IRI FACT (Fast Extract)
Unload huge tables to flat files fast. Beat SLA deadlines. Use your database data everywhere.
Accelerate Data Acquisition
Speed unloads up to 7X with parallel query technology. Performance scales linearly in volume . . . extract billions of rows in minutes!
9 Databases -> 1 Unloader
Use the same fast extraction tool for all of your databases. No learning curve -- use SQL syntax in simple text scripts or an Eclipse GUI.
Data Ready for Everyone
Produce flat files that any application can use. Plug into and feed ETL operations and offline reorgs. Archive your data. Migrate your VLDB.
IRI FACT Use Cases
"We have used FACT for almost 10 years to speed Oracle table unloads. We tried multiple alternatives and nothing could beat FACT’s extraction speed."
"One of the keys to the rapid deployment and use of our transaction data is FACT. We could not meet our production deadlines without it."
"FACT is an essential component of our banking application because it, along with CoSort, produces faster and cheaper results than any ETL tool."
The Only Unload Tool You Need
Fast Extract Tables from:
- DB2 UDB
- SQL Server
IRI FACT is a Critical Step in:
- Database Archive
- Database Migration
- Data Protection
- Data Replication
- DW ELT
- DW ETL
- Offline Reorgs
- Offline Reporting
When to Consider IRI FACT
- Are you a data warehouse ETL / ELT architect, or DBA needing faster extraction?
- Do you experience unload, query, or load bottlenecks with large transaction tables?
- Are you responsible for DB archive, replication, migration, or subsetting operations?
- Upgrading or leaving Oracle, DB2, Sybase, MySQL, SQL Server, Altibase, or Tibero?
- Noticing slower query response times from unordered rows or fragmented table space?
What Others Are Reading
Among the key issues for companies who need to unload big data from Oracle, DB2, Sybase and SQL Server are speed, scalability, and simplicity.
Over time, data in large RDBMS tables eventually become fragmented. Reorgs can save table space and improve query performance.
The decision to transform data inside or outside the database has significant speed, support, and spend consequences.