TL;DR: The Ingestion Safety Net * The Problem: Messy data gets into your system, causing errors, bad reports, and distrust. * The Solution: A structured, repeatable checklist for every new data source. * Key Areas: Define the Source, understand the Data, build Quality Checks, and ensure Governance. * The Big Win: Consistent, high-quality

TL;DR: The Reliability Roadmap * The Problem: Bad data in = Bad decisions out. Even with great security, if your data is duplicate, null, or outdated, it’s a liability. * The Solution: An integrated Data Quality (DQ) framework. * The Strategy: * Profiling: Understanding the "health" of your data before you

TL;DR: The Security Super-Stack * The Challenge: Protecting sensitive data at scale (like PII or GDPR-relevant info) is nearly impossible using manual, static rules. * The Solution: Merging Attribute-Based Access Control (ABAC) with Apache Atlas Tag-Based Policies. * How it Works: Atlas tags the data (e.g., SENSITIVE), while LDAP attributes define

TL;DR: The Power of ABAC * The Problem: Role-Based Access Control (RBAC) leads to "Role Explosion." You end up creating hundreds of roles (e.g., Finance-Manager-NY, Finance-Analyst-LDN) just to handle basic permissions. * The Solution: ABAC (Attribute-Based Access Control). Instead of creating a new role, you tell Ranger: "

TL;DR: The Ranger Essentials * The Problem: Standard "folder-level" security isn't enough for complex data—you need to control specific rows and columns. * The Solution: Apache Ranger. It’s the centralized security "brain" for the Cloudera Data Platform. * The Big Win: Dynamic Security. Using

Let’s be honest: Managing data at scale is usually a mess. TL;DR: The Cloudera Deep-Dive * The Problem: Data is stuck in "silos"—split between on-prem and cloud, creating a security and management mess. * The Solution: Cloudera Data Platform (CDP). It’s a hybrid "Data Lakehouse&