Apache Flume is an open-source service for efficiently collecting, aggregating, and moving large amounts of log data from different sources to a centralized data store or data processing framework. It is used for data ingestion and is commonly used in big data environments to handle data streams from various sources. Flume is highly configurable and flexible, allowing users to customize the data flow process to suit their specific needs. Knowledge of Flume is required for professionals working with big data technologies and data analytics.

Apache HBase is a distributed NoSQL database that allows for storing and managing large amounts of data in a fault-tolerant manner. It is built on Apache Hadoop and supports low-latency access to data through its column-oriented architecture. HBase is commonly used in large-scale web applications and as a backend for analytics systems. A person with skills in Apache HBase is expected to have knowledge of its architecture, administration, and data modeling.

Apache Oozie is a workflow scheduler system for Apache Hadoop that allows users to define and run workflows, or sequences of processes that execute Hadoop jobs. It is designed to simplify the coordination of large-scale data processing tasks, allowing developers to create, schedule, and manage complex workflows that involve multiple Hadoop jobs and other dependencies. Oozie supports a range of Hadoop ecosystem components and other data processing systems, including Pig, Hive, MapReduce, and Spark, making it a versatile tool for building and running big data applications.

Hadoop Distributed File System (HDFS) is an open-source software framework that is designed to store and manage large data sets across clusters of computers. It is based on the Google File System (GFS) and provides scalable, reliable, and fault-tolerant storage for big data applications. HDFS divides data into blocks and distributes them across multiple nodes in a cluster, which allows for efficient and parallel processing of data. Hadoop developers and administrators need to have specialized skills in configuring, managing, and optimizing HDFS for specific data processing tasks.

Sqoop is an open-source software tool used to transfer data between Hadoop and relational databases or other structured data sources. It can import data from structured sources like MySQL, Oracle and export data from Hadoop to these sources. Sqoop makes it easy to transfer large volumes of data efficiently between the Hadoop ecosystem and external data stores, which is essential for big data processing.