Apache Druid is an open-source data store designed for handling large amounts of real-time and historical data in a scalable, distributed system. It allows for quick and interactive analytical queries, with the ability to filter, group, and aggregate data in real-time. Druid is commonly used in data applications such as business intelligence, analytics and metrics collection, and provides a low-latency, high-throughput query performance that can be scaled horizontally across multiple nodes.

Azkaban is an open-source workflow management system used to schedule, run, and monitor Hadoop jobs. It provides a web-based interface for defining jobs, dependencies, and scheduling. Azkaban is designed to simplify the process of developing and deploying Hadoop workflows and to improve productivity by automating many of the repetitive tasks involved in managing Big Data projects. It is ideal for managing large-scale data processing tasks such as batch processing, ETL, machine learning, and data analytics.

Distributed Data Store refers to a system that manages data across multiple locations, ensuring data availability and reliability. This skill involves understanding the architecture and mechanisms that allow data to be stored, retrieved, and synchronized across distributed environments. Knowledge of Distributed Data Store is used to enhance scalability and fault tolerance in applications, enabling efficient data management in cloud computing, big data analytics, and real-time processing scenarios.

Luigi (Python Package) refers to a Python package used to define, organize, and run workflows made up of dependent tasks. It is used in work settings to build and manage data processing pipelines, schedule repeatable jobs, and coordinate task execution across steps. The skill involves setting up task dependencies, monitoring workflow completion, and handling complex batch processes in a structured way.

Stream Processing refers to the continuous processing of data streams in real-time, allowing for the analysis and manipulation of data as it is generated. This skill involves techniques and tools that enable the handling of large volumes of data in motion, facilitating immediate insights and actions based on incoming information. Knowledge of Stream Processing is used in various applications, such as monitoring system performance, detecting anomalies, and enabling real-time analytics in fields like finance, telecommunications, and IoT.