Data protection on ETL pipelines

Adaptive protects data as it moves through ETL pipelines from the source to a data warehouse like Snowflake or other data sources.

Data is extremely fluid in organizations. Most organizations use ETL data pipelines to move data from structured data sources such as Postgres and MySQL to data warehouses like Snowflake, Databricks, or even S3 buckets. Protecting data becomes challenging once it reaches its destination, as it is then accessible to all different folks, such as data science and analytics teams.

$4.45 million

The global average cost of a data breach in 2023, marking a 15% increase from the previous year.

78%

Share of organizations storing sensitive data in SaaS applications, heightening data leakage risks.

$401

The average cost per lost or stolen record in 2022, up 2.4% from 2021

One of the major challenges for organizations is ensuring sensitive data is masked while moving through the ETL pipeline. Without these protection policies, sensitive data can be propagated to various locations, increasing the risk of leakage. Furthermore, the high number of ETL pipelines in an organization makes it difficult to maintain visibility on the application of protection policies.

Set Data Protection Policies centrally and ensure only masked data moves to the destination data source.

Adaptive applies masking or tokenization policies to sensitive data, ensuring that only protected data reaches the destination data source. This approach minimizes the exposure of sensitive data and helps organizations remain compliant with privacy regulations by default. By centrally defining protection policies as configurations rather than embedding them in code, organizations gain higher visibility, enabling them to scale these policies across multiple ETL pipelines without errors.

Masking and Tokenization Policies

Utilises native database views to apply protection policies such as masking or tokenization.

Protect sensitive data with pre-defined masks without changing the ETL pipeline's workflows or access.

Observability for ETL Pipeline

Comprehensive data observability for ETL pipelines, ensuring real-time monitoring and detailed insights into data flows.

Identify and resolve issues, maintaining the reliability and integrity of data processes.

Drop-in Replacement

Seamlessly integrates with your existing ETL pipeline workflow, requiring no changes to workflow and serving as a drop-in replacement.

Just update the connection string to implement any level of data protection policy

Enterprise Grade

Data protection on ETL pipelines

Agentless Architecture

Zero Network Reconfiguration

Deploy in the Cloud or On-Prem

Protected Data Access Gateway for Your Organization

SOC2 Type II