Hadoop Security Architecture with Kerberos and Ranger

Complete Guide for Building Secure Big Data Platform

  • A unified, secure Big Data platform performs Data Integration and Migration of the data.
  • Execute, create and maintain different scripts to integrate and migrate the data from various sources to Hive.
  • Secure Mode Apache Hadoop, Apache Spark operations and authenticated team members access clusters with different access level and permissions.
  • Optimization of analytical queries performed on Hive using Apache Spark.
  • Infrastructure Automation of Apache Hadoop and Apache Spark Cluster.

Top Challenges for Big Data Security

Infrastructure Security

  • Secure Distributed Processing of Data
  • Security Best Actions for Non-Relational Databases

Data Privacy

  • Data Analysis through Data Mining Preserving Data Privacy
  • Cryptographic Solutions for Data Security
  • Granular Access Control

Data Management and Integrity

  • Secure Data Storage and Transaction Logs
  • Granular Audits
  • Data Provenance

Reactive Security

  • End-to-End Filtering & Validation
  • Supervising the Security Level in Real-Time

Solution Offerings for Data Security

  • Unified Big Data platform to integrate different sources and Data Migration tasks using a single dashboard.
  • Enable security for the Apache Hadoop and Apache Spark clusters with auto deployment process and optimal query execution time of analytical queries and Machine Learning algorithms.
  • Infrastructure Automation using Ansible
  • Enable Security using Apache Knox and Apache Ranger
  • Create Playbooks For Auto-Deployment
  • Enable Security and User Authentication using Apache Kerberos
  • Data Migration To MySQL and Apache Hadoop Cluster
  • Optimization of Spark Execution Engine
  • Enable Monitoring and Performance Metrics Visualization in Graphene
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