Agenda

Automation platforms are becoming more metadata-driven, utilizing metadata to orchestrate and automate data management processes. Automation tools can intelligently automate tasks such as data integration, transformation, and governance by leveraging metadata to comprehend the structure, relationships, and lineage of data assets. VaultSpeed is a pioneer in this evolution. We provide the only automation tool that blends business understanding with metadata from all sources to suggest a comprehensive company-wide data model and automates the transformation and integration of data to populate that model. The underlying magic lies in our built-in integration templates, which generate ETL and DDL instructions for various sources and technologies, encompassing 2220 unique combinations. VaultSpeed also champions the ability to allow data engineers to code custom business logic automation, such as KPIs, and apply that across all data presentations in the information layer. We're thrilled to offer you a sneak peek of our latest advancements in Automated Data Transformation. Our team is currently focused on creating and testing features that will make it easier for users to perform customized data transformations, both upstream and downstream. We're diligently working on an intuitive, graphical drag-and-drop interface that will enhance accessibility to data automation for every data team. This will enable them to deliver consistency throughout the organization in alignment with their business objectives and data governance policies.

The leaders in tomorrow's economies will be data + ai companies. Today, the Data Lakehouse is an established best practice as it brought the performance and reliability of Data Warehouses to the elasticity and cost effectiveness of Data Lakes. But what about AI? Tomorrow, the Data Intelligence paradigm embeds AI in every layer and all capabilities of the modern data platform to transform businesses around the world. During this breakout session we'll take a look behind the scenes and what to expect in the future.

AI or ML processes often require data to be pulled from the data store, processed, and then the results/predictions stored back in the data store. This introduces a certain level of complexity and should be avoided. We show you how an AI/ML model can be executed directly at the location of your data without the need to move it beforehand - an advantage for keeping complexity and costs low while maintaining high quality.

Learn how WhereScape improves Microsoft Fabric. Learn about streamlined migration, low-code integration, and improved metadata management that increases data management efficiency. Experience the synergy between WhereScape and Microsoft Fabric for advanced data technology.

The sports retail industry increasingly relies on leveraging data as part of day-to-day operations. With their newly modernized emnosSPORTS platform, emnos has witnessed true transformation based on real-time collaboration between sports retailers and sports brands. To achieve this, they require intuitive, flexible data analytics and visualization for multiple user types and organizations - a familiar challenge for emnos given their 15 years experience in the sports retail and food industries. Yellowfin and emnos have partnered to accelerate time to value for emnos’ clients, the sports retailers as well as the sports brands. Yellowfin provides the embedded analytics platform which enables emnos to deliver sophisticated sales, customer & stock KPIs, and forecasts to the sports industry - ultimately maximizing sales, reducing planning inefficiencies, and ensuring the right product at the right time in the right place to win with the consumers.

Truly complete data vault automation means that data vault expertise is no longer required. With this vision in mind, Reinhard and Marco developed Yotilla. Yotilla's innovative approach fully automates Data Vault data modeling and enables the definition of business logic without knowledge of the Data Vault data model. In the Breaktalk, we will present the way Yotilla works and show how Yotilla automates all changes to the data warehouse so that you can keep pace with changing or new business requirements at the same time. Using a customer example, we will show that Yotilla both significantly reduces the costs of creating and developing the data warehouse and generates benefits much faster.

Organisations try to federate the access to meaningful data and thereby generate value in a more efficient way. Usually technical roles are involved in the process of transforming raw data into consumable data products. By providing alternative entry points for less technical people to participate in the transformation process and starting to self-serve their needs for meaningful data products, quicker time-to-value can be achieved.

In the realm of AI Assurance and ensuring trust through agile data management, the emergence of RAG (Retrieval augmented generation) represents a pivotal advancement. RAG's integration of retrieval and generation techniques fosters richer and more contextually relevant responses, enhancing transparency and accountability in AI systems. By leveraging pre-existing knowledge alongside generative capabilities, RAG promotes robust data governance and bias mitigation, crucial components in fostering trustworthiness. In this session you will learn about the crucial role that effective Data Management plays in conjunction with Gen AI and how the Denodo Platform can enhance Gen AI by providing greater context.

The users of a modern data & AI platform can be very different with their individual needs in terms of development productivity, performance, user-friendliness and, of course, the use of the latest ML & AI technologies. In this talk, we will take a closer look at three user types: DATA PRODUCT BUILDER - as a technologist, is interested in quickly converting technical requirements into intuitively usable data products. Access to all relevant data, high-performance pipelines, data quality and professional presentation/management of data products are important success factors here. CITIZEN DATA SCIENTIST - as a business user, prefers to analyze specific questions "at the speed of thought" and appreciates the ease of use of data products and ML/AI functions. ML AND APPLICATION ENGINEER - helps to put an AI experiment/MVP into production, e.g. in the form of an intelligent, scalable app. The focus here is on the ability to use suitable software/frameworks (on a container basis), apply MLOps practices and operate the entire solution securely. How well does the Snowflake perspective fit your company's data strategy? The Snowflake team on site is looking forward to numerous and lively discussions after this talk!

Artificial Intelligence (AI) is currently a central topic and is revolutionizing industries with a dynamic reminiscent of the rapid rise of the internet. Algorithms such as regressions, collaborative filtering, or factorization have been used for years to generate recommendations. But what possibilities do the latest generative technologies open up, and how can we further harness their potentials? In this presentation, you will be introduced to effective use cases showcasing the innovative capabilities of Bloomreach AI "Loomi" and gain insight into how these new technologies are transforming into inspiring opportunities.

Carsten Schweiger, DWH Engineer and Agile Coach, shows how lengthy and inflated DWH projects are becoming a relic of the past. A model-driven data vault approach and short feedback cycles bring business and IT together. Automation and standardization in the development process drastically reduce costs and effort. Welcome to a world in which time-to-market is no longer an obstacle, but a competitive advantage.