Apache Cassandra

Apache Cassandra is a free, open-source project and a second-generation distributed NoSQL database and is considered to be the best choice for high availability and scalability databases, particularly when dealing with large amounts of data. Cassandra supports replication across multiple datacenters, while also making the write and read processes highly scalable by offering tunable consistency. This Apache Cassandra training course will provide you with an overview of the fundamentals of Big Data and NoSQL databases, an understanding of Cassandra and its features, architecture and data model, its role in the Hadoop Big Data ecosystem, and show you how to install, configure and monitor Cassandra.

The large volume and variety of data that today's businesses process require the need for a highly available, low latency database. Apache Cassandra provides this solution by permitting high-speed reads and writes across a replicated, distributed system. This Apache Cassandra training course provides data modeling experience to take advantage of the linearly scalable peer-to-peer design of Cassandra.

Delegates  will learn how to

·       Architect Cassandra databases and implement commonly used design patterns

·       Model data in Cassandra based on query patterns

·       Access Cassandra databases using CQL and Java

·       Create a balance between read/write speed and data consistency

·       Integrate Cassandra with Hadoop, Pig, and Hive



Professionals aspiring for a career in NoSQL databases and Cassandra

·       Analytics professionals

·       Research professionals

·       IT developers

·       Testers

·       Project managers


·       Knowledge of databases and SQL

·       Java programming

NoSQL Overview

Justifying non-relational data stores

Listing the categories of NoSQL Data Stores

Exploring Cassandra

Defining column family data stores

Surveying Cassandra

Dissecting the basic Cassandra architecture

Querying Cassandra

Defining Cassandra Query Language, CQL

Enumerating CQL data types

Manipulating data from the cqlsh interface

 Leveraging Cassandra structures and types

Drawing comparisons with the relational model

Organizing data with keyspaces, tables and columns

Creating collections and counters

Modeling data based on queries

Designing tables around access patterns

Clustering with compound primary keys

Improving data distribution with composite partition Keys

 Detailing tunable consistency

Identifying consistency levels

Selecting appropriate read and write consistency levels

Distinguishing consistency repair features

Balancing consistency and performance

Relating replication factor and consistency

Trading consistency for availability

Achieving linearizable consistency with Compare-And-Set

 Working with Cassandra collection types

Grouping elements in sets

Ordering elements in lists

Expressing relationships with maps

Nesting collections

Storing data for easy retrieval

Mapping data to tuples and user defined types

Investigating the frozen keyword

Applying the Valueless Columns Pattern

Strategic implementation of clustering columns

Controlling data life span

Expiring temporal data with time-to-live

Reviewing how tombstones achieve distributed deletes

Executing DELETEs and UPDATEs in the future

Constructing materialized views and time series

Modeling time series data

Enhancing queries with materialized views

Materialized views maintained in the application

Driving analytics from materialized views

Managing triggers

Creating triggers by implementing ITrigger

Attaching triggers to tables

Supporting materialized views with triggers

Querying Cassandra data with the Datastax Java Driver

Connecting to a Cassandra cluster

Running CQL through the Java Driver

Batching prepared statements

Paginating large queries

Persisting Java Objects with Kundera

Defining the Java Persistence Architecture, JPA

Configuring Kundera to work with Cassandra

Generating schemas automatically

Managing JPA transactions in Kundera

Leveraging built-in Cassandra connectors

Loading data into Hadoop MapReduce with the Cassandra InputFormat

Utilizing the Cassandra Loader to create Pig relations

Converting a Cassandra table to a Hive table with the Casssandra serializer/deserializer (SerDe)

Program Details
Duration 3 Days
Capacity Max 12 Persons
Training Type Classroom / Virtual Classroom

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