From 0 to 1: The Cassandra Distributed Database

A real world project: A Miniature Catalog Management System using the Cassandra Java driver

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Intermediate

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6 Hours

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A real world project: A Miniature Catalog Management System using the Cassandra Java driver

Course Information

About this course:

Has your data gotten huge, unwieldy and hard to manage with a traditional database? Is your data unstructured with an expanding list of attributes? Do you want to ensure your data is always available even with server crashes? Look beyond Hadoop - the Cassandra distributed database is the solution to your problems. This course helps your set up a cluster with multiple nodes to distribute data across machines. Cassandra is a columnar store. There are no empty cells or space wasted when you store data with variable and expanding attributes. Cassandra uses partitioning and replication to ensure that your data is available even when nodes in a cluster go down.

The average salary for Database Engineer is $86,509 per year.

Course Objective:

After completing this course, students will have a working understanding of:

  • Introduction: Cassandra as a distributed, decentralized, columnar store
  • The Cassandra Cluster Manager
  • The Cassandra Data Model
  • Shell Commands
  • Keys And Indexes: Primary Keys, Partition Keys, Clustering Key, Secondary Indexe
  • Storage Systems

Audience: 

This course is intended for:

  • Engineers and analysts who understand traditional, relational databases and want to move to big data storage systems
  • Students who are just starting out understanding databases and have no prior experience with one

Prerequisites:

  • The basics of SQL and traditional relational databases
  • The basics of Java in order to use the Cassandra Java library

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Subjects IT Ops & Management
Lab Access No
Technology Cassandra
Learning Style Self-Paced Learning
Learning Type Course
Difficulty Intermediate
Course Duration 6 Hours
Language English
VPA Discount VPA Discount

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Tom Robertson
(Data Science Enthusiast)

Tom is an innovator first, and then a Data Scientist & Software Architect. He has integrated expertise in business, product, technology and management. Tom has been involved in creating category defining new products in AI and big data for different industries, which generated more than hundred million revenue cumulatively, and served more than 10 million users.
As a Data Scientist and Software Architect Tom has extensive experience in data science, engineering, architecture and software development. To date Tom has accumulated over a decade of experience in R, Python & Linux Shell programming.

Tom has expertise on Python, SQL, and Spark. He has worked on several libraries including but not limited to Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn, SciPy, NLTK, Keras, and Tensorflow.

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