Automating Data Governance with Apache Ranger and REST API

Enhance Data Security and Compliance

Streamline Data Management Processes

The digital landscape today is awash in data. In the enterprise, beyond traditional sources, data is being generated at an exponential rate from newer sources like wearables, sensors, and IoT devices. That too in volumes and formats previously unimaginable. The traditional, centralized approach to data collection simply can’t keep pace. Moving massive datasets around creates bottlenecks and inefficiencies.

The answer therefore lies in a paradigm shift – consuming data at its source rather than moving it. This approach demands a focus on real-time access and controlled permissions for specific users and processes.

Today’s businesses thrive on insights extracted via data analysis. But with data velocity, volume, and variety at an all-time high, it’s impossible for traditional methods to deliver the speed and agility needed for performing analytics. Businesses need to seamlessly bridge data silos and transform raw data into actionable insights – in near real-time. This in turn, is necessitating a re-evaluation of our data strategies, both from a business and technological perspective. Today, several cutting-edge technologies are revolutionizing how organizations approach data management. These empower real-time data availability, enabling more efficient data consumption, analysis, and decision-making.

Introduction to Apache Ranger and REST API

Setting Up Apache Ranger for Data Governance

Implementing REST API for Automation

Benefits of Automated Data Governance

Overview of Data Governance Challenges

Steps to Configure Apache Ranger

Integrating REST API with Apache Ranger

Case Studies and Success Stories

In this blog, we will explore how technologies address key business and technical challenges. However, before we do that, first let’s articulate the asks from business and technical standpoints for clarity. 

From a business perspective, the goal is to unlock value from real-time data by reducing operational costs like storage and compute. To achieve this technically, we need real-time data collection and processing alongside cost-efficient storage and compute solutions. This focus on real-time insights necessitates finding ways to connect data sources without physically moving them, evaluating the potential efficiencies of decentralized data storage, and implementing strategies to avoid data silos while maximizing cost savings throughout the data management process.

Let’s now look at how some of the modern data management technologies meet the aforementioned goals. 

Data Virtualization:

Data Virtualization focuses on connecting data sources in real-time without physically moving the data. It provides a unified view of data across sources, promotes agility, and optimizes resource utilization. 

• Connecting Data Sources in Real Time:
  Data virtualization platforms like Denodo enable real-time integration and access to data without physically moving it. They provide a unified view of data across sources, promoting agility and efficiency in data utilization. 
• Avoiding Data Silos:
  Data virtualization helps in avoiding data silos by providing a centralized and virtualized layer for accessing and querying data from diverse sources. It fosters data democratization and collaboration across teams. 
• Cost-saving Measures:
  Data virtualization can lead to cost savings by reducing the need for data duplication, data movement, and maintaining multiple data copies. It optimizes resource utilization and supports flexible deployment options. 

Denodo integrates data from disparate sources in real-time, providing a unified view for analysis and decision-making. It enables agile data access without data movement, reducing complexity and improving efficiency in data management. Denodo’s data virtualization empowers organizations to derive actionable insights and drive innovation through data-driven strategies. 

Data Mesh:

Data Mesh emphasizes decentralization of data storage, enhancing efficiency by fostering domain-oriented data teams and breaking down data silos. It encourages collaboration and governance within specific domains. 

• Decentralization of Data Storage:
  Data Mesh is focused on decentralizing data ownership and management, aligning with the goal of evaluating decentralization for efficiency. It promotes domain-oriented data teams responsible for managing and curating data products within their respective domains. 
• Avoiding Data Silos:
  By decentralizing data management and ownership, Data Mesh aims to break down data silos and enable cross-domain data collaboration. It encourages a federated approach to data governance and access. 
• Implementing Cost-saving Measures:
  While Data Mesh is primarily about improving data agility and collaboration, it can indirectly lead to cost savings by streamlining data operations, reducing duplication, and enhancing data quality. 

Starburst data mesh combines the power of Starburst’s distributed SQL query engine with the principles of data mesh architecture. It enables organizations to scale their data infrastructure dynamically, decentralize data ownership, and promote data autonomy across domains for improved data governance and agility. 

Data Fabric:

Data Fabric solutions offer a comprehensive approach to data management, including real-time integration, governance, and accessibility. They support avoiding data silos, improving data quality, and optimizing data workflows for cost savings and efficiency. 

• Connecting Data Sources in Real-Time:
  Data Fabric solutions provide a holistic approach to data management, including real-time data integration, governance, and accessibility. They create a unified and seamless data ecosystem across diverse sources. 
• Avoiding Data Silos:
  Data Fabric emphasizes data governance practices, metadata management, and data lineage tracking, which are essential for avoiding data silos and ensuring data consistency and reliability. 
• Implementing Cost-saving Measures:
  Data Fabric solutions contribute to cost savings by optimizing data workflows, improving data quality, reducing data redundancy, and facilitating efficient data access and usage. 

Stardog Data Fabric is a comprehensive knowledge graph platform that integrates data from diverse sources, providing a unified view for analytics and decision-making. It leverages semantic technologies to connect, query, and analyse structured and unstructured data in real-time, enhancing data interoperability and insights. Stardog Data Fabric supports data virtualization, reasoning, and data governance, empowering organizations to derive value from their data assets efficiently. 

Each of these technologies offers unique strengths, making them ideal for different data management challenges. By carefully evaluating their needs and data landscape, organizations can select the solution that best positions them to unlock the true value of their data assets and gain a significant competitive advantage in today’s data-driven world.