Scalable, Secure and Flexible Analytics Database

More Efficient Query Processing

• Vectorized query execution exploits Single Instruction, Multiple Data (SIMD) support in x86 CPUs
• Query result caching avoids rerunning queries when there are no changes

Maximizing CPU Cache for Execution

• Uses private CPU core and caches as execution memory – 100x faster than RAM
• Delivers significantly greater throughput without limitations of in-memory approaches

Other CPU Optimizations

Supports hardware-accelerated string-based operations, benefiting selections on strings using wild card matching, aggregations on string- based values, and joins or sorts using string keys

Column-based Storage

• Reduces I/O to relevant columns
• Opportunity for better data compression
• Built in storage indexes maximize efficiency

Data Compression

• Multiple options to maximize compression: Run Length Encoding (RLE), Patched Frame of Reference (PFOR), Delta encoding on top of PFOR, Dictionary encoding, and LZ4: for different string values
• 4-6x compression ratios common for real-world data

Live Data

Maintain real-time access to data as it is compressed on disk, resulting in I/O and CPU savings and shorter execution time

Easy Integration and Migration

• Use DataFlow for fast data loading and DataConnect with over 200 connectors and templates to easily source data at scale.
• Loads structured and semi-structured JSON data, including event-based messages and streaming data without coding.
• Move a database to a cloud or remote datacenter in one step using the integrated “clonedb” function

Storage Indexes

• Automatic min-max indices enable block skipping on reads
• Eliminates need for explicit data partitioning strategy

Parallel Execution

Flexible adaptive parallel execution algorithms to maximize concurrency while enabling load prioritization

MPP Architecture

• Handles thousands of users, nodes, and petabytes of data
• Exploits redundancy in HDFS to provide system-wide data protection

Security

• Role-based security
• Re-keying encryption
• Dynamic data masking
• Column-level de-identifcation

Manageability

• Queue-based workload management to dynamically adjust queues based on resource availability and quotas
• Easy-to-use administrative console and SQL editor

Native Spark Integration

• Spark- powered direct query access
• Direct connection to Spark functionality via DataFrames
• Fast streaming for machine learning and artificial intelligence

Extensive SQL Support

• Standard ANSI SQL enabling the use of existing SQL without rewrite
• Advanced analytics, including cubing, grouping, and window functions

Mature Query Optimizer

• Mature and proven cost-based query planner
• Optimal use of all available resources, including node, memory, cache, and CPU