The most common big data technologies include distributed storage systems, processing frameworks, and tools for real-time data handling. These technologies address challenges like scalability, speed, and managing diverse data types. Examples include Hadoop, Apache Spark, and Kafka, which form the backbone of many modern data pipelines.
Distributed storage and batch processing frameworks are foundational. Apache Hadoop, for instance, uses HDFS (Hadoop Distributed File System) to store large datasets across clusters and MapReduce for parallel processing. While Hadoop is reliable for batch jobs, Apache Spark has gained popularity for its in-memory processing, which speeds up iterative tasks like machine learning. Spark also supports SQL queries (Spark SQL), streaming (Spark Streaming), and graph processing (GraphX), making it versatile. Tools like Apache Hive enable SQL-like querying over Hadoop data, bridging the gap between traditional databases and big data systems.
For real-time and specialized use cases, technologies like Apache Kafka and NoSQL databases are essential. Kafka acts as a distributed event streaming platform, handling high-throughput data pipelines and real-time analytics. NoSQL databases like MongoDB (document-based) or Cassandra (wide-column) provide flexible schemas and horizontal scaling for unstructured data. Cloud-based data warehouses, such as Snowflake or Amazon Redshift, offer scalable analytics without managing infrastructure. Workflow tools like Apache Airflow automate pipeline orchestration, while TensorFlow and PyTorch support machine learning at scale. Cloud platforms (AWS, GCP) further simplify deployment with managed services like EMR or BigQuery. Together, these tools enable developers to build end-to-end solutions tailored to specific data needs.