Apache

Apache Doris, a high-performance, real-time analytical database, boasts an impressive underlying architecture and code design. For developers, mastering source code compilation and debugging is key to understanding Doris’s core. However, the build process involves multiple toolchains and dependency configurations, and during debugging, you may encounter various complex issues that can leave beginners feeling overwhelmed. This article walks you through the process from source code to runtime, providing a detailed analysis of Apache Doris’s compilation and debugging procedures. From environment setup…

In today’s interconnected world, integrating systems, applications, and data is a critical requirement for businesses. However, building reliable and scalable integration solutions can be challenging due to the complexity of handling different protocols, data formats, and error scenarios. Apache Camel, combined with Spring Boot, provides a powerful and flexible framework to address these challenges. In this article, we’ll explore how to use Apache Camel with Spring Boot to solve real-world integration problems, including data integration, messaging routing, file processing, and…

Apache Flink is a real-time data stream processing engine. Most of the stream processing applications are ‘stateful.’ This means the state is stored and used for further processing. In Apache Flink, the state is managed through a configured state backend. Flink supports two-state backends in production. One is the HashMapStateBackend, and the other one is the EmbeddedRocksDBStateBackend.  To prevent data loss and achieve fault tolerance, Flink can persist snapshots of the state to a durable storage. Flink can be configured…

Retrieval-augmented generation (RAG) is the most popular approach for obtaining real-time data or updated data from a data source based on text input by users. Thus empowering all our search applications with state-of-the-art neural search.  In RAG search systems, each user request is converted into a vector representation by embedding model, and this vector comparison is performed using various algorithms such as cosine similarity, longest common sub-sequence, etc., with existing vector representations stored in our vector-supporting database. The existing vectors…

We know that data management today is changing completely. For decades, businesses relied on data warehouses, which stored information in an appropriate manner. They are structured, governed, and quick to extract information from, although expensive and rigid in nature. In contrast, data lakes are more efficient and allow for the storage of enormous amounts of data regardless of structure. However, the emergence of the data lakehouse architecture combines the benefits of the data lakes and data warehouses. Lakehouse models allow…

Large organizations where the number of users accessing crucial data is pretty high have to face a lot of challenges in managing fine-grained access. A variety of AWS services like IAM, Lake Formation, and S3 ACL can help in fine-grained access control. But there are scenarios where a single entity containing the global data needs to be accessed by multiple user groups across the system with restrictive access. Also, organizations with a global presence might be working in different environments and…

LLMs need to connect to the real world. LangChain4j tools, combined with Apache Camel, make this easy. Camel provides robust integration, connecting your LLM to any service or API. This lets your AI interact with databases, queues, and more, creating truly powerful applications. We’ll explore this powerful combination and its potential. Setting Up the Development Environment Ollama: Provides a way to run large language models (LLMs) locally. You can run many models, such as LLama3, Mistral, CodeLlama, and many others…

Apache Paimon is made to function well with constantly flowing data, which is typical of contemporary systems like financial markets, e-commerce sites, and Internet of Things devices. It is a data storage system made to effectively manage massive volumes of data, particularly for systems that deal to analyze data continuously such as streaming data or with changes over time like database updates or deletions.  To put it briefly, Apache Paimon functions similarly to a sophisticated librarian for our data. Whether…

Waking up in the middle of the night due to a data bug again, have you ever dreamed of an ideal data world where queries return in seconds, data is never lost, and costs are so low that your boss is smiling? Sounds like a dream? No! This is becoming a reality. Remember that night you were crushed by data partitioning issues, with the product manager frantically pushing for progress while you struggled with scattered data? Cross-source queries were as…