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Processing PB-scale high-capacity data that overcome limit of performance

Extreme data loading speed: Up to 300,000 records per second
Real-time indexing: Up to 2 million records per second
High-speed search from massive data: Takes about 20 seconds to search from 1 billion records

Unique solution optimized for time-series/log data processing in real time

Scales out up to petabyte-scale without degrading performance

IRIS maintains its performance stably even with increasing data volume.

Provides FTS (Full-text Search) function for the fast search of string

Full-text search engine embedded
Creates and saves full-text index when loading data
Fast search of numbers, letters, words and sentences ★ IRIS searches for a specific string within one second of a table with 5 billion records.

Stores data on hybrid storage(memory/SSD/HDD) for the real-time processing of large amount of data

In-memory data processing for real-time data retrieval
Creates index of 1 million to 10 million records per second without performance degradation

Provides a query(Select/Insert/Update/Delete) execution environment through SQL on distributed data nodes

Provides a capability to operate a production system in a short period of time through SQL support
Improves processing speed and performance through load balancing by executing SQL queries in parallel on distributed nodes.
Supports join operations between distributed tables

Spark / Hadoop integration

By integrating Spark and Hadoop, IRIS allows companies used open source to easily access data stored in IRIS DB using existing applications.

Scale-out Scalability

Can be expanded by just adding additional nodes because IRIS has been designed as a distributed architecture
Data nodes can be expanded without any service interruption during the system operation

Data replication for ensuring data reliability and load balancing

Stores and process input data in separate data nodes

Case Study

Large mobile carrier expands capacity of its data warehouse system at much lower cost	Global mobile carrier searches over 2 billion mobile network quality information on the map in real time
The existing data warehouse(DW) system becomes unstable due to the explosion of input data(over 100 billion data per day). ⇒ Instead of expanding capacity of the existing DW system at high cost, IRIS takes over heavy-load processes running on the DW.	Data can not be retrieved due to performance degradation when searching for massive data(more than 2 billion a day) related with the wireless quality ⇒ Stores and searches wireless quality data in IRIS
- Reduces the expansion cost of existing DW by 60% - Resolves the problem of service disruption caused by data explosion	- Allows users to search wireless quality data at low cost by introducing IRIS
Global mobile carrier is able to do service quality analytics per 4G/5G subscriber with Mobigen	Major government-affiliated organization shares cyber threat information in real time
Analytics system of service quality perceived by individual subscriber is necessary in order to resolve customer complaints about service quality. ⇒ A probe system together with IRIS is introduced.	Client wants to share the cyber attack information with external organizations in real time in order to respond to cyber attacks and prevent the spread of damages.
- Captures packet data from the network and generates the detailed call logs in real time - Allows operators to monitor end-to-end service quality per subscriber in real time	- Provides a comprehensive analysis environment for various types of cyber attacks - Contributes to prevent spread of damage by sharing information in real time

Awards

IRIS DB has acquired GS(Good Software) certificate and received the New Software Grand Prize.

Mobigen's IRIS DB was certified with GS(Good Software) related to big data DB for the first time in South Korea The outstanding quality of IRIS DB has been recognized with the New Software Grand Prize from the Minister of Knowledge Economy.

Mobigen has proved that it enabled to analyze more than 100 billion records per day successfully by applying IRIS DB Cluster to systems that are desired to process such big data.