We are doing our best to create the agenda as soon as possible. We want to make it appropriate to every Participant, so we will built 5 simultaneous sessions:
Architecture, Operations and Cloud
This track is dedicated to architects, administrators and experts with DevOps skills who are interested in technologies, techniques and best practices for planning, building, installing, managing, containerising and securing their Big Data infrastructure in enterprise environments – both on-premise and the cloud.
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 and ingestion, ETL, job scheduling, metadata and schema management, distributed processing engines, distributed datastores and more.
Streaming and Real-Time Analytics
This track covers technologies, strategies and valid use-cases for building 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.
Artificial Intelligence 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. It also covers topics related to prototyping and operationalizing ML/AI models in production, data visualisation and A/B tests.
Data Strategy and ROI
This track is for technical and business experts 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. Since most of the Big Data projects are difficult to complete on time and in budget, presentations will also explain necessary technical, cultural and leadership aspects that are key to successful Big Data initiatives at enterprises, avoiding wasting money and getting a positive return on investment (ROI).
The second part of the conference will contain over 20 round-table discussions on various topics like:
- Challenges of building a modern & future-proof data processing platform
- AI, Machine Learning and Big Data in the enterprise
- Data visualization – how to visualize large, complex and dirty data and what tools to use
- Choosing a right BI solution for a large data and a quick response time
- Analytics and Customer Experience Management on top of Big Data
- IoT in production – use-cases, data, tools and challenges
- Stream processing engines – features, performance, comparison
- Being an efficient Data Engineer. Tools, skills and ways of learning
- Data privacy, personal integrity and GDPR
- Data ingestion technologies, techniques and challenges
- Big Data – the cloud way
- From on-premise to cloud: an end to end cloud migration journey
- Big Data on Kubernetes