Big Data Protection
Masking & Testing in VLDBs, HDFS, Dark Data
Protection of large data sets
With the affordable IRI software (front-ended in Eclipse), you can now PII and other sensitive data in big data environments Discover, classify, and protect.
IRI's proven data-centric security solutions support multiple legacy and modern Data sources with multiple masking and encryption functions, which is identical in your local file system (with the power of CoSort) and Hadoop–Clusters running for unlimited scalability.
Mask existing production data or generate secure test data from scratch with a powerful Eclipse™ IDE, which includes or supports the following:
IRI DarkShield – to detect, deploy, and delete PII in unstructured text files, PDFs, and other formats from dark data repositories.
IRI FieldShield – for finding and masking PII in large flat or JSON files or very large databases, before, during, and after ETL, analytics, etc.
IRI Voracity – the total data management platform that supports both, along with Big Data Processing and Provisioning.
IRI RowGen – for intelligent, large Test data generation and virtualization
What is "Big Data" here?
The ability of PII in newer Hadoop Hive, S3, NoSQL, and cloud/SaaS platformsSources as well as in massive structured, semi-structured and unstructured Directly discover and mask data. Several assistants for data discovery and profiling in the IRI Workbench IDEs for Voracity, based on Eclipse™, enable the searching, classifying, extracting, and editing of PII in structured and unstructured sources. IRI Data Masking jobs leverage proven redaction and big data processing engines in multi-core servers or multi-node Hadoop environments.
Big Data Masking
With a privacy feature 12 Categories According to business and data protection regulations, you can target any element.
For example, choose format-preserving encryption or tokenization for credit card values, pseudonymization for names, randomization for ages, redaction for formulas, and character masking for national ID values.
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Big Test Data
Generate and populate huge amounts of secure, realistic test data into file, table, and report destinations.
Use production metadata –but no production data-, to create structurally and referentially correct volumes that correspond to the appearances, value ranges, frequency distributions, and layouts of real DB, DWH, and Hadoop environments.
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