Expert Interview: Architecture and Trends of Big Data Platforms

Although big‑data platforms consist of many components, newcomers often feel overwhelmed; DataFun interviewed several experts to clarify the architecture, pinpoint key points, difficulties, and trends, helping readers focus on essential knowledge.

The article first introduces the overall big‑data platform architecture, then delves into specific modules:

Data Integration – covering log synchronization (Flume, Vector), extraction tools (DataX, BitSail), and transmission queues (Kafka, RabbitMQ, Pulsar) with expert comments on reliability and performance.

Data Storage & Computation – discussing HDFS storage characteristics, offline engines (MapReduce, Hive, Spark) and real‑time engines (Flink, Storm, Spark Streaming) with expert insights on suitability and optimization directions.

Data Scheduling – reviewing common task schedulers (Crontab, Apache Airflow, Oozie, Azkaban, XXL‑JOB, DolphinScheduler) and resource managers (YARN, Azkaban), accompanied by expert opinions on their strengths in big‑data scenarios.

Big‑Data Query – comparing OLAP engines (Presto, StarRocks, Impala) and optimization tools (Alluxio, JuiceFS, JindoFS), with experts evaluating performance, ease of use, and integration with cloud storage.

Finally, the experts discuss future trends, emphasizing faster OLAP processing, elastic storage resembling single‑node databases, cloud‑native architectures, and continued development of real‑time computation frameworks like Flink.

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