DataOps School

📌 Introduction & Overview What is Anonymization? Anonymization is the process of transforming personal or sensitive data in a way that prevents the identification of individuals, even indirectly. Unlike pseudonymization (which replaces identifiers with pseudonyms but still allows re-identification with additional data), anonymization removes or masks all identifiable information irreversibly. In DevSecOps—where security is a … Read more

1. Introduction & Overview What is Normalization? Normalization in the context of DevSecOps refers to the process of transforming data, configurations, logs, or system inputs into a standardized and consistent format. This enables better comparison, automation, validation, security analysis, and decision-making across environments and toolchains. It is applied in areas such as: History or Background … Read more

1. Introduction & Overview What is Cleansing? In DevSecOps, cleansing refers to the practice of removing, sanitizing, or redacting sensitive data, metadata, or malicious inputs from systems, codebases, logs, and configurations to reduce security risks and maintain compliance. It ensures that secrets, personally identifiable information (PII), or vulnerabilities are not propagated across the software development … Read more

1. Introduction & Overview What is Aggregation? Aggregation in the context of DevSecOps refers to the systematic collection, unification, normalization, and correlation of data from diverse sources such as logs, metrics, vulnerabilities, code quality scans, audit trails, cloud configurations, and CI/CD pipelines. This consolidated view enhances observability, threat detection, compliance auditing, and overall decision-making. Aggregation … Read more

1. Introduction & Overview What is Enrichment? In the context of DevSecOps, Enrichment refers to the process of augmenting raw security data (logs, alerts, metrics) with contextual information that makes the data actionable and insightful. Enrichment adds intelligence by linking events to: History or Background Traditionally, IT security teams struggled to prioritize security alerts due … Read more

1. Introduction & Overview What is Transformation? In the context of DevSecOps, Transformation refers to the strategic and operational shift in an organization’s culture, processes, and tooling to integrate security as a shared responsibility throughout the entire software delivery lifecycle. It involves moving from traditional siloed development, security, and operations teams to a unified, automated, … Read more

1. Introduction & Overview What is Ingestion? Ingestion refers to the process of collecting, importing, and processing data from various sources into a centralized system for analysis, storage, or monitoring. In the context of DevSecOps, ingestion typically involves the real-time or batch processing of: It acts as the entry point for observability, compliance, and security … Read more

📘 Introduction & Overview What is Agile Data? Agile Data refers to the application of agile methodologies—like iterative development, cross-functional collaboration, and incremental delivery—to data management and data analytics processes. Just as Agile revolutionized software development, Agile Data is transforming how data is collected, governed, analyzed, and secured in fast-paced environments like DevSecOps. History or … Read more

1. Introduction & Overview What is the DataOps Lifecycle? The DataOps Lifecycle refers to the end-to-end process of managing data workflows—from ingestion and transformation to deployment and monitoring—using DevOps principles like automation, collaboration, and continuous improvement. It ensures that data engineering, operations, and security are seamlessly integrated in agile environments. History or Background Why is … Read more

1. Introduction & Overview What is Data Observability? Data Observability is the ability to fully understand the health, lineage, and performance of data across your infrastructure. In a DevSecOps context, it ensures that data pipelines are trustworthy, auditable, and compliant—especially critical when automating deployments, ensuring security, and meeting regulatory requirements. Data observability incorporates: History or … Read more