Apache CarbonData

Apache CarbonData is a high-performance, fully indexed columnar data format designed for big data analytics. It is native to the Hadoop ecosystem and deeply integrated with Apache Spark, enabling fast, interactive querying over petabyte-scale datasets. Developed originally by Huawei and now a top-level Apache Software Foundation project, CarbonData is optimized for analytical workloads with advanced indexing, compression, and encoding techniques 

12.Features

• Columnar Storage Format: Efficient data layout for analytical queries.
• Multi-Level Indexing: Reduces I/O and CPU usage by pruning unnecessary data scans.
• Advanced Compression & Encoding: Improves storage efficiency and query performance.
• Custom DDL/DML Support: Extended SQL syntax for table creation, data loading, updates, and deletes.
• Segment Management: Handles incremental data loads with transactional capabilities.
• Partitioning: Supports Hive-style and CarbonData-specific hash, list, and range partitions.
• Compaction: Merges segments to optimize query performance.
• External Table Support: Reads CarbonData files and infers schema for SQL querying.
• Bloom Filter Index: Accelerates filtering operations.
• Lucene Index: Enables efficient text data indexing.
• Materialized Views (MV): Supports query rewriting for pre-aggregated or joined data.
• Streaming Support: Near real-time data ingestion via Spark Streaming DSL.
• SDK Access: Read/write CarbonData files from non-Spark applications.
• Cloud & Distributed Storage: Compatible with S3, OBS, HDFS, and Alluxio.

Capabilities

• Big Data Query Acceleration: Sub-second response times on terabyte-scale data.
• Spark Integration: Deeply embedded in Spark’s DataSource API for optimized execution.
• Flexible Data Management: Supports updates, deletes, and incremental loads.
• Cross-Platform Compatibility: Works across Hadoop-based systems and cloud environments.
• Custom Application Integration: SDKs for Java and C++ enable CarbonData usage outside Spark.
• Interactive Analytics: Designed for fast, ad-hoc querying in analytical environments.
• Data Pruning: Efficiently reduces scanned data using indexing and partitioning.

Benefits

• Performance: Queries are significantly faster due to indexing and compression.
• Scalability: Handles petabyte-scale datasets with ease.
• Cost Efficiency: Runs on commodity hardware with optimized resource usage.
• Flexibility: Supports various data ingestion and querying patterns.
• Open Source: Free to use under Apache License 2.0.
• Ecosystem Integration: Works seamlessly with Spark, Hive, Presto, and cloud storage systems.
• Developer-Friendly: Rich API support and SQL extensions for custom applications.