Big Data: Hadoop and Kafka

Big Data: Hadoop and Kafka

Hadoop

• Definition: Hadoop is an open-source, distributed computing framework used for storing and processing large datasets.
• Key Components:
  • HDFS (Hadoop Distributed File System): a distributed storage system that stores data across a cluster of machines.
  • MapReduce: a programming model used for processing data in parallel across a cluster of machines.
• Features:
  • Scalability: handles large datasets and scales horizontally by adding more nodes to the cluster.
  • Flexibility: supports various data formats and can process unstructured and semi-structured data.
  • Cost-effective: uses commodity hardware and open-source software, reducing costs.
• Use Cases:
  • Data Warehousing: used for storing and processing large datasets for analytics and reporting.
  • Log Processing: used for processing and analyzing large logs from applications and systems.

Kafka

• Definition: Kafka is an open-source, distributed streaming platform used for building real-time data pipelines and streaming applications.
• Key Concepts:
  • Topics: a named stream of messages that can be produced and consumed by applications.
  • Producers: applications that send messages to Kafka topics.
  • Consumers: applications that subscribe to Kafka topics and consume messages.
• Features:
  • High-throughput: handles high-volume data streams and provides low-latency message delivery.
  • Fault-tolerant: designed to handle node failures and provide guaranteed message delivery.
  • Scalable: horizontally scalable and can handle large amounts of data.
• Use Cases:
  • Real-time Analytics: used for building real-time analytics and event-driven architectures.
  • Log Aggregation: used for aggregating and processing logs from applications and systems.

Comparison: Hadoop vs. Kafka

• Hadoop: focused on batch processing and storing large datasets for offline analytics.
• Kafka: focused on real-time data streaming and event-driven architectures.