Automate and accelerate the data discovery and mapping process
According to Forrester Research (June 2007): “Data discovery and integration steps take between 50% and 80% of any business intelligence effort… and are where many initiatives fail.”
But the discovery and integration challenge is not limited to business intelligence projects. Every data integration initiative – whether it supports better decision making, a merger/acquisition, standards compliance, or other business need – requires a set of processes to be completed before the data can be made available to business users. Though this set of processes is fairly well understood by industry practitioners, there are still many areas left unaddressed with the current toolsets in the marketplace and, therefore, remain time-consuming, inefficient, unpredictable, and costly.
Sypherlink Harvester automates and accelerates this traditionally manual discovery and mapping process, thereby reducing the time, effort and cost required, while increasing the quality of data involved.
Harvester provides design-time discovery and mapping and provides a reusable and consistent methodology for performing the entire data discovery, mapping, and pre-ETL (extract, transform, and load) process. In addition, Harvester provides a flexible, shareable platform which can be leveraged across various data integration initiatives, helping organizations move away from one-off work to embrace organizational best practices.
Automated Data and Metadata Discovery
Harvester automatically discovers key information, including metadata, database statistics, sample data and data profiling metrics from a variety of data sources, including relational, non-relational and non-traditional data sources. This information is leveraged by Harvester to determine where relationships exist across systems.
Harvester Analyzer: Automated Many-to-Many Analysis
Harvester Analyzer utilizes unique, patented heuristics-matching technology to speed the analysis of relationships across multiple data sources. This analysis is especially valuable in projects where there is limited information or domain expertise available about data sources and their contents. The software examines both the database field content and the field metadata – such as field names and field attributes. This analysis can be performed on multiple systems simultaneously, further accelerating the previously tedious and time-consuming mapping process. Users have the ability to define which data sources are the source(s) and which are the target.
Harvester Relationship Manager: An Intuitive Mapping Interface
Harvester Relationship Manager is a powerful platform for allowing data architects and analysts to review, approve and manage the results of the automated data discovery and mapping process.
The intuitive, point-and-click interface allows users to view database relationships – both direct and indirect – and the underlying source data. It provides visual feedback to the user via detailed progress displays to enable users to track the mapping process from start to finish and simplify the mapping of exceptions found during the automated discovery and mapping process.
A Powerful Data Management Platform
Harvester is a powerful and flexible platform for supporting the ongoing need to manage relationships across critical data assets. Harvester provides central management and administration for all of the application’s analyses, generated relationships, and both automated and manual mappings. This information can be easily accessed via reports, enabling the user to track a project’s overall progress.
The software also enables project collaboration, providing the ability to partition and assign individual mapping and ETL tasks based on specific schemas/tables in designated sources and targets. Once complete, these individual tasks can then be imported back into the master project.
Accelerate ETL and Complementary Data Management Tools
Harvester feeds key metadata and mapping information to its companion data integration application, Harvester Integrator, as well as popular Extract, Transform and Load (ETL), design and modeling, and metadata management tools.
- Fast access to data and statistics
- Analysis across an unlimited number of data sources
- Powerful relationship discovery heuristics
- Easy organization of mapping and analysis work
- Data source schema comparison
- Advanced data profiling capabilities
- Quick, easy creation of business flow (pre-ETL)
- Automatic conversion of mappings to ETL
- Re-use of data mappings
- Broad relational RDBMS support and legacy flat-file support
- Accelerates data integration discovery and mapping initiatives
- Provides quantifiable ROI and time to value
- Consistent and reusable results
- Shares metadata with other data management tools
- Data integration projects
- Master Data Management initiatives
- Business Intelligence projects