Data

DataOps

dataops

Duration

1 Day

Languages

French - English

Trainer(s)

Thibault PERIER​ - Lead Data Engineer Astrakhan

The DataOps engineer has an emerging role and is the driving force behind Data industrialization. He is trained to use the platform, responsible for accelerating the deployment of projects, and is therefore the final link in securing the company’s ambitious Data and AI initiatives.

Inherited from DevOps practices, and therefore from agility, the DataOps function initially revolves around the delivery and ongoing deployment of Data projects, but the role is already diversifying and expanding to fill any gaps in the supervision and governance of a Data platform, as well as to improve overall coherence and collaboration within a Data team.

This training course aims to present the state of the art of the role and the technologies on which the DataOps engineer can rely.  As such, it will offer product demos as well as case studies.

Target audience

  • Technical Profiles with 1 to 5 years of experience in Data
  • Devops or DevSecops wanting to specialize in Data

Prerequisites

  • Training and if possible technical experience in Data Engineering or Data Architecture

Course Delivery

On site,
in your offices

Remote,
via Teams

Podcasts

Workshops

Training Program

Introduction: the composite DNA of DataOps

  • The DataOps agile manifesto
  • DevOps: ongoing integration and deployment

Roles and responsabilities of the DataOps engineer

  • The Data project lifecycle: accelerating and making it reliable at the same time
  • The role of the DataOps engineer in data quality
  • Responsibilities and deliverables

Key processes and deliverables

  • Pipeline automation
  • Hybrid cloud and meta-orchestration
  • Tests
  • Environment management
  • Version management
  • Monitoring

Data platform governance and collaborative processes

  • Domains and bricks of the Data platforms
  • Data Factory/Data Lab
  • The DataOps engineer, the missing link between Data Engineer and Data Scientist
  • Role of the DataOps engineer in Data Innovation

DataOps tools

  • Focus on the offers of Data Kitchen, Radical Bit, Saagie
  • Case studies
  • Demo

Extending the role of the DataOps engineer to Artificial Intelligence & MLOps

  • Reproducibility, model performance analysis, model exposure
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