Biz Metadata Management
Biz Metadata Management
Enables parallel processing of extraction, conversion, and transfer functions to extract change data in real-time.
GUI-based solution that allows users to monitor data and resolve errors in real-time.
Convert change data into a format that can be loaded into the target database in real-time.
Minimize the use of DB engine and system resources using transcription logs (Redo log, Archive log) in the DBMS.
Enables parallel processing of extraction, conversion, and transfer functions to extract change data in real-time.
GUI-based solution that allows users to monitor data and resolve errors in real-time.
Convert change data into a format that can be loaded into the target database in real-time.
Minimize the use of DB engine and system resources using transcription logs (Redo log, Archive log) in the DBMS.
Minimized load on the source system by extracting change data from DBMS Redo and archive logs.
Supports processing methods tailored to the system using DBMS APIs and Log-Sync agents.
The platform ensures high-performance loading of initial large-scale
data by incorporating FACT (Fast and Accurate Capture Technology),
a high-speed extraction engine for large volumes of data.
The capture process allows for a specified number of retry attempts during an incident.
It also verifies the checkpoint at the time of the incident to ensure that data from the point of
failure onwards is accurately reflected, preventing data loss.
This enables effective response to DBMS and network failures, ensuring data integrity and continuity of operations.
Includes web monitoring to detect any incidents/issues in real-time. When an incident occurs,
the operations team is promptly notified through internal SMS or SMTP integration.
Additionally, operators can perform reprocessing from a specified point using
the System Change Number (SNC) or timestamp value.
This allows for efficient recovery and resolution of issues.
Extract data from source and target databases to perform data reconciliation for daily updates or specific time ranges.
Compare the records at a granular level and identify any mismatched table records.
Additionally, users can modify data based on the source to maintain data integrity.
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