HANDS-ON FLUX TRAINING

8-9 June 2020

Hands-On Flux Training

Duration: Two half-day, hands-on virtual course.
Date: 8-9 June 2020, 8:30 am – 12:00 noon BST
Available spots: Spots are limited and only the first 50 will be able to register.
Registration: The fee for Flux Training is £250 registration (of course, you are also welcome to attend the now-free virtual InfluxDays conference featuring the keynote sessions).

Training overview

The course provides an introduction to the InfluxDB 2.0 architecture, InfluxDB Cloud offering and services. It presents fundamental knowledge about time series analysis and stream processing. Central to the course is the use of Flux lang by InfluxData. The course will cover subjects from Flux core concepts to advanced topics like User Defined Functions, passing by basic queries to manipulate time series data.

After taking this class, attendants will be able to:

  • Articulate and implement simple use cases for InfluxDB
  • Understand the basics of time series analysis and Stream Processing
  • Understand the basics of Flux’s internals
  • Use a subset of InfluxDB functionalities to manipulate time series data
  • Master just enough Go as required to use Flux lang
  • Build data pipelines and query time series using Flux lang
  • Deploy Flux Tasks as Streaming jobs with InfluxDB
  • Visualize the query results using InfluxDB Cloud UI

Audience

Data engineers who want a quick introduction into how to use InfluxDB and Flux lang to enhance their ability to manipulate time series data and provide real-time analytics.

Prerequisites and notes

  • Basic programming experience in an object-oriented or functional language
  • Knowledge of SQL (would be helpful)
  • Knowledge about data engineering tasks
  • All participants will need:
    • An internet connection
    • A free account in InfluxDB Cloud

Day One

Day Two

8:30 am – 9:00 amSetup8:30 am – 9:00 amIndividual presentation and group discussion
9:00 am – 9:30Introductions10 minutesBreak for Q&A
9:30 am – 10:00 am
  • Motivation
  • Time series
  • InfluxDB 2.0
9:10 am – 9:55 am
  • Set up Telegraf agent for system’s metrics
  • Data Analysis (cont.)
  • Hands-On section
10 minutesBreak for Q&A10 minutesBreak for Q&A
10:10 am – 10:50 am
  • Data ingestion
  • Conceptual View (data models)
  • Logical View (implementations)
  • Physical View (syntaxes)
  • Use Case
10:05 am – 10:50 am
  • Join
  • Hands-On section
10 minutesBreak for Q&A10 minutesBreak for Q&A
11:00 am – 11:50 amData analysis11:00 am – 11:20 am
  • Simple alerts
  • Tasks
11:50 am – 12:00 noonHomework presentation11:20 am – 11:50 am
  • Anomaly detection
  • Time series forecasting
  • Time series enrichment
  • Using Flux in Python Notebooks
  • Kafka-avro as a data source
  11:50 amQ&A

Day One

8:30 am – 9:00 amSetup
9:00 am – 9:30Introductions
9:30 am – 10:00 am
  • Motivation
  • Time Series
  • InfluxDB 2.0
10 minutesBreak for Q&A
10:10 am – 10:50 am
  • Data ingestion
  • Conceptual View (Data Models)
  • Logical View (Implementations)
  • Physical View (Syntaxes)
  • User Case
10 minutesBreak for Q&A
11:00 am – 11:50 amData Analysis
11:50 am – 12:00noonHomework presentation

Day Two

8:30 am – 9:00 amIndividual presentation and Group discussion
10 minutesBreak for Q&A
9:10 am – 9:55 am
  • Set up Telegraf agent for system’s metrics
  • Data Analysis (cont.)
  • Hands-On section
10 minutesBreak for Q&A
10:05 am – 10:50 am
  • Join
  • Hands-On section
10 minutesBreak for Q&A
11:00 am – 11:20 am
  • Simple Alerts
  • Tasks
11:20 am – 11:50 am
  • Anomaly detection
  • Time Series Forecasting
  • Time Series Enrichment
  • Using Flux in Python Notebooks
  • Kafka-avro as a data source
11:50 amQ&A

Your Trainers

Emanuele Della Valle, PhD

Assistant Professor | Politecnico di Milano

Expert in semantic technologies and stream computing. Brander of stream reasoning: an approach to master the velocity and variety dimension of Big Data blending stream processing and AI +20 years experience in innovation and research projects

Marco Balduini, PhD

Founder & CEO | Quantia Consulting

Expert in data processing, data integration and data science technologies. Main contributor of the C-SPARQL Engine, author of Streaming Linked Data framework and FraPPE ontology ~10 years experience in innovation and research projects.

Riccardo Tommasini

Assistant Professor | The University of Tartu

Riccardo Tommasini is an Assistant Professor at the University of Tartu, Estonia. Riccardo did his PhD at the Department of Electronics and Information of the Politecnico di Milano with a thesis on “Velocity on the Web”. The thesis investigates the velocity aspects that concern the Web environment, together with other challenges such as variety and volume. His research interests span Stream Processing, Knowledge Graphs, Logics and Programming Languages. Riccardo’s tutorial activities comprise Stream Reasoning Tutorials at ISWC 2017, ICWE 2018, ESWC 2019, and TheWebConf 2019, and DEBS 2019.