prerequisites

  • To have already worked as an architect and/or have received technical training and/or have worked in Data Integration and/or Data Engineering

Course Delivery

On site,
in your offices

Remote,
via Teams

Podcasts

Workshops

Training Program

Introduction : why data architecture​

Information System and Data System​

  • Data Typology: structured, unstructured, anonymized, encrypted, open data, private data, metadata​
  • Data Architectures and Layered Models: Introduction to Enterprise Architecture and Data Mapping​
  • The positioning of data in the IS​
  • Principles of Security, Ethics and Data Protection​
  • Key aspects of Data Architecture.​

Architecture and Storage systems​

  • The key role of the Data Catalog,​
  • The data processing chain (collection, preparation, cleaning, ingestion…). How to get organized? How to make sequences?​
  • Storage, a field in its own right​
    • Storage models – relational, decision-making, graph, noSQL​
    • Centralized/distributed storage and Data Mesh architecture​
  • Integrity, versioning, auditability of data and impacts of the GDPR​
  • Transactions: ACID, compensation, distribution & CAP theorem

Data Architecture patterns​

  • Datalake, Business Intelligence, Master Data, Big Data, Streaming, Data Warehouse, IoT/Edge, Artificial Intelligence​
  • Data Factory/Data Lab architectures and articulation.​

Mapping of the key publishers and players in the data market​

  • Big Data Ecosystem and Data Engineering (AWS, Microsoft, Google)​
  • Focus on the offers of Databricks, Snowflake, Data Robot, Data Kitchen

Data Exchange Architecture Patterns​

  • Data Integration: modes and patterns (replication, bulk, ETL, ELT…)​
  • The principles of API Management and Data Virtualization​
  • The principles of streaming​
  • Case-studies​

Technical architecture and infrastructure​

  • Security​
  • Clustering​
  • High availability​
  • Backup and recovery​

Workshop – How to build a Data Platform  ​

  • The domains and bricks of the platform​
  • Define your building platform strategy: single-player approach, open architecture, best-of-breed​
  • Platform governance and monitoring: key roles and technologies​

Architecture Organization and Gouvernance​

  • Good practices​
  • Operational teams and systems, validation bodies​
  • Case-studies​
  • How data governance is used as an input to the architecture​
  • How to organize the technological watch​
  • How to prepare platform evolutions and what prototyping strategy to define? ​

Conclusion: how to align the Data Architecture to the Data maturity of the Information System​

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