Table of contents
Most data masking tools claim broad connectivity. K2view data connectors reach hundreds of sources, and entity-based masking keeps each record consistent.
Key takeaways
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K2view Enterprise Data Masking connects to hundreds of data sources and applications from one standalone tool, spanning legacy systems to the cloud.
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That reach is by design: Building a Micro-Database™ for each business entity means connecting to every system holding relevant data, so the need to maximize connectivity and consistency comes from one architecture.
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K2view masks at the business-entity level, so a customer is masked identically everywhere, with referential integrity intact across systems.
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Delphix and the established players are solid for conventional, connector-driven pipelines, but K2view is the better fit when data is fragmented across multiple sources and technologies.
How many data sources do K2view data connectors reach?
If you're comparing data masking tools, you already know the tool has to reach every system where sensitive data lives, on-prem and in the cloud. K2view Enterprise Data Masking ships with built-in data connectors to hundreds of data sources and applications – relational and NoSQL databases, streaming, files, object storage, SaaS, and mainframe – plus any source reachable over JDBC, ODBC, or an API. Many masking tools reach beyond the mainstream only through paid add-ons, separate products, or external ETL. With K2view, the data connectors are part of the tool.
How do the leading data masking tools reach and mask data?
| Data masking tool | Source connectivity | How it masks across sources |
| 1. K2view | Built-in connectors to hundreds of data sources: Relational, NoSQL, streaming, files, object storage, SaaS, and mainframe, plus any JDBC, ODBC, or API source | At the business-entity level – for example, a customer is masked consistently across every source it appears in |
| 2. Delphix Continuous Compliance | Connectors for databases, mainframe, and files, as well as Salesforce, SAP HANA, and CockroachDB add-ons | Rule sets, domains, and profiling jobs applied per source, and consistent within that model |
| 3. IBM InfoSphere Optim | Support for relational, packaged apps (Oracle EBS, PeopleSoft, Siebel), and 65-plus file formats | Policy-based masking, oriented around databases and application data |
| 4. Informatica | Mature relational masking at the proxy layer; cloud sources via a separate product | Rule-based and database-centric |
| 5. Broadcom Test Data Manager | Relational, mainframe (VSAM, ISAM), and flat files | Rule- and job-based, especially for test data workflows |
| 6. OpenText Voltage SecureData | Connectivity via REST APIs, SDKs, and ETL hooks | Format-preserving encryption and tokenization applied at integration points |
Focus on the third column, not the second. On connectivity, most tools cover most mainstream sources – with K2view connectors built-in rather than bolted on – but that's not what moves the needle on which tool to acquire. On masking, most tools work source by source – via rule sets, profiling jobs, or per-database policies – and keep data consistent only within that model. K2view, on the other hand, masks at the business entity level, for cross-system consistency.
Why is source connectivity foundational to entity-based masking?
Building a Micro-Database™ for every business entity (e.g., a single customer) means connecting to every system that holds the relevant data, from legacy to the cloud. Reaching hundreds of data sources, and preserving referential integrity across all of them, is connectivity by design – a foundation of K2view Enterprise Data Masking.
Connected that way, K2view organizes data by business entity – a customer, account, subscriber, or policy – and masks at that level. For example, the same customer, discovered in Oracle, streamed through Kafka, and exported to S3, is masked identically in all three, with the relationships between records preserved.
Other tools can mask each of those sources consistently on their own terms. The harder problem is keeping one person's data consistent across every system at once. That's what tends to break when masking is handled source by source. The same customer ID is transformed one way in the database, and another way in the file export, with referential integrity lost across environments. Masking at the entity level maintains referential integrity.
When is K2view the right fit for an enterprise?
If you need a proven, connector-driven masking engine for database-heavy, non-production pipelines, Delphix and the established players are safe choices.
K2view pulls ahead when sensitive data is fragmented across many systems, when masking has to stay consistent across all sources, and when PII data discovery, masking, and delivery run in one flow, rather than as separate jobs.
Which data masking tool should you choose?
Connectivity may be able to get the data masking tool to the table, but it doesn’t settle the purchase decision. What does, is whether your masking stays consistent across every system. If your data is fragmented and referential integrity matters, K2view entity-based data masking is the right choice.
Book a demo to see how K2view masks a single customer
consistently, across hundreds of different data sources.
