Building Apache Cassandra Databases Training

Level: Intermediate
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This Cassandra Database training course will teach you to architect and engineer Cassandra databases for competitive advantage, model data in Cassandra based on query patterns and access Cassandra databases using CQL and Java. In addition, you learn how to create a balance between read/write speed and consistency, integrate Cassandra with Hadoop, Pig and Hive and implement commonly used Cassandra design patterns.

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. Attend this hands-on Cassandra Database course and learn to leverage all its features.

Key Features of this Cassandra Database Training

  • After-course instructor coaching benefit
  • Learning Tree end-of-course exam included
  • After-course computing sandbox included

You 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

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Cassandra Database Course Information

  • Recommended Experience

    • Knowledge of databases and SQL
    • Java programming

Cassandra Database Course Outline

  • Introduction to Apache Cassandra

    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
  • Representing Data in the Cassandra Data Model

    Leveraging Cassandra structures and types

    • Drawing comparisons with the relational model
    • Organising data with keyspaces, tables and columns
    • Creating collections and counters

    Modelling data based on queries

    • Designing tables around access patterns
    • Clustering with compound primary keys
    • Improving data distribution with composite partition Keys
  • Configuring Data Consistency

    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
  • Leveraging Cassandra Idioms and Programming Patterns

    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 materialised views and time series

    • Modelling time series data
    • Enhancing queries with materialised views
    • Materialised views maintained in the application
    • Driving analytics from materialised views

    Managing triggers

    • Creating triggers by implementing ITrigger
    • Attaching triggers to tables
    • Supporting materialised views with triggers
  • Accessing Cassandra Programmatically

    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
  • Integrating Cassandra with Analytical Frameworks

    Leveraging built-in Cassandra connectors

    • Loading data into Hadoop MapReduce with the Cassandra InputFormat
    • Utilising the Cassandra Loader to create Pig relations
    • Converting a Cassandra table to a Hive table with the Casssandra serializer/deserializer (SerDe)

Cassandra Database Training FAQs

  • What is Cassandra?

    Apache Cassandra provides a solution to the large volume of variety of data by permitting high-speed reads and writes across a replicated, distributed system.

  • Where does Cassandra fit in my data science training?

    A data science algorithm will ingest data from an appropriate storage technology like a relational database, Cassandra, MongoDB, Hadoop distributed file system into R or Python for data wrangling and model building. If the amount of data is large execution is performed in parallel using Spark. Spark could be deployed on Cassandra itself or on Hadoop’s HDFS file system or other file systems. The results are often visualised by the end user on dashboards.

  • Can I learn to build a Cassandra database online?

    Yes! We know your busy work schedule may prevent you from getting to one of our classrooms which is why we offer convenient online training to meet your needs wherever you want, including online training.

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