Serverless data transformation approach seems to be good choice for many if not for all use cases? Do you agree? Probably the answer is not so straight forward. Thus I would like to invite you to the discussion about that. We will share our real project experience, talking about chances and risks. We will analyse several perspectives including: available technology stacks, solution delivery time, overall cost, scale of solution, competences availability, vendor lock. See you soon!

DataLakeHouse is a concept everybody knows. However how, together with AI it can support busniess decisions. Should DataLakeHouse still feed AI so it can make it own correct decisions in finance world. Let's discuss together, about core concepts, is datalake house really needed, and if AI at some point of time, can make proper investment decisions.

Digital experimentation: the ultimate weapon for businesses seeking a competitive edge in the world of tech. By using real-time data and cutting-edge tech, it optimizes digital offerings, enhances user experiences, and drives growth. Unlock the power of digital experimentation to take your digital game to the next level.

What we can observe today is the rapid growth of new AI models that achieve astonishing results in generating text, audio, images, and even source code. It becomes clear that the generative AI revolution has begun. The question is: are companies ready for it? Join me for a discussion – everyone will have a chance to share their thoughts, discuss experiences, exchange ideas, ask and answer questions.
Discussion points include e.g.:
● Which AI models have the potential for being applied by companies?
● How can they be used to gain profit?
● What are the risks of using them?
● How can we mitigate these risks?

What are the key characteristics of a self-service data streaming platform? Is ANSI SQL good enough? If not, what's missing? What about data discoverability? How data should be shared inside of an organization and how it should be secured? What else does your organization care deeply about?

Data Mesh has been one of the most hyped buzzwords in the data space for the past 2-3 years. Everybody talks about it, but only a few have fully grasped the concepts behind it. Let's get together at this roundtable, to discuss our impressions and understandings of Data Mesh, what is at the core of its concepts, and what experiences we can share and learn from each other.

Description: There’s a lot of fuss around Data Quality, Data Observability and DataOps, but what they are all about?
How do they fit in a data platform or a data practice? What are the most relevant tools in the space?

Join this round table to discuss your experience and learn other people experience regarding Data Ops, Data Observability and Data Quality.

From on-prem Hadoop to GCS, Dataflow, and Bigquery, from TSV to protobuf, from EMR to Databricks, from EC2 to k8s, from Redshift to Delta and Presto. Sounds familiar? Let's talk about migrations in Data Platforms, how to do that, and why it's hard, costly, and usually takes longer than expected.
- Why does it take longer than initially estimated?
- Why do users often not want to / can't migrate now?
- Do you always need to run two data infrastructures (the old and the new one) in parallel? Will it cost twice as much as expected?
- Should we offload migration to the users or migrate on their behalf by task force teams?
- Why do we need to migrate again, haven't we finished the last migration like 3 months ago?

At a time when various industries are increasingly embracing AI, the excitement around the opportunity may overshadow the wide-ranging ethical & legal implications. For example, an ed-tech startup may fast-track its way to operationalizing a language tutoring chatbot by using pre-trained language models containing built-in bias, which may lead to reputational damage if not managed properly. Similarly, a healthcare company may choose to implement a highly accurate black box model without realizing that the customers (or regulators) may require a solution that is transparent and interpretable––even if it is less accurate. In this session, we’ll share our approaches to these and other considerations around developing responsible AI.