Architecture, Operations, and Cloud

This track is dedicated to architects, administrators and experts with DevOps skills who are interested in technologies and best practices for designing, building, operating and securing their Big Data infrastructures and platforms in enterprise environments – both on-premise and the cloud.

Data Engineering

This track is the place for engineers to learn about tools, techniques, and battle-proven solutions to collect, store, and process large amounts of data. It covers topics like data collection, ingestion, ETL, job scheduling, metadata and schema management, distributed processing engines, distributed datastores, and more.

Streaming and Real-Time Analytics

This track covers technologies, techniques, and valid use-cases for building event streaming systems and implementing real-time applications that enable actionable insights and interactions not previously possible with classic batch systems. This includes solutions for data stream ingestion and applying various real-time algorithms and machine learning models to process events coming from IoT sensors, devices, front-end applications, and users.

MLOps

This new track focuses on the full life-cycle of ML models, from experimentation and feature engineering through model training to its productization. It describes real-world use-cases, technologies to build own AI/ML platforms and feature stores, as well as other technical challenges needed to solve to avoid hidden technical debt in ML projects.

AI, ML and Data Science

This track includes real-world case studies demonstrating how data & technology are used together to address a wide range of complex problems in the domain of machine learning, artificial intelligence, and data science.

Data Analytics, BI & Visualisation

This track focuses on day-to-day analytics including SQL & Python-based solutions for data analytics, productive BI solutions as well as convenient tools for data visualization.

Data Strategy and ROI

This track is for data and business professionals who are interested in learning how data and analytics can be used to generate growth, value added, and positive financial impact. It will contain presentations about real-world use cases that cover useful data-focused solutions, new business models, and various data monetization strategies. Presentations will also explain necessary technical, cultural, and leadership aspects that are key to successful Big Data initiatives at enterprises to avoid wasting money and getting a positive return on investment (ROI).

Academia, the incubating projects, and POCs

This track contains presentations about innovative use-cases and solutions that are still in the research or incubating phases, and when eventually completed, they can inspire community or positively impact big data & cloud landscape.