What will you learn in Apache Cassandra Certification Training
Understand Cassandra’s architecture, data model, and decentralized peer-to-peer design
Master CQL (Cassandra Query Language) for schema design, queries, and data manipulation
Design and optimize high-availability, fault-tolerant data models with appropriate partition keys and clustering
Configure and manage Cassandra clusters, including replication strategies and tuning
Implement backup, restore, and monitoring using tools like nodetool and metrics
Apply Cassandra best practices for real-time, large-scale applications
Program Overview
Module 1: Introduction to Cassandra & Setup
⏳ 1 week
Topics: NoSQL vs. RDBMS, Cassandra use cases, installation, and cluster setup
Hands-on: Install Cassandra locally or in Docker and create your first keyspace
Module 2: Data Modeling with CQL
⏳ 1 week
Topics: Tables, primary keys, partition/clustering keys, collection types
Hands-on: Create tables and write CQL queries to insert, update, and retrieve data
Module 3: Replication & Consistency
⏳ 1 week
Topics: Replication factor, consistency levels, read/write paths
Hands-on: Configure multi-datacenter replication and test different consistency levels
Module 4: Performance Tuning & Indexing
⏳ 1 week
Topics: Compaction, caching, secondary indexes, materialized views
Hands-on: Benchmark write/read performance and apply tuning parameters
Module 5: Cluster Management & Monitoring
⏳ 1 week
Topics: nodetool commands, JMX metrics, alerting, repair workflows
Hands-on: Monitor cluster health, perform repairs, and simulate node failures
Module 6: Backup, Restore & Security
⏳ 1 week
Topics: Snapshot backups, incremental backups, authentication, and authorization
Hands-on: Take snapshots, restore data, and configure role-based access control
Module 7: Advanced Features & Use Cases
⏳ 1 week
Topics: Lightweight transactions, TTL, time-series patterns, Spark integration
Hands-on: Build a simple time-series application and query with TTL settings
Module 8: Capstone Project
⏳ 1 week
Topics: End-to-end design and deployment of a Cassandra-backed application
Hands-on: Design a high-throughput, multi-region user activity tracker
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Job Outlook
Cassandra expertise is in high demand for roles in data engineering, backend development, and site reliability
Common positions include Cassandra Developer, Data Engineer, and Big Data Architect
Salaries range from $100,000 to $160,000+ based on role and experience
Widely adopted in industries like IoT, finance, and digital services requiring low-latency, scalable storage
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Specification: Apache Cassandra Certification Training Course
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FAQs
- Basic familiarity with Linux commands and a Java environment is recommended.
- Understanding relational databases helps, but prior NoSQL experience is not mandatory.
- Python or Java programming experience will help with Cassandra client integrations.
- Knowing database concepts like indexing, replication, and partitions accelerates learning.
- Hands-on practice during the course will reinforce these fundamentals.
- Cassandra uses a decentralized peer-to-peer architecture for high availability.
- It excels at write-heavy workloads and multi-datacenter replication.
- Data modeling relies heavily on partition and clustering keys, unlike document stores.
- It provides tunable consistency levels for read/write operations.
- Ideal for real-time, large-scale applications like IoT or high-traffic analytics.
- Course covers cluster setup, replication strategies, and monitoring via
nodetool. - Hands-on exercises include performance tuning, compaction, caching, and indexing.
- Guidance on backups, restores, and security (authentication/authorization) is provided.
- Simulated node failures and repair workflows teach fault-tolerant operations.
- Skills gained are applicable to enterprise deployments with high availability requirements.
- Basic integration with Spark is introduced for analytics and ETL tasks.
- Kafka or real-time streaming integrations are not deeply covered.
- Learners can extend their knowledge using additional resources or tutorials.
- Focus is on designing scalable, fault-tolerant data models compatible with big data pipelines.
- Core skills can be applied to other ecosystems after completing the course.
- Dedicate 5–10 hours per week for hands-on labs and module completion.
- Set up a local or Docker-based Cassandra environment for practice outside class.
- Start with simple keyspaces and tables before moving to complex data models.
- Document performance tuning and repair experiments to reinforce understanding.
- Complete the capstone project to showcase practical skills in real-world scenarios.
