Apache Cassandra course training from Yuva Sakthi Academy helps the student to validate their knowledge and enhance career opportunities. Reputed companies most require people with Apache Cassandra skills. A report states that they are getting a 31% higher salary in the field of database technology. Apache Cassandra and training course from Yuva Shakthi Academy make the student prepare for Apache Cassandra 3.x developer associate exams.

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Apache Cassandra Training Course

Apache Cassandra’s training and course will develop the expertise in working with Big Data Hadoop Framework’s high-volume Apache Cassandra database management system. The Apache Cassandra training will make the students gain knowledge and skills on Apache Cassandra features, data model, concepts, architecture, how to monitor, configure, and install open-source database with its integration with Kafka, Spark, Hadoop, and other Apache frameworks through hands-on approaches, real-time training, and case studies. Apache Cassandra’s training course from Yuva Sakthi Academy is best suitable for analytics professionals and software developers who wish to make a great career in the Big Data domain.

Yuva Sakthi Academy offer an Apache Cassandra course completion certificate once the students complete the assignments and projects of the Apache Cassandra training course. The trainers of Yuva Sakthi Academy are highly skilled experts who have experience and certified in different frameworks and industries. The instructors of Yuva Sakthi Academy are professionals who are passionate about sharing their skills, expertise, and knowledge with the students to enrich their careers. According to the report of Ziprecruiter.com, the average base salary the Apache Cassandra developer receives per annum is about $121,854.

About Apache Cassandra Course Training:

The course materials of Apache Cassandra course training are designed by industry professionals who stay updated on the present technologies and trends occurring in Apache Cassandra and relevant fields. Yuva Sakthi Academy Apache Cassandra training course helps students to gain in-detailed knowledge of Fault tolerance, scalability, high availability, fast processing, and other NoSQL database features, ability to perform different operations and ingest data, gain exposure to different real-life projects and assignments, experience with multi-node as well as single cluster set up, and various node operations with the help of node tool, knowledge on different backup and security features offered by Apache Cassandra, and more.

The students will know to install Apache Cassandra single node cluster, design, and model Apache Cassandra applications, perform Apache Cassandra administrator operations, implement recovery and backup strategies, host Apache Cassandra on the cloud, and more. Yuva Sakthi Academy offer job support to every student who has completed their Apache Cassandra and training successfully, along with projects and assignments. Yuva Sakthi Academy has tie-up with more than 200 reputed and top-rated IT corporations like Cognizant, TCS, HCL, Cisco, Ericsson, Sony, and more. The placement assistance team helps the students in preparing the best industry updated resumes, conduct mock interviews, schedule interviews, providing training classes on how to present themselves in front of the interviewer, and more to increase the confidence level and boost the knowledge and skills of Apache Cassandra to the students.

Upcoming Training Batches

Yuva Sakthi Academy provides flexible timings to all our students. Here is the Apache Cassandra Training Course Schedule in our branches. If this schedule doesn’t match please let us know. We will try to arrange appropriate timings based on your flexible timings.

Time Days Batch Type Duration (Per Session)
8:00AM - 12:00PM Mon - Sat Weekdays Batch 4Hr - 5:30Hrs
12:00PM - 5:00PM Mon - Sat Weekdays Batch 4Hr - 5:30Hrs
5:00PM - 9:00PM Mon - Sat Weekdays Batch 4Hr - 5:30Hrs

Syllabus of Apache Cassandra Training

Introduction to Cassandra

Cassandra Architecture

Cassandra Installation and Configuration

  • Course Map
  • Objectives
  • Cassandra Versions
  • Steps to Install and Configure Cassandra on Ubuntu System
  • Operating System Selection
  • Machine Selection
  • Preparing for Installation
  • Setup Repository
  • Install CassandraCheck the Installation
  • Configuring Cassandra
  • Configuration for a Single-Node Cluster
  • Configuration for a Multi-Node and Multi-Datacenter Clusters
  • Setup Property File
  • Configuration for a Production Cluster
  • Setup Gossiping Property File
  • Starting Cassandra Services
  • Connecting to Cassandra
  • Installing on CentOS
  • Demo-Installing and Configuring Cassandra on Ubuntu

Creation of Sample Application

  • Database Design
  • Sample Application RDBMS Design
  • Sample Application Cassandra Design
  • Application Code
  • Creating Database
  • Loading Schema
  • Data Structures
  • Setting Connections
  • Population of database
  • Application Features

Cassandra Data Model

  • Advance Modelling
  • Rules of Cassandra data modelling
  • increasing data writes
  • duplication
  • reducing data reads
  • modelling data around queries
  • creating table for data queries

CQL

  • Data Definition language(DDL) Statements
  • Data Manipulation Language (DML)
  • User permission
  • Create and modify Users
  • Capture CQL output to a file
  • Import and export data
  • CQL scripts from within CQL
  • CQL Scripts from the command prompt

Trainer Profile of Apache Cassandra Training Course

Our Trainers provide complete freedom to the students, to explore the subject and learn based on real-time examples. Our trainers help the candidates in completing their projects and even prepare them for interview questions and answers. Candidates are free to ask any questions at any time.

  • Trained more than 2000+ students in a year.
  • Strong Theoretical & Practical Knowledge.
  • Certified Professionals with High Grade.
  • Expert level Subject Knowledge and fully up-to-date on real-world industry applications.
  • Trainers have Experienced on multiple real-time projects in their Industries.
  • Our Trainers are working in multinational companies such as CTS, TCS, HCL Technologies, ZOHO, Birlasoft, IBM, Microsoft, HP, Scope, Philips Technologies etc

Apache Cassandra Training Exams

Yuva Sakthi Academy is Accredited by all major Global Companies around the world. We provide after completion of the theoretical and practical sessions to fresher’s as well as corporate trainees.

Our at Yuva Sakthi Academy is accredited worldwide. It increases the value of your resume and you can attain leading job posts with the help of this in leading MNC’s of the world. The is only provided after successful completion of our training and practical based projects.

Key Features of Our Training Institute

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One on One Teaching

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Flexible Timing

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Fully Practical Oriented Classes

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Class Room Training

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Online Training

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Corporate Training

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100 % Placement

Projects in Apache Cassandra Training Course

Sales Pipeline Dashboard

Build a dashboard to get a clearer view of your sales pipeline and know where your leads are coming from, so that you can double down on your efforts there to meet your targets.

Sales Growth Dashboard

Build a dashboard to measure your sales team’s performance and how much revenue can be raised within a specific time frame.

Healthcare Data Dashboard

The Apache Cassandra Training Healthcare Data dashboard for hospital managers to manage and identify patients’ risk from one screen.

Training Courses Reviews

I highly recommend the computer training institute for anyone who wants to improve their computer skills. The instructors are knowledgeable and patient, and they create a comfortable and supportive learning environment. The curriculum is well-structured and covers a range of topics, from basic computer operations to advanced programming languages.

T

TOM DINESH

*Right place to learn new technologies *Self motivated staffs ... *This institution is a good start for emerging youngster who has a passion in their life I have trained for core Java. It was very useful to learn java from basic level. *The trainers are knowledgeable and real time worked employees. I like this institutions be hope with us. You are really reached your goals....

H

Harish Arjunan

One of the best academy to easy learn tally prime from kalpana mam one to one teaching is very excellent ..,coaching is very good and motivational person are here ...great service and excellent teach and friendly staff, good environment and comfortable to learn easily Thank you for wonderfull guide mam. ....

P

Priya Mohan

Frequently Asked Questions

What is Apache Cassandra?

Apache Cassandra is a distributed NoSQL database management system designed for handling large amounts of data across many commodity servers, providing high availability and fault tolerance. It offers linear scalability and ensures continuous availability by employing a masterless architecture with no single point of failure.

Cassandra is known for its decentralized design, peer-to-peer architecture, and eventual consistency model, making it suitable for applications that require fast writes, high availability, and linear scalability, such as IoT (Internet of Things), real-time analytics, and recommendation engines.

What are the key features of Apache Cassandra?
  • Distributed Architecture: Data is distributed across multiple nodes, ensuring scalability and fault tolerance.
  • High Availability: No single point of failure, with data replicated across nodes for redundancy.
  • Linear Scalability: Scales linearly by adding more nodes to the cluster, supporting large-scale data sets.
  • Schema-free: Flexible data model with support for dynamic schema changes and wide column support.
  • Eventual Consistency: Provides eventual consistency by default, with tunable consistency levels for reads and writes.
  • Rich Query Language: CQL (Cassandra Query Language) similar to SQL, designed for easy data access and manipulation.
  • Transactional Support: Supports lightweight transactions and batch operations for data integrity.
  • Advanced Replication: Multi-datacenter replication and built-in repair mechanisms for data durability.
  • Tunable CAP Theorem: Allows configuring Consistency, Availability, and Partition Tolerance based on application requirements.
  • Community Support: Developed and maintained by the Apache Software Foundation with a large community and active development.
How does Apache Cassandra ensure data consistency?

Apache Cassandra employs a tunable consistency model to ensure data consistency across distributed nodes:

  • Eventual Consistency: By default, Cassandra provides eventual consistency, allowing updates to propagate to all nodes eventually.
  • Consistency Levels: Cassandra allows setting consistency levels for read and write operations, such as ONE, QUORUM, ALL, and LOCAL_QUORUM, to balance consistency, availability, and partition tolerance.
  • Lightweight Transactions: Supports lightweight transactions (COMPARE AND SET operations) to maintain data integrity across distributed nodes.
  • Batch Operations: Allows batching multiple data modifications in a single atomic operation to ensure transactional consistency.
  • Hinted Handoff: Provides hinted handoff for temporarily unavailable nodes, ensuring data consistency when nodes recover.
  • Read Repair: Automatically repairs inconsistencies during read operations by comparing data across replicas and resolving differences.
  • Write Path: Optimizes write operations using a log-structured storage engine and commit log to ensure durability and consistency.
What are the advantages of using Apache Cassandra?
  • Scalability: Scales linearly by adding nodes to the cluster, supporting large-scale data storage and processing.
  • High Availability: No single point of failure with data replicated across nodes, ensuring continuous availability.
  • Performance: Offers high write throughput and low latency read operations, suitable for real-time applications.
  • Flexible Data Model: Schema-free design with support for wide columns and dynamic schema changes.
  • Fault Tolerance: Data is replicated across multiple nodes, ensuring data durability and fault tolerance.
  • Multi-Datacenter Replication: Supports multi-datacenter deployments for geographic distribution and disaster recovery.
  • Cost-Effective: Runs on commodity hardware, reducing infrastructure costs compared to traditional databases.
  • Community Support: Developed and maintained by the Apache Software Foundation with active community support and contributions.
  • Security: Provides authentication, authorization, and encryption features to secure data and cluster communication.
  • Operational Simplicity: Easy to manage with self-healing mechanisms, automatic repair, and scalable architecture.
How does Apache Cassandra handle data distribution and replication?

Apache Cassandra uses a decentralized and distributed architecture to handle data distribution and replication:

  • Partitioning: Divides data into partitions or shards using a consistent hashing algorithm (Murmur3) to distribute data evenly across nodes.
  • Replication: Replicates data across multiple nodes (replicas) within a Cassandra cluster to ensure high availability and fault tolerance.
  • Replication Factor: Defines the number of replicas (copies) for each data partition, with configurable replication factors for redundancy.
  • Snitch: Uses a pluggable snitch mechanism (e.g., SimpleSnitch, GossipingPropertyFileSnitch) to determine network topology and data center awareness for replicas.
  • Hinted Handoff: Handles temporary node failures by storing hints about missed writes and delivering them once the node recovers.
  • Consistency Levels: Allows configuring consistency levels (e.g., ONE, QUORUM, ALL) for read and write operations to balance data consistency, availability, and partition tolerance.
  • Multi-Datacenter Replication: Supports multi-datacenter deployments with configurable data replication strategies (e.g., NetworkTopologyStrategy) for geographic distribution and disaster recovery.
  • Token Ring: Uses token-based partitioning to assign data partitions to nodes based on token ranges, ensuring efficient data distribution and management.
  • Dynamic Ring Membership: Supports dynamic addition and removal of nodes to handle scaling, maintenance, and cluster rebalancing without downtime.
  • Repair Mechanisms: Includes built-in repair mechanisms and anti-entropy processes to maintain data consistency and resolve inconsistencies across replicas.
What are the recommended use cases for Apache Cassandra?

Apache Cassandra is suitable for various use cases that require scalability, high availability, and fault tolerance:

  • Real-Time Analytics: Cassandra supports high-speed writes and fast read operations, making it ideal for real-time analytics and reporting applications.
  • Internet of Things (IoT): With its ability to handle massive amounts of sensor data and time-series data, Cassandra is well-suited for IoT platforms and applications.
  • Recommendation Engines: Cassandra's linear scalability and high throughput make it suitable for building recommendation engines that require processing large datasets.
  • Online Retail: E-commerce platforms benefit from Cassandra's ability to handle high transaction volumes, product catalogs, and customer data with low latency.
  • Social Media: Social networks and platforms leverage Cassandra's distributed architecture for storing user profiles, activity logs, and social graphs.
  • Content Management: CMS applications use Cassandra for storing and serving dynamic content, managing user sessions, and handling high traffic loads.
  • Financial Services: Banking and finance applications rely on Cassandra for storing transactional data, customer profiles, and fraud detection systems.
  • Gaming: Online gaming platforms benefit from Cassandra's ability to manage player data, game states, leaderboards, and in-game analytics.
  • Healthcare: Healthcare systems use Cassandra for managing electronic health records (EHRs), patient data, medical imaging, and health monitoring applications.
  • Ad Tech: Advertising technology platforms utilize Cassandra for real-time bidding, ad serving, campaign management, and user targeting based on behavioral data.
How does Apache Cassandra ensure data durability and fault tolerance?

Apache Cassandra ensures data durability and fault tolerance through several mechanisms:

  • Replication: Data is replicated across multiple nodes (replicas) within a Cassandra cluster to ensure redundancy and fault tolerance. Replication factor determines the number of copies maintained for each data partition.
  • Hinted Handoff: Handles temporary node failures by storing hints about missed writes and delivering them once the node recovers.
  • Write Path: Uses a commit log and memtable for fast write operations. Data is first written to the commit log for durability and then to a memtable for faster access. Periodically, memtables are flushed to SSTables (Sorted String Tables) on disk for persistent storage.
  • Read Repair: Automatically repairs data inconsistencies during read operations by comparing data across replicas and resolving differences. This ensures data consistency and durability.
  • Anti-Entropy Repair: Performs periodic repairs to reconcile data inconsistencies and ensure uniformity across replicas. Anti-entropy processes compare data between replicas and repair any inconsistencies.
  • Built-in Compaction: Cleans up obsolete data and optimizes storage by merging SSTables and removing tombstones (deleted data markers). Compaction processes ensure efficient storage utilization and prevent data fragmentation.
  • Snapshotting: Takes snapshots of data periodically for backup and disaster recovery purposes. Snapshots capture the state of the database at a specific point in time and can be used to restore data in case of node failures or data corruption.
  • Multi-Datacenter Replication: Supports replication across multiple datacenters for geographic distribution and disaster recovery. Cassandra's replication strategies (e.g., NetworkTopologyStrategy) allow configuring data placement and replication factors across datacenters.
  • Consistency Levels: Allows configuring consistency levels (e.g., ONE, QUORUM, ALL) for read and write operations to balance data consistency, availability, and partition tolerance based on application requirements.
  • Repair Operations: Administers repair operations to synchronize data across replicas and ensure data durability and fault tolerance in distributed environments.
How does Apache Cassandra handle concurrent read and write operations?

Apache Cassandra uses a distributed and decentralized architecture to handle concurrent read and write operations:

  • Distributed Hashing: Uses consistent hashing (Murmur3) to partition data into shards or partitions across multiple nodes. Each node manages a range of data tokens and determines data placement.
  • Write Path: Writes are first logged to a commit log on disk for durability. Updates are then written to an in-memory structure called memtable for fast write operations.
  • Hinted Handoff: Ensures temporary node failures do not disrupt write operations. Cassandra stores hints about missed writes and delivers them to the appropriate nodes once they recover.
  • Consistency Levels: Allows configuring read and write consistency levels (e.g., ONE, QUORUM, ALL) to balance data consistency, availability, and partition tolerance based on application requirements.
  • Conflict Resolution: Resolves conflicts during concurrent updates by applying the last write wins policy or using application-specific conflict resolution mechanisms.
  • Lightweight Transactions: Supports lightweight transactions (COMPARE AND SET operations) to maintain data integrity and handle concurrent updates across replicas.
  • Anti-Entropy: Performs periodic anti-entropy repairs to synchronize data across replicas and ensure consistency in distributed environments.
  • Read Repair: Automatically repairs data inconsistencies during read operations by comparing data across replicas and resolving differences.
  • Multi-Datacenter Replication: Supports replication across multiple datacenters to ensure geographic distribution and disaster recovery while maintaining data consistency and fault tolerance.
  • Scalable Architecture: Scales linearly by adding nodes to the cluster, allowing Cassandra to handle concurrent read and write operations across distributed environments.
What is the architecture of Apache Cassandra?

Apache Cassandra follows a decentralized, peer-to-peer architecture designed for scalability, high availability, and fault tolerance:

  • Node: Each Cassandra instance is a node that communicates with other nodes in the cluster.
  • Ring: Nodes are organized in a ring topology, with each node responsible for a portion of the data (token range).
  • Data Partitioning: Uses consistent hashing (Murmur3) to partition data into shards or partitions across multiple nodes. Data is distributed evenly based on partition keys.
  • Replication: Replicates data across multiple nodes (replicas) to ensure redundancy and fault tolerance. Replication factor determines the number of copies maintained for each data partition.
  • Commit Log: Writes are first logged to a commit log on disk for durability and then written to an in-memory structure called memtable for fast write operations.
  • Memtable: Stores recent writes in memory for fast access. Periodically, memtables are flushed to Sorted String Tables (SSTables) on disk.
  • Compaction: Cleans up obsolete data and merges SSTables to optimize storage and prevent data fragmentation.
  • Read Path: Reads data from memtable and SSTables on disk. Data is retrieved using partition keys and can be served from multiple replicas based on read consistency levels.
  • Write Path: Handles write operations by logging data to commit logs and storing updates in memtables for fast write access. Updates are later flushed to SSTables for persistence.
  • Snitch: Determines network topology and data center awareness to route requests and maintain cluster communication.
How does Apache Cassandra handle data consistency?

Apache Cassandra ensures data consistency through its tunable consistency model and built-in mechanisms:

  • Eventual Consistency: By default, Cassandra provides eventual consistency, allowing updates to propagate to all nodes eventually.
  • Consistency Levels: Allows configuring consistency levels (e.g., ONE, QUORUM, ALL) for read and write operations to balance consistency, availability, and partition tolerance.
  • Lightweight Transactions: Supports lightweight transactions (COMPARE AND SET operations) to maintain data integrity and handle concurrent updates across replicas.
  • Write Path: Logs writes to a commit log for durability and stores updates in memtables for fast write access. Data is later flushed to SSTables on disk for persistence.
  • Read Repair: Automatically repairs data inconsistencies during read operations by comparing data across replicas and resolving differences.
  • Anti-Entropy: Performs periodic anti-entropy repairs to synchronize data across replicas and ensure consistency in distributed environments.
  • Hinted Handoff: Stores hints about missed writes and delivers them to nodes once they recover, ensuring temporary node failures do not disrupt data consistency.
  • Multi-Datacenter Replication: Supports replication across multiple datacenters to maintain data consistency and fault tolerance while ensuring geographic distribution and disaster recovery.
  • Conflict Resolution: Resolves conflicts during concurrent updates using the last write wins policy or application-specific conflict resolution mechanisms.
  • Scalable Architecture: Scales linearly by adding nodes to the cluster, allowing Cassandra to handle distributed data consistency and availability efficiently.
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