NoSQL Architecture Comparison Training

Chapter 1. Introduction to NoSQL Systems

• Gartner's Definition of Big Data
• The V
• 3
• Properties
• Limitations of Relational Databases
• Limitations of Relational Databases (Cont'd)
• What are NoSQL (Not Only SQL) Databases?
• What are NoSQL Databases?
• The Past and Present of the NoSQL World
• NoSQL Database Properties
• NoSQL Benefits
• Use Cases for NoSQL Database Systems
• NoSQL Database Storage Types
• The CAP Theorem
• Mechanisms to Guarantee a Single CAP Property
• NoSQL Systems CAP Triangle
• Limitations of NoSQL Databases
• Mix-and-Match Approach
• Big Data Sharding
• Sharding Example
• Google BigTable
• BigTable-based Applications
• BigTable Design
• Barriers to Adoption
• Dismantling Barriers to Adoption
• Industry trends
• NoSQL Technology Adoption Action Plan
• Quiz
• Quiz Answers
• Summary

Chapter 2. Apache HBase

• What is HBase?
• HBase Design
• HBase Master (HMaster)
• Sparse Data Sets
• Regions and Region Servers
• HBase Features
• HBase High Availability
• The Write-Ahead Log (WAL) and MemStore
• HBase vs RDBS
• HBase vs RDBS (Cont'd)
• Interfacing with HBase
• HBase Thrift and REST Gateway
• HBase Table Design
• Column Families
• A Cell's Value Versioning
• Timestamps
• Accessing Cells
• HBase Table Design Digest
• The Conceptual View of an HBase Table
• HBase Compaction
• Loading Data in HBase
• Column Families Notes
• Cardinality of Column Families
• Hotspotting
• Rowkey Design Notes
• Security
• HBase Shell
• HBase Shell Command Groups
• Creating and Populating a Table Using HBase Shell
• Getting a Cell's Value
• Counting Rows in an HBase Table
• HBase Java Client
• HBase Scanners
• The Scan Class
• The KeyValue Class
• The Result Class
• Getting Versions of Cell Values Example
• The Cell Interface
• HBase Java Client Example
• Scanning the Table Rows
• Dropping a Table
• The Bytes Utility Class
• Table Schema Main Rules to Follow
• Good Use Cases for HBase
• Not Good Use Cases for HBase
• Business Continuity Caveats
• Summary

Chapter 3. Introduction to MongoDB

• MongoDB
• Main Features
• MongoDB's Logo
• Positioning of MongoDB
• The CAP Placement
• MongoDB Clients
• MongoDB Nexus Architecture
• Blending the Best of Both Worlds
• What Makes MongoDB Fast?
• Pluggable Storage Engines
• The BSON Data Format
• BSON Caveats
• MongoDB Terminology
• MongoDB Data Model
• MongoDB Data Model (Cont'd)
• The _id Primary Key Filed Considerations
• Indexes
• (Traditional) Data Modeling in RDBMS
• Data Modeling in MongoDB
• An Example of a Data Model in MongoDB
• MongoDB Data Modeling
• A Sample JSON Document Matching the Schema
• To Normalize or Denormalize? Is that a Question?
• MongoDB Query Language (QL)
• The
• find()
• Method
• The limit()
• Method
• A MongoDB QL Example
• Query Syntax is Driver-Specific!
• More Client Code Examples
• MongoDB Query to SQL Select Comparison
• Data Inserts
• Data Lifecycle Management
• Data Lifecycle Management: TTL
• Data Lifecycle Management: Capped Collections
• Data Sharding
• Data Replication
• GridFS
• MongoDB Security
• Authentication
• Data and Network Encryption
• MongoDB Limitations
• MongoDB Use Cases
• Summary

Chapter 4. Apache Cassandra

• What is Apache Cassandra?
• Main Features
• Peer-to-Peer (No Master)
• Wide Column Store NoSQL Databases
• Cassandra Model vs Relational Model
• Column Families
• Columns
• Simplified Data Model
• Data Model
• The Cap Placement
• CQL
• CQL Simple Examples
• The Update Statement
• Update Caveats
• Update Statement with TTL and TIMESTAMP Examples
• Collections
• Example of Using a Set Collection
• Using the List Collection
• Data Replication
• Visualizing Data Replication
• The Write Path
• Sequential Data Storage Engine
• Java Client Code Example
• Data Distribution
• Native Aggregate Functions
• Creating UDFs
• HBase vs Apache Cassandra
• Cassandra vs MongoDB
• Security
• WAN-Wide High Availability
• Summary

Chapter 5. Introduction to Hadoop

• The Client – Server Processing Pattern
• Apache Hadoop
• Apache Hadoop Logo
• Typical Hadoop Applications
• Hadoop Clusters
• Hadoop Distributions
• Hadoop's Main Components
• Hadoop Distributed File System (HDFS)
• HDFS Considerations
• Data Blocks
• HDFS NameNode Directory Diagram
• HDFS Balancing
• Accessing HDFS
• Examples of HDFS Commands
• Other Supported File Systems
• YARN
• Hadoop-based Systems for Data Analysis
• MapReduce
• Similarity with SQL Aggregation Operations
• MapReduce Word Count Example
• Distributed Computing Economics
• Discussion: Divide and Conquer
• Apache Pig
• Pig Latin
• Running Pig
• Pig Latin Script Example
• What is Hive?
• Hive's Value Proposition
• Who uses Hive?
• What Hive Does Not Have
• HiveQL
• Working with Hive Tables
• Summary

Chapter 6. Introduction to Functional Programming

• What is Functional Programming (FP)?
• Terminology: Higher-Order Functions
• Terminology: Lambda vs Closure
• A Short List of Languages that Support FP
• FP with Java
• FP With JavaScript
• Imperative Programming in JavaScript
• The JavaScript map (FP) Example
• The JavaScript reduce (FP) Example
• Using reduce to Flatten an Array of Arrays (FP) Example
• The JavaScript filter (FP) Example
• Common High-Order Functions in Python
• Common High-Order Functions in Scala
• Elements of FP in R
• Summary

Chapter 7. Introduction to Apache Spark

• What is Apache Spark
• A Short History of Spark
• Where to Get Spark?
• The Spark Platform
• Spark Logo
• Common Spark Use Cases
• Languages Supported by Spark
• Running Spark on a Cluster
• The Driver Process
• Spark Applications
• Spark Shell
• The spark-submit Tool
• The spark-submit Tool Configuration
• The Executor and Worker Processes
• The Spark Application Architecture
• Interfaces with Data Storage Systems
• Limitations of Hadoop's MapReduce
• Spark vs MapReduce
• Spark as an Alternative to Apache Tez
• The Resilient Distributed Dataset (RDD)
• Spark Streaming (Micro-batching)
• Spark SQL
• Example of Spark SQL
• Spark Machine Learning Library
• GraphX
• Spark vs R
• Summary

Chapter 8. The Spark Shell

• The Spark Shell
• The Spark Shell UI
• Spark Shell Options
• Getting Help
• The Spark Context (sc) and SQL Context (sqlContext)
• The Shell Spark Context
• Loading Files
• Saving Files
• Basic Spark ETL Operations
• Summary

Chapter 9. Spark RDDs

• The Resilient Distributed Dataset (RDD)
• Ways to Create an RDD
• Custom RDDs
• Supported Data Types
• RDD Operations
• RDDs are Immutable
• Spark Actions
• RDD Transformations
• Other RDD Operations
• Chaining RDD Operations
• RDD Lineage
• The Big Picture
• What May Go Wrong
• Checkpointing RDDs
• Local Checkpointing
• Parallelized Collections
• More on parallelize() Method
• The Pair RDD
• Where do I use Pair RDDs?
• Example of Creating a Pair RDD with Map
• Example of Creating a Pair RDD with keyBy
• Miscellaneous Pair RDD Operations
• RDD Caching
• RDD Persistence
• The Tachyon Storage
• Summary