Designed to take full advantage of scalable, commodity cloud computing and deliver high-performance real-time ingest and analytics:

  • Future-proof existing data warehouses by scaling horizontally on inexpensive commodity hardware without downtime
  • Easily profile, classify and integrate new data sources into existing models for analysis
  • Distributed computing (MapReduce, distributed storage, cloud platform, etc.) with massive parallel processing on Big Data in cloud environments
  • Integrated BI Middleware with Inzata query execution engine for optimal performance
  • Linear scalability for large number of users and projects with complex data models
  • Can be integrated with 3rd party apps via Inzata Widget objects
Near Real Time Reporting

1. Ad hoc reporting

a. on multidimensional, structured data

b. with excellent report retrieval time – near real time

c. with no pre-aggregated data

d. on distributed, partitioned (vertically and horizontally) columnar data store

1. Real time

a. data versioning with concurrent access to several data versions

b. efficient incremental load on columnar stored data

c. several data distribution patterns across cloud supporting high availability

Cloud scheme
Distributed Repository

Column based approach

The Cluster is a set of columns with common aggregation level and constrain

The Cluster is horizontally split into Partitions, therefore columns are split into Chunks

Partitions are organized into Allocation Units to define distribution over computing nodes (to optimize parallel processing and ensure redundancy for high availability)

lz4 compression algorithm (per chunk)

Each data chunk can be presented in several versions concurrently (there is possible to run queries against different versions concurrently or load data to a new version in parallel to reporting from other versions)

Chunks that contain the same data can share common physical data space