{"id":15979,"date":"2022-06-28T12:33:49","date_gmt":"2022-06-28T16:33:49","guid":{"rendered":"https:\/\/www.iri.com\/blog\/?p=15979"},"modified":"2025-11-12T06:13:55","modified_gmt":"2025-11-12T11:13:55","slug":"real-time-incremental-data-masking","status":"publish","type":"post","link":"https:\/\/www.iri.com\/blog\/data-protection\/real-time-incremental-data-masking\/","title":{"rendered":"Real-Time, Incremental Data Masking"},"content":{"rendered":"

Abstract: <\/b><\/em>The previous article<\/a> in this series of 4 demonstrated real-time database replication with IRI Ripcurrent, an IRI-developed command-line Java application that utilizes the <\/span>Debezium embedded engine<\/span><\/a> and the streaming features of the <\/span>IRI (CoSort) SortCL program<\/span><\/a>. Together they react in real-time to database change events.<\/span><\/em><\/p>\n

The capability demonstrated in the <\/span>previous article<\/span><\/a> can also be augmented with the classification and masking of sensitive data in transit to file or database targets. When data in the source table is inserted or updated, it is moved and masked in real-time to the designated target table using rules defined in the IRI FieldShield<\/a> data masking tool. When data in the source table is deleted, the same row(s) will be deleted in the (different) target table.<\/span><\/em><\/p>\n

Prerequisites<\/b><\/h4>\n

Assuming the prerequisites detailed in the <\/span>first article<\/span><\/a> have been met, for this case an IRI Data Class & Rule Library<\/a> are also required.<\/span><\/p>\n

For Ripcurrent, a default rule is required to be assigned for each data class. The default rule is used by Ripcurrent to apply a chosen <\/span>masking function<\/span><\/a> to data or a column name matching the specified data class search parameters.<\/span><\/p>\n

Generating a Ripcurrent Properties File<\/b><\/h4>\n

Before running Ripcurrent, a property file should be created to define configuration options that are required for Ripcurrent to run.<\/span><\/p>\n

After installing the Ripcurrent feature in IRI Workbench, the <\/span>Ripcurrent Properties Generator <\/span><\/i>wizard is available from the Voracity menu to assist in generating a properties file for Ripcurrent.<\/span><\/p>\n

On the first page of the wizard, options can be specified pertaining to the database to be monitored. In this image, an Oracle connection was selected and a filter was specified to narrow down the tables to be monitored to only those in the <\/span>SCOTT<\/span><\/i> schema.<\/span><\/p>\n

\"\"<\/p>\n

In this case, the DB name (required to set up a configuration to monitor an Oracle database) was also entered, <\/span>XE<\/i><\/b> in this case.<\/span><\/p>\n

On the second page of the wizard, there are options to replicate to database tables, files, or <\/span>both <\/i><\/b>files and database tables. The image shows the choice of replicating data changes from the source Oracle database to a MySQL database with existing tables of the same name and structure as the source database, as well as to Excel files.\u00a0<\/span><\/p>\n

\"\"<\/p>\n

On the third page, locations of an IRI data class library and rule library can be specified. The image shows the selection of the library files created earlier in the article:<\/span><\/p>\n

\"\"<\/p>\n

On the final page, the image shows the selection of a log location for structural change event information from the source database. Names for the connector and server connection used by Debezium are also specified.\u00a0<\/span><\/p>\n

\"\"<\/p>\n

After the properties file was successfully generated for my configurations, I began the execution of Ripcurrent by running the <\/span>ripcurrent.bat<\/i><\/b> script. After completion of an initial snapshot of the source database (if running for the first time), Ripcurrent will be monitoring for changes from the source database.<\/span><\/p>\n

Adding Test Data to Source Tables with IRI RowGen<\/b><\/h4>\n

Next, I will run a batch file generated by the <\/span>New Database Test Data<\/span><\/i><\/a> wizard, accessible from the <\/span>IRI RowGen<\/span><\/a> menu to synthesize and simulate new data coming into the source tables. The New Database Test Data wizard synthesizes data consistently by classifying database columns and pairing them with generation rules.<\/span><\/p>\n

The image below shows an entity-relationship mapping diagram of the test data batch operation, produced in the IRI Workbench<\/a> GUI for all IRI software.<\/span><\/p>\n

\"\"<\/p>\n

Replication of Data from Source Tables to Multiple Targets, with Consistent Masking Rules Applied to Classified Data<\/b><\/h4>\n

After running the batch file to generate data for 5 tables in the source database, Ripcurrent recognized the new data inserted and triggered a replication and masking operation to the targets.<\/span><\/p>\n

This is a sample of the data that was generated in one of the<\/span> source<\/b> Oracle database tables (named DM_EMPLOYEES):<\/span><\/p>\n

\"\"<\/p>\n

Ripcurrent was silently triggered by this insertion event, and automatically masked and moved the new data into its defined targets, per this event log:<\/span><\/p>\n

\"\"<\/p>\n

See that a series of Excel files have also been created in the target directory:<\/span><\/p>\n

\"\"<\/p>\n

In the Excel file associated with the DM_EMPLOYEES table in the source database, notice that the email values have been modified from the original value.<\/span><\/p>\n

\"\"<\/p>\n

Additionally, in the target MySQL database, data was replicated to a table with the same name and structure as the source table, with the email address column encrypted to the same values as in the Excel file.<\/span><\/p>\n

\"\"<\/p>\n

This column was classified as containing email addresses based on the regular expression pattern to match emails defined in the data class library. The original email values were encrypted with format-preserving encryption, defined by the default rule that was mapped to the EMAIL data class.<\/span><\/p>\n

Conclusion<\/b><\/h4>\n

This article demonstrated another use case of Ripcurrent that builds on the database data replication process demonstrated in the previous article \u2013 that is, consistent data classification, data masking and data replication as rows are inserted, updated or deleted in source tables and refresh into target tables (usually in a lower environment).\u00a0<\/span><\/p>\n

This approach ensures that data can be dynamically replicated to test schema, with sensitive data classified appropriately and consistently masked according to data classes and rules that can be flexibly defined.<\/span><\/p>\n

The next and final article<\/a> in this series deals with real-time notifications of structural changes to the database which can impact the layout of the source or target tables. This would indicate the need to re-run data class search operations on your tables.<\/span><\/p>\n

\n
\n
\n

Frequently Asked Questions (FAQs)<\/h3>\n
\n
1. What is real time incremental data masking and what problem does it solve? <\/i><\/i><\/div>\n
Real time incremental data masking applies classification and masking to only the rows that change, as they change, while data is replicated from a source database to file or database targets. This protects sensitive values during continuous movement so lower environments and downstream systems are refreshed with safe, consistent data without waiting for batch jobs.<\/div>\n<\/div>\n
\n
2. How does Ripcurrent perform change capture and masking in this workflow? <\/i><\/i><\/div>\n
Ripcurrent embeds Debezium to listen for inserts, updates, and deletes, then streams those change events through the IRI SortCL<\/a> program (powering FieldShield) where pre-defined data masking rules<\/a> are applied. Masked rows are written to the defined targets, and deletes in the source are propagated to the target tables.<\/div>\n<\/div>\n
\n
3. What are the prerequisites to enable real time masking with Ripcurrent? <\/i><\/i><\/div>\n
You need: 1) the IRI Voracity Ripcurrent feature<\/a> installed in your IRI Workbench IDE<\/a>; 2) a generated Ripcurrent properties file; 3) JDBC or other connections to the source and target systems; and, 4) an IRI Data Class and Rule Library<\/a> with data masking functions assigned as rules to each active data class.<\/div>\n<\/div>\n
\n
4. How are masking rules selected and applied consistently across targets? <\/i><\/i><\/div>\n
Each data class, such as Email or SSN, has a default rule like format preserving encryption or pseudonymization. When Ripcurrent detects a field that matches a class, it applies that class\u2019s default (deterministic data masking<\/a>) rule so the same source value becomes the same masked value in every target.<\/div>\n<\/div>\n
\n
5. What targets can receive replicated and masked data in this setup? <\/i><\/i><\/div>\n
You can replicate to relational databases that mirror the source table structures, to files such as Excel for operational sharing, or to both at once. Target selection is configured in the Ripcurrent properties wizard.<\/div>\n<\/div>\n
\n
6. How are the Ripcurrent properties created and what do they control? <\/i><\/i><\/div>\n
Use the Ripcurrent Properties Generator wizard in IRI Workbench to specify the source database, table filters, destinations, locations of the data class and rule libraries, connector names, server settings, and log paths. The wizard outputs the properties file that Ripcurrent uses at runtime.<\/div>\n<\/div>\n
\n
7. What happens when a source row is updated or deleted? <\/i><\/i><\/div>\n
For updates, only changed columns are reprocessed and masked, then written to targets. For deletes, Ripcurrent issues corresponding deletes in the target tables so downstream environments remain synchronized.<\/div>\n<\/div>\n
\n
8. How can I generate realistic change events to test the pipeline? <\/i><\/i><\/div>\n
Use the IRI RowGen<\/a> wizard in IRI Workbench called New Database Test Data job<\/a> to synthesize structurally and referentially correct data. Inserting this data into source tables triggers Ripcurrent, which replicates and masks those rows into targets for end to end validation.<\/div>\n<\/div>\n
\n
9. What masking techniques are recommended for transactional consistency? <\/i><\/i><\/div>\n
Format preserving encryption is commonly used to maintain patterns and lengths, while deterministic pseudonymization preserves value consistency across rows and systems. Choose techniques that keep schema constraints valid and support referential integrity in test environments.<\/div>\n<\/div>\n
\n
10. How is latency handled and what performance factors matter? <\/i><\/i><\/div>\n
Latency depends on source CDC throughput, network I\/O, and transformation complexity. Limiting active data classes to those required, optimizing rule choices, and filtering to the specific schemas and tables of interest help maintain low end to end delay.<\/div>\n<\/div>\n
\n
11. What logging or audit artifacts are produced during streaming operations? <\/i><\/i><\/div>\n
Ripcurrent writes change event logs and structural change notifications, while FieldShield rule usage is traceable through the data class and rule library mappings. These artifacts support verification, troubleshooting, and compliance reporting.<\/div>\n<\/div>\n
\n
12. How do I control which tables and columns are monitored? <\/i><\/i><\/div>\n
Apply schema and table filters in the properties wizard and scope your data classes to only the columns or patterns that represent PII. Using precise classification reduces overhead and false positives.<\/div>\n<\/div>\n
\n
13. What happens if a masked value would violate a target constraint? <\/i><\/i><\/div>\n
If a rule would break a constraint, such as making a number non numeric, switch to a format preserving method for that class or add validation logic. Reprocess the affected events so targets stay consistent and load compliant data.<\/div>\n<\/div>\n
\n
14. How can I ensure deterministic masking across multiple targets and runs? <\/i><\/i><\/div>\n
Use stable keys and consistent rule configurations for classes that require repeatable outcomes. Keep encryption keys and rule definitions secure and version controlled so the same inputs always yield the same masked outputs.<\/div>\n<\/div>\n
\n
15. What is the best way to manage configuration changes over time? <\/i><\/i><\/div>\n
Store the Ripcurrent properties, data class library, and rule library in source control. When schemas evolve, regenerate properties with the wizard, rerun classification, and update default rules as needed, then restart Ripcurrent with the revised configuration.<\/div>\n<\/div>\n
\n
16. How does this approach support data privacy compliance goals? <\/i><\/i><\/div>\n
By classifying and masking sensitive fields in motion, the pipeline prevents exposure of raw PII in non production systems and shared files. The consistent application of rules and the presence of audit logs help demonstrate appropriate safeguards during reviews.<\/div>\n<\/div>\n
\n
17. What monitoring steps help keep the pipeline healthy? <\/i><\/i><\/div>\n
Track Ripcurrent console output and logs for connector status, check target row counts against source change volumes, and periodically validate a sample of masked fields for rule correctness. Add alerts for connector disconnects or schema drift.<\/div>\n<\/div>\n
\n
18. Can I run masking only on selected columns while pass through others? <\/i><\/i><\/div>\n
Yes. Only fields that match active data classes have rules applied. Non sensitive columns are streamed unchanged, which preserves performance and data utility.<\/div>\n<\/div>\n
\n
19. How do I handle encryption key rotation without breaking consistency? <\/i><\/i><\/div>\n
Plan rotations during maintenance windows and, if deterministic behavior is required across time, maintain versioned keys and mappings. Decide whether post rotation data must match prior masked values or can start a new deterministic sequence.<\/div>\n<\/div>\n
\n
20. What is the typical rollout path from proof of concept to production? <\/i><\/i><\/div>\n
Start by classifying a subset of tables, define default rules, and validate masking outcomes in a non production target. Expand table coverage, tune performance, add monitoring, and then move the same configuration into production to protect live change streams.<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/section>\n","protected":false},"excerpt":{"rendered":"

Abstract: The previous article in this series of 4 demonstrated real-time database replication with IRI Ripcurrent, an IRI-developed command-line Java application that utilizes the Debezium embedded engine and the streaming features of the IRI (CoSort) SortCL program. Together they react in real-time to database change events. The capability demonstrated in the previous article can also<\/p>\n

Read More<\/a><\/div>\n","protected":false},"author":119,"featured_media":16027,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_exactmetrics_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"footnotes":""},"categories":[8,34,91,29,3],"tags":[218,14,1305,51,1796,1398,2012,1239,651,98,1486,1696,1699,2080,1698,2079,1697],"class_list":["post-15979","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-protection","category-business","category-iri-workbench","category-test-data","category-vldb-operations","tag-cdc","tag-data-masking","tag-data-masking-tools","tag-data-replication","tag-database-column-pii","tag-database-masking","tag-database-replication","tag-db-replication","tag-dynamic-data-masking","tag-format-preserving-encryption","tag-incremental-data-masking","tag-iri-ripcurrent","tag-real-time-data-masking","tag-real-time-database-replication","tag-real-time-db-data-replication","tag-real-time-replication","tag-ripcurrent"],"acf":[],"yoast_head":"\nReal-Time, Incremental Data Masking - IRI<\/title>\n<meta name=\"description\" content=\"See how real-time (incremental) database data masking works during data replication to keep sensitive information secure and compliant.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.iri.com\/blog\/data-protection\/real-time-incremental-data-masking\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Real-Time, Incremental Data Masking\" \/>\n<meta property=\"og:description\" content=\"See how real-time (incremental) database data masking works during data replication to keep sensitive information secure and compliant.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.iri.com\/blog\/data-protection\/real-time-incremental-data-masking\/\" \/>\n<meta property=\"og:site_name\" content=\"IRI\" \/>\n<meta property=\"article:published_time\" content=\"2022-06-28T16:33:49+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-11-12T11:13:55+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.iri.com\/blog\/wp-content\/uploads\/2022\/06\/Blog-3-Image-Feature.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1200\" \/>\n\t<meta property=\"og:image:height\" content=\"628\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Devon Kozenieski\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Devon Kozenieski\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"11 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/www.iri.com\/blog\/data-protection\/real-time-incremental-data-masking\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/www.iri.com\/blog\/data-protection\/real-time-incremental-data-masking\/\"},\"author\":{\"name\":\"Devon Kozenieski\",\"@id\":\"https:\/\/www.iri.com\/blog\/#\/schema\/person\/de972c035aaeecfc40a3ae2ea5ff7ba1\"},\"headline\":\"Real-Time, Incremental Data Masking\",\"datePublished\":\"2022-06-28T16:33:49+00:00\",\"dateModified\":\"2025-11-12T11:13:55+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/www.iri.com\/blog\/data-protection\/real-time-incremental-data-masking\/\"},\"wordCount\":1890,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/www.iri.com\/blog\/#organization\"},\"image\":{\"@id\":\"https:\/\/www.iri.com\/blog\/data-protection\/real-time-incremental-data-masking\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/www.iri.com\/blog\/wp-content\/uploads\/2022\/06\/Blog-3-Image-Feature.png\",\"keywords\":[\"CDC\",\"data masking\",\"data masking tools\",\"data replication\",\"database column PII\",\"database masking\",\"database replication\",\"DB replication\",\"dynamic data masking\",\"format-preserving encryption\",\"incremental data masking\",\"IRI Ripcurrent\",\"real-time data masking\",\"real-time database replication\",\"Real-time db data replication\",\"real-time replication\",\"Ripcurrent\"],\"articleSection\":[\"Data Masking\/Protection\",\"IRI Business\",\"IRI Workbench\",\"Test Data\",\"VLDB\"],\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/www.iri.com\/blog\/data-protection\/real-time-incremental-data-masking\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.iri.com\/blog\/data-protection\/real-time-incremental-data-masking\/\",\"url\":\"https:\/\/www.iri.com\/blog\/data-protection\/real-time-incremental-data-masking\/\",\"name\":\"Real-Time, Incremental Data Masking - 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