| Framework | Description | Use Cases | Strengths |
|---|---|---|---|
| Apache Hadoop | A distributed framework for storing and processing large datasets using the Hadoop Distributed File System (HDFS) and the MapReduce programming model. | Batch processing, ETL, data storage. | – Scalable and reliable storage (HDFS). – Proven technology. |
| Apache Spark | A unified analytics engine for large-scale data processing, offering in-memory computing and support for batch, streaming, and ML workloads. | Streaming analytics, machine learning, graph processing, batch ETL. | – In-memory processing for speed. – Broad use case support. |
| Apache HBase | A distributed, NoSQL database built on HDFS for real-time, random read/write access to large datasets. | Real-time applications, time-series data, IoT data storage. | – Low-latency reads/writes. – Scales horizontally. |
| Apache Flink | A framework for real-time stream processing and distributed batch processing, with low-latency and high-throughput capabilities. | Real-time analytics, event processing, streaming ETL. | – True real-time processing. – Stateful stream management. |
| Presto | A distributed SQL query engine designed for fast, interactive queries across large datasets, optimized for analytics over heterogeneous data sources. | Interactive SQL querying, federated queries, analytics on data lakes. | – High performance for SQL. – Query federation. |
| Hive | A data warehouse tool built on Hadoop, providing SQL-like query capabilities (HiveQL) for processing and analyzing structured datasets. | Data warehousing, batch analytics, schema-on-read processing. | – Familiar SQL-like interface. – Integrates with Hadoop. |