#Data Engineering

Explore how leading industries apply data science and machine learning in practical production scenarios. Understand the implementation of ML projects through problem framing, techniques, results, and scientific backing. Access curated resources on vital subjects like data quality, engineering, and feature stores to gauge real-world ML project returns.

The data-engineer-handbook repository offers a comprehensive set of resources for those interested in data engineering. It features practical projects, interview guidance, and a curated selection of books on data engineering and machine learning. Access active communities and high-rated courses to support learning and career development. This platform provides insights into data architecture and modern techniques such as Apache Spark, fostering an inclusive educational journey. Discover relevant companies, in-depth blogs, and professional podcasts for the latest industry updates.

Explore our daily updated repository with a curated selection of articles, blog posts, and videos about Scala, Data Engineering, Java, Big Data, AI, and relevant technology fields. This shared collection is designed to offer succinct insights into the latest trends and innovations. With an easy-to-navigate search feature, readers can find diverse information spanning topics like Kafka ecosystems, unstructured data, data security, and more. Stay informed with regular contributions from tech experts, providing updates on evolving data strategies and technology insights.

Explore a curated selection of resources aimed at enhancing leadership capabilities within the engineering field. This guide covers key areas such as software engineering, system design, management, and business strategies, featuring a variety of books, newsletters, and industry experts to follow. By focusing on personalized skill assessment and targeted learning, professionals can effectively advance in leadership roles, software development, data engineering, and product management.

This detailed guide offers a comprehensive collection of state-of-the-art resources in the field of Large Language Models (LLM), covering key topics such as algorithms, training processes including fine-tuning and alignment, as well as inference and data engineering. It explores model compression techniques, evaluation metrics, and prompt engineering, supported by diagrams and practical examples. The guide provides insights into models like Transformer, GPT, and MoE, while looking into future multimodal model developments. Including links to in-depth guides and code repositories, it is a crucial resource for staying informed in the rapidly evolving domains of AI and machine learning.

Spring AI facilitates AI application development with a Spring-centric API, ensuring portability and modular design. It connects enterprise data and APIs with major AI models, and supports key providers like OpenAI and Google. Inspired by Python projects such as LangChain and LlamaIndex, Spring AI supports multiple languages, offers mapping of AI outputs to POJOs, vector database support, and Spring Boot auto-configuration for seamless generative AI integration.

Synmetrix is an open source data engineering platform enhanced by a semantic layer and Cube.js integration. It centralizes metrics management, supports data modeling and transformation, enables role-based access control, and facilitates BI and data democratization across organizations.