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