1. Hadoop: Hadoop is an open-source, distributed storage and computation framework that can handle large-scale data sets.

2. Spark: Spark is a fast, general-purpose, distributed computing system that can be used for large-scale data processing and analysis.

3. Storm: Storm is a distributed real-time computing system that can be used to process streaming data.

4. Flink: Flink is a distributed streaming and batch processing system that can be used to process large-scale data sets.

5. Hive: Hive is a Hadoop-based data warehouse tool that can be used to query and analyze large-scale data.

6. HBase: HBase is a Hadoop-based non-relational database that can be used to store large-scale data.

7. Kafka: Kafka is a distributed stream processing platform that can be used to process real-time data streams.

8. Zookeeper: Zookeeper is a distributed orchestration service that can be used to maintain configuration information, namespaces, distributed synchronization, and more.

These technologies are all very important components of big data development. Mastering these technologies can help you better process and analyze large-scale datasets.