Data Integration Implementation


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Overview Speed Your ETL Tool Leave Your ETL Tool Build New ETL Beyond ETL

All the features/functions listed below are supported in the IRI Voracity data management platform and constituent IRI Data Manager suite products.

GUI refers to the IRI Workbench Graphical User Interface. IRI Workbench is a free Integrated Development Environment (IDE), built on Eclipse,™ for integrating and transforming data with the SortCL program in Voracity, IRI CoSort, and all other IRI software.

DTP refers to the Data Tools Plugin (and Data Source Explorer) in the IRI Workbench. DDF refers to Data Definition Files, the metadata for source and target data layouts.

Operation Description
Discover data in pattern searches through DBs, files, and "dark data" documents. Perform traditional DB profiling and E-R diagramming on connected tables.
Create and modify jobs in multiple ways: a Sirius visual workflow palette, end-to-end wizards, GUI dialogs, and batchable 4GL scripts that are modeled and outlined in the GUI's syntax-aware editor and compatible with any external text editor
All structured data assets (including RDBs, LDIF, CSV, XML, COBOL, and other sequential files) available in the project explorer, data source explorer, and remote systems explorer. Support is also available for mainframe index files, unstructured data data file formats, ASN.1-compatible CDRs, multiple legacy/proprietary formats, and soon, big data and cloud/SaaS platforms; see the complete list here.
Job Wizards
Specify extract, transform, load, reorg, report, and test data generation jobs.
High performance, standalone and integrated ETL steps through IRI FACT, CoSort, and bulk DB loaders in Voracity (as well as more real-time via ODBC select/update). Coming are seamless Voracity options for Hadoop transformation processing via MapReduce, Spark, Storm, and Tez.
High performance "E" and pre-sorted "L". Design/manage "T" in DTP SQL editor
Generate detail and summary reports in the same-pass, or hand-off data to BIRT, et al
Encrypt, mask, de-ID, encode, hash, randomize, pseudonymize, tokenize, blur, or redact PII
Improve data quality with a variety of data scrubbing and standardization techniques
Acquire, filter, subset, re-map and/or copy data from old to new data stores
Version & Compare
Update, check-in, manage, and share metadata and jobs in GIT, or other SCCS
For DDF metadata, master data formats, set (lookup) files, rules, flow, and job scripts
Data Views
Multiple editor and cell display format supports for tables, files, and report formats
Use static or create dynamic schemas via target mapping and table creation options
Data mapping and search functions support manual data lineage and impact analysis. Track and compare metadata and other resources (scripts, rules, templates) in version control hubs.
Job Fragments
Save, reference, and re-use job and metadata subsets in standalone, portable .DDF files
Compare files or tables to identify, report on, and feed updates for smaller, real-time ETL
Report on values from "fuzzy" lookup logic where they satisfy 'other than equal' criteria
Windowed Aggregates
Perform aggregation within specified row ranges for fair cost accounting and other apps
Define, store, and re-use field-level business rules for data transformation, protection, and test data generation

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