Coursera

Real-Time Big Data Access using HBase: Boosting Performance (Coursera)

Offered by LearnQuest,

In the world of big data, the significant growth in both the sheer volume and variety of data has presented significant challenges. Apache HBase has emerged as a robust and scalable solution. HBase is a powerful, distributed, and scalable NoSQL database designed to handle large amounts of data while maintaining high performance.

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In this introductory course, you will explore the fundamental concepts of HBase and its significance in handling real-time data processing and analysis.
Topics covered in this course include:
-Fundamentals of HBase
-Schema Design in HBase
-HBase Cluster Setup, Monitoring, and Backup
-HBase Querying and Retrieval Basics
-Advanced HBase Querying Techniques
-Batch Operations and Data Manipulation
-Data Modeling for Real-Time Applications
-Deployment Strategies for Real-Time Applications
-Scalability and Availability in Real-Time Applications
This course includes video lectures, video demonstrations, as well as hands-on application in a lab environment. By the end of this course, you will be able to design efficient HBase schemas, set up and optimize HBase clusters, perform data operations, and evaluate the proper application of HBase in real-time scenarios while considering scalability and effective deployment strategies.

Syllabus

Introduction to HBase
In this module, you will first be introduced to your instructor and the course. Then, we will delve into HBase, exploring NoSQL Databases, HDFS, HBase Architecture and components, and HBase clusters. We'll compare HBase with other big data landscapes, and discuss creating effective data modeling and schema design.

HBase Querying and Data Access
In this module, we will discuss importing data to HBase using Sqoop, and explore querying techniques such as Scans, Filters, and Get requests, then practice constructing HBase queries in a lab environment. We will take a look at advanced querying using a Java application, and optimizing HBase performance using caching and scan optimization. Lastly, we will discuss manipulating data using batch operations for improving performance.

HBase in Real-Time Applications
In this final module, we will discuss HBase data modelling for real-time applications, delving into strategies and use cases. We will also explore deployment strategies for real-time applications, starting with best practices and applying it in a lab environment. Lastly, we will take a look at HBase scalability and availability in real-time applications, discussing limitations and techniques for analyzing performance, before practicing what you have learned.

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