Data Governance and Lineage

Apiro addresses aspects of data storage, governance, and lineage. They enhance security, privacy, flexibility, and overall data management capabilities in a complex data processing environment.

Internal NoSQL Data Store:

  • Flexibility for Unstructured Data: NoSQL data store provides flexibility for managing unstructured or semi-structured data, accommodating diverse data formats and structures.
  • Scalability: NoSQL databases are often designed for horizontal scalability, enabling the platform to handle growing volumes of data efficiently.
  • Schema-less Design: The schema-less design of NoSQL databases allows for agile development and accommodates evolving data requirements.

Metadata Support at the Data Point Level:

  • Granular Metadata Management: Metadata at the data point level enables granular management, providing detailed information about each piece of data.
  • Improved Data Discovery: Detailed metadata enhances data discovery, making it easier for users to understand and locate relevant data points.
  • Impact Analysis: Data point-level metadata supports impact analysis by identifying dependencies and relationships between different data points.

PII Classification:

  • Compliance with Privacy Regulations: PII classification helps ensure compliance with privacy regulations by identifying and safeguarding personally identifiable information.
  • Risk Mitigation: Enables the platform to implement appropriate security measures and access controls to protect sensitive PII data.
  • Data Governance Enhancement: PII classification strengthens overall data governance efforts by highlighting and managing sensitive data elements.
  • Data Masking:

    • Privacy Protection: Data masking protects sensitive information by replacing or obscuring actual data with masked values.
    • Secure Testing Environments: Supports the creation of secure testing environments by masking sensitive data, allowing testing without exposing confidential information.
    • Risk Reduction: Reduces the risk of unauthorized access to sensitive data, enhancing overall data security.
  • Data Encryption at Rest:

    • Confidentiality Assurance: Encryption at rest ensures the confidentiality of stored data by encrypting it on disk or storage media.
    • Regulatory Compliance: Meets regulatory requirements for protecting data, especially in sensitive industries like healthcare and finance.
    • Risk Mitigation: Protects against unauthorized access or data breaches by securing data even when it’s not actively in use.

Synthetic Data Generation:

  • Data Privacy in Testing: Enables the generation of synthetic data for testing purposes, preserving data privacy and compliance.
  • Performance Testing: Synthetic data supports performance testing scenarios with large datasets without exposing real data.
  • Useful for Training Models: Synthetic data can be valuable for training machine learning models when real data is limited or sensitive.

Comprehensive Data Change Log (Data Point Level):

  • Auditability: A comprehensive change log at the data point level provides detailed audit trails, enhancing accountability and compliance.
  • Troubleshooting: Facilitates troubleshooting by tracking changes in data, aiding in the identification and resolution of issues.
  • Historical Analysis: Supports historical analysis by capturing every change, allowing users to analyze the evolution of data over time.


Metadata Processors:

  • Automated Metadata Management: Metadata processors automate the extraction, transformation, and loading of metadata, reducing manual efforts.
  • Consistency: Ensures consistency in metadata across the platform, preventing discrepancies and improving overall data quality.
  • Integration with Governance Policies: Metadata processors can be configured to align with data governance policies, ensuring metadata adherence to defined standards.


Bitemporal Data (Time Machine):

  • Temporal Analysis: Bitemporal data enables temporal analysis, allowing users to analyze data changes over specific time periods.
  • Historical Reconstruction: Facilitates historical reconstruction by preserving and managing historical versions of data.
  • Regulatory Compliance: Supports regulatory compliance by providing a clear timeline of data changes and activities.


Data Deduplication:

  • Storage Optimization: Data deduplication reduces storage redundancy by identifying and eliminating duplicate data.
  • Data Quality Improvement: Enhances data quality by preventing the storage of redundant or inconsistent information.
  • Efficient Resource Utilization: Optimizes resource utilization by minimizing the storage footprint of duplicated data.


Fuzzy Logic:

  • Flexible Matching: Fuzzy logic enables flexible matching of similar or approximate data, accommodating variations or errors.
  • Improved Data Matching: Enhances data matching and linkage, even when exact matches are not available or feasible.
  • Data Quality Enhancement: Supports data quality by allowing for more accurate and comprehensive data matching.


Global Asset ID Generation:

  • Unique Identifiers: Global asset ID generation ensures the creation of unique identifiers for assets, avoiding conflicts and ensuring consistency.
  • Cross-System Integration: Enables cross-system integration by providing a standardized way to reference and identify assets globally.
  • Traceability: Enhances traceability by associating a unique ID with each asset, facilitating tracking and auditing activities.

Apiro’s data platform provides data integration, transformation and distribution features from and to any source or destination and format.


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