Скачать или смотреть "Apache Spark and Databricks" + "Enhancing Java Apps with LangChain4j"

JOIN US IN-PERSON FOR FREE PIZZA AND GOOD COMPANY! To register, visit https://www.eventbrite.com/e/apache-s...

Apache Spark and Databricks in Action: Driving Cost Transparency Platform Through Intelligent Data Processing
Abstract
This talk focuses on implementing Apache Spark and Databricks within the Cost Transparency (CT) Platform for intelligent data processing. The CT Platform processes diverse expense data, employing Spark for essential tasks like filtering, merging, enriching, and aggregating information.
A key feature is the Rule Service, a microservice enabling flexible business logic and dynamic rule updates without code changes. The DraftRun Service allows for testing new rules on data subsets. Apache Spark is an open-source distributed engine for various workloads, known for its speed through in-memory computing. Databricks enhances Spark as a cloud platform, offering streamlined cluster management, an optimized runtime, and collaborative tools.
The presentation demonstrates how Spark and Databricks drive efficient data processing in the CT Platform, including practical examples.

Vladimir Shipovalov
My introduction to computers began in the 90s when my parents bought a ZX Spectrum. I became familiar with Basic, then Pascal. At university, I studied C/C++. After graduating, I worked at a telecommunications company. I developed various systems using C++. Later, I got a little acquainted with C#.
Then there was a break in my development career. I joined an oil company where, in addition to administering computer systems, I created the country's most powerful computing cluster.
I decided to return to development because I enjoyed it more. I chose Java as a more promising language. I worked on several interesting projects in domains such as e-commerce and fintech.
I have been developing with Java for over 8 years.
Besides Java, I have also developed using Kotlin and Golang.

Enhancing Java Applications with LangChain4j: Practical AI Integrations
Abstract
This talk explores practical strategies and insights for integrating LangChain4j and Large Language Models (LLMs) within Java applications. Attendees will learn how Java developers can leverage LangChain4j for tasks such as:
Interpreting Complex Swarm Intelligence: Enhance transparency and interpretability of autonomous drone behaviors by translating complex swarm decisions into clear, actionable insights.
Automating High-Quality Code Documentation: Improve developer productivity by automating accurate, consistent code documentation and facilitating efficient onboarding.
Enterprise Integration of LLMs: Simplify and streamline the integration of advanced LLM capabilities in enterprise-grade Java systems.
Attendees will gain actionable knowledge to immediately implement AI-driven solutions using Java and LangChain4j.

Alok Tribrewala
Alok Tibrewala is an IEEE Senior Member and seasoned Software Engineer with over nine years of expertise in enterprise-scale software architecture, Java-based application development, and AI integration. He holds an MS in Computer Science from The George Washington University and is an active independent researcher with focus on the intersection of Java ecosystems and Large Language Models (LLMs), including frameworks like LangChain4j.
Alok regularly contributes to the tech community through speaking engagements at IEEE Conferences, Hackathons (e.g., IEEE CAI, NJIT), peer reviews, and mentoring, sharing innovative insights and practical solutions from his research in AI-enhanced application intelligence.