Bakul Banthia, Tessell, Co-Founder Perren Walker, Tessell, VP, Distinguished Oracle & Business Value Cloud Architect
Leverage cloud-native NVMe-based high-performance compute shapes to get up to an unbeatable 2 million IOPS on a single database instance. Learn about the patented architecture that converts this ephemeral infrastructure into a durable, persistent cloud storage solution, ensuring zero data loss protection. Enjoy these features for your choice of database engine (Oracle, PostgreSQL, MySQL, SQL Server, etc.) on your choice of cloud (AWS, Azure, etc.). No lock-in.
With close to two decades of experience in the lifecycle management of business-critical applications and cloud services, and with multiple patents to his name - Bakul Banthia is a multi-cloud, hybrid-cloud expert and leader. Bakul is currently the Co-Founder and Head of GTM at Tessell... Read More →
We often hear stories from Kafka users about taking nine months to implement production-ready Kafka-based data pipelines. We see customers having 50 teams relying on a single Kafka cluster managed by one person. Data engineers cannot easily simulate a production environment without a complex initial setup. Or data scientists struggle with data integration, building an offline ML pipeline to experiment, reproduce models, and debug them locally. Let's explore how to skip the headache of creating computing clusters, managing partitions, shards, and workers' setup.
The talk demonstrates a different way of implementing a cost-efficient serverless streaming pipeline using pure Python. We will leverage technologies like KEDA (Kubernetes Event-Driven Autoscaling) with integrated NATS Jetstream for the distribution of real-time events and Fission serverless framework to run lambda-like functions on Kubernetes.
ππ‘π¨ π ππ¦Bobur is a developer advocate and speaker specializing in software and data engineering. With over 10- years of experience in IT, he blogs about open-source technologies and the community around them.ππ‘ππ π ππ¨Bobur works with companies... Read More →
Mohammed Amine Garmes, Amadeus, Principal Data Engineer Liam Mulhall, Amadeus, Innovation Leader
Platform Overview: The Amadeus Hexadata Platform (AHP) is an advanced suite of capabilities for constructing and managing multiple End-to-End (E2E) big data pipelines. - Processes approximately 2 billion transactions daily. - Fully migrated from on-premise to Azure Cloud. - Data size 10PB. Data Integration: - Integrates data from around 6,000 servers across various Amadeus divisions and external clients. - 90% of data handled via Kafka, hosted on Azure or within a private data center. - Utilizes Mirror Maker to replicate data across four Kafka clusters. - Utilizes KafkaConnectors to stream files to Kafka Technologies Used: KStreams, Databricks, Kafka Connectors, Airflow, Internal Framework Additional Capabilities: Operational on OpenShift. (4000 Pods) ElasticSearch MLOPS pipeline : CICDCD for ML models on the platform in Azure. - on-going - Key Benefits: Provides scalable, reliable, and efficient big data processing and analytics solutions.
As a Principal Data Engineer with over nine years of experience at Amadeus, I specialize in designing and optimizing the company's Big Data platform. I lead complex data engineering projects, leveraging my expertise to ensure scalable, efficient, and high-performance data processing... Read More →
A highly motivated and results-oriented technical leader with over 25 years of experience in delivering complex software solutions. My expertise lies in building scalable, secure, and real-time systems across various industries - all with a centralized view around 'achieving the best... Read More →
Learn why GPU clusters need massive high performance networks. InfiniBand vs Ethernet. And anatomy of large scale network fabrics behind cloud providers.
Discover the future of Industrial IoT with Machine Learning and Data processing on the edge in this dynamic and practical session. We will cover use cases such as digital twins, smart homes, and connected cars, while learning pro's and con's of for deploying AI models on edge vs on the cloud.
The presentation will include a live demo featuring a Raspberry Pi running an IoT setup with open source ML model deployed on the edge using Python, InfluxDB for time-series data storage and analytics. Our demo code and hardware setup will be open-sourced on GitHub.
You will learn:
- Pro's and Con's of data processing and ML inference on edge vs on the cloud - How to deploy and run full stack application with real time data processing and analytics - Connect with machine learning and data science tools to gain better insight and build the foundation of your industrial IoT solutions.
Suyash Joshi is a seasoned software engineer and developer advocate at InfluxData in London, UK. With a B.S. in Computer Science and an M.A. in Game Design, he brings a robust technical and creative background to his role.Suyash has a rich professional history, having previously worked... Read More →
