CD210 Couchbase NoSQL Data Modeling, Querying, and Tuning Using N1QL

CD210 Couchbase NoSQL Data Modeling, Querying, and Tuning Using N1QL

Summary

A 3-day hands-on lab-intensive course on architecture and design of NoSQL data using N1QL. Instruction and exercises are focused on data modeling, query, storage and access for robust, scalable, high-performance NoSQL applications. Topics include: the N1QL query language (SQL for JSON), data modeling for NoSQL applications, indexes and views, and methods of application performance tuning for principal use cases. See Customer testimonials

Description

Through conceptual presentation, demonstration and extensive hands­-on labs and exercises, this 3-day instructor-­led course guides developers, architects and DBA's on storing and accessing data rapidly and at web scale, in diverse document-database use cases based on JSON, using Couchbase Server, N1QL, and related technologies. All labs rely on queries in N1QL without dependency on any particular application programming language. The course is aimed at developers, architects, analysts, DBAs, data warehouse and business intelligence admins and analysts, and DevOps professionals.

The hands-on labs (usually 60% of class time) get you real-world "flight-time" modeling NoSQL data using best practices for JSON and key/value, creating and exploiting secondary indexes, performing complex aggregations using map-reduce Views, and becoming capable with N1QL as a basis for developing powerful, scalable, and reliable applications using NoSQL data, in any programming language.

Duration

3 Days

Objectives

The course is geared to produce effective NoSQL data modeling, storage, querying, and performance tuning competency in experienced software application developers and other technical professionals who may be either newbies to data-driven apps, or already experienced with SQL and modern databases from prior data application development projects.

Audience

This course is intended for application developers, architects, software engineers, database administrators, data warehouse managers, business intelligence analysts, data engineers, and DevOps professionals with experience in any software development or scripting language, and with or without prior experience using SQL in a relational database (RDBMS) context.

Prerequisites

The course is designed for developers and data architects seeking to use Couchbase for data modeling, storage, and retrieval. No specific programming language expertise is required or assumed. Success in the hands-on labs will be assured for participants who bring prior experience writing software or scripting in ANY modern language, for example, Java, C#/.Net, Node.js, Go, Python, C/C++, Ruby, PHP, etc. Familiarity with basic programming fundamentals and database concepts is assumed, but no prior experience with NoSQL databases (or with SQL datbases) is required. We strongly suggest Participants complete the following Couchbase online courses prior to class: CB030, and CB105, and CB110.

Outline

Partial list of concepts, topics and hands-on labs. Subject to change and update without prior notice. Course includes:

1. DAY 1 - Welcome (4 hands-on labs)
    Welcome to our course
    Who's all here?
    What are the logistics?
    What will this course cover?
    What do we assume you are familiar with?
2. Couchbase Server (1 hands-on lab)
    How do customers use Couchbase?
    What does Couchbase provide?
    Where would Couchbase fit into an enterprise app?
    How are analytical and operational uses different?
    Where did we come from, and where are we going?
    What is the project history?
    What are the main architectural structures?
    How is a node organized?
    What is the high-level architecture?
    What does the Cluster Manager provide?
    What does the Data Manager provide?
    How can services be scaled across a cluster?
    What is multi-dimensional scalability (MDS)?
    How does Couchbase provide these capabilities?
    How is a cluster administered?
    What do you configure in the setup wizard?
    What administrative interfaces are available?
    Once you're installed, where should you look?
    Document browser in web console
    How do you find/edit documents in the console?
    Command-Line Interface (CLI) Tools
    What administrative interfaces are available?
    What command line tools are available?
    How do you bulk load documents into a bucket?
    What desktop tools work with N1QL?
3. Storing Data in Couchbase (4 hands-on labs)
    How does Couchbase organize data?
    What are the main architectural structures?
    How does Couchbase organize data?
    What is JSON?
    Structured vs. unstructured data
    What is JavaScript Object Notation?
    Want to see a few examples?
    Why does Couchbase focus on JSON?
    Why store data as JSON?
    How do NOSQL and relational approaches compare?
    Static vs. flexible data models
    Structured vs. unstructured data
    How do data modeling approaches compare?
    Normalization vs. de-normalization
    How do data modeling approaches compare?
    What's wrong with over-normalization?
    What are key strengths of each approach?
    Are Couchbase buckets like a table? A database?
    How do we access the data?
    How do you get documents from Couchbase?
    Whole-document access
    Subdocument API
    How do we write applications?
    For what languages are SDKs supported?
    Show me some code...
    What are the common APIs?
    How is N1QL used by developers?
    How about community support?
    What is the scope of transactions?
    How may atomicity affect modeling?
    How does optimistic locking with CAS behave?
    Can consistency be maintained?
4. N1QL (2 hands-on labs)
    N1QL is like SQL
    How do you query the data?
    What information comes back with results?
    How do you select documents by key, in N1QL?
    How do you query for a document key?
    How do you alias a bucket?
    How do you filter for a specific value?
    How could you efficiently select a range?
    How do you filter text with wildcards?
    Aggregating and ORDERing
    How do you count documents or attributes?
    How do you sequence results?
    How do you select unique values?
    How do you aggregate values?
    Operators and Functions
    What operators are available?
    What functions are available in N1QL?
    Primary Index
    Indexes: Primary Indexes
    Secondary Indexes
    How do you create a full secondary index?
    How do you utilize a full secondary index?
    How do you partially index the documents?
    How do you utilize a filtered index?
    EXPLAINing the data access plan
    How do you determine if an index is in use?
    Executions Plans & Explain
    Main Operations
    Index Hinting
    Minimize Items Scanned
    System Namespace
    How do you determine indexes via N1QL?
    How do you determine buckets via N1QL?
    Indexes: Secondary Indexes
    Query Workbench
    What desktop tools work with N1QL?
    Query Workbench
    Features of the Query Workbench
    N1QL Editor
    Bucket Analysis
    Results
    JSON Result Format
    Table Result Format
    Tree/List Result Format
Day 2
5. Index Engines
(7 hands-on labs)
    Overview
    Indexing Architecture and Indexers
    MapReduce View Indexer
    Spatial View Indexer
    GSI Indexer
    Distributed Secondary Indexes (DSI = View)
    Indexing - DSI - Scatter/Gather
    Global Secondary Indexes (GSI)
    Query and Index with GSI
    What are Global Secondary Indexes?
    Power of GSI
    Distribution in GSI (Range Partitioning)
    Latency in GSI
    GSI and Scalability
    Load-balancing in GSI
    GSI and Replication
    Which to choose - GSI vs Views
    Index Service - Capacity Management
    Memory-Optimized Global Indexes
    Enabling Memory-Optimized Global Indexes on the Cluster
6. N1QL is NOSQL (7 hands-on labs)
    JSON extensions to SQL, or "SQL for Documents"
    What is JavaScript Object Notation?
    How do you select nested attributes?
    How do you respond to missing data?
    What is a "collection" in a JSON document?
    How do you select for a value in an array?
    How do you select for a value in an object array?
    Array Indexing
    Data Manipulation Language (DML)
    DML Statements
    DELETE
    INSERT
    UPDATE
    INSERT vs. UPSERT
    JOIN
    How do you join a single document?
    How do you join document sets from a key array?
    NEST
    NEST vs. JOIN
    UNNEST Clause
    How do you UNNEST an embedded collection?
    Flexible Commutative JOIN in N1QL
7. Introduction to Modeling (6 hands-on labs)
    What is Data Modeling?
    Logical Data Modeling
    Example: Logical Data Modeling
    Physical Data Modeling
    Data models used in this course
    What data models are used in the course?
    How is Couchmusic1 shaped?
    How is Couchmusic2 shaped?
    How is Couchmusic3 shaped?
8. Query Engine (2 hands-on labs)
    N1QL empowers, beyond other NOSQL engines
    Query Architecture
    How do you get documents from Couchbase?
    Couchbase Server Cluster Architecture
    How does Couchbase provide these capabilities
    Architecture Highlights
    Query Execution
    How is a N1QL query processed?
    Query Execution
    How does the query execute?
    Client to Query Service: REST API
    The Parse Phase
    Query Execution: Plan
    Query Execution: Scan Phase
    Query Execution: Fetch
    Query Execution: Join
    Query Execution: Filter
    Query Execution: Aggregate, Sort, Offset, Limit
    Query Execution: Project
    Intersect Scan
    IntersectScan (Multi-Index Scan)
    IntersectScan
    Index Covering
    Phases of Query Execution - (Without Index Covering)
    N1QL Query Execution Flow: Index Covering
    Phases of Query Execution - (With Index Covering)
    Index Covering
    Querying data with N1QL
    More Index Covering
    Scaling
    Query Execution
    Query Service - Capacity Management
Day 3
9. More Modeling
(1 hands-on lab)
    Data Modeling in a JSON and NOSQL context
    What choices impact JSON document design?
    What factors may influence structural choices?
    Modeling entities in JSON
    What is an "entity"?
    How can entities be described in JSON?
    Is a UserProfile one entity? Or, more than one?
    Is a Country an entity?
    Is an Address an entity?
    Is a Phone an entity?
    Modeling relationships in JSON
    How can relationships be expressed in JSON?
    When might you nest a related entity?
    When might you nest an array of related entities?
    When might you embed a foreign document key?
    Making choices in JSON document design
    How may the root attribute be used?
    Are attributes best expressed as objects or arrays?
    Should array values be simple or complex?
    What options do you have for timestamps?
    What choices can be made about attribute names?
    How does N1QL distinguish empty, null, missing?
    Making choices in JSON key design
    What is a natural key?
    What is a surrogate key?
    Common Patterns
    How may key patterns be useful?
    What is the Lookup Key document pattern?
    Is this traditional, widely-used pattern still needed?
    Can consistency be maintained?
10. Tuning (2 hands-on labs)
    Fundamentals
    Minimize Items Scanned
    Monitoring
    How do you determine indexes in the console?
    Monitoring GSI Indexes
11. Migrating from SQL to N1QL (no labs)
    Motivation for Migration
    Motivation for Migration to Couchbase
    How to Migrate to Couchbase?
    Motivation for Migration to Couchbase
    Data Model
    Data Model: Map RDBMS to Couchbase
    Map Tables to Buckets: Normalized Data
    Map Database(s) to Buckets: Normalized Data
    Map Database(s) to Buckets: Normalized Data
    Map Database(s) to Buckets: Denormalized Data
    Datatypes
    Data Types Mapping
    MISSING in JSON
    Queries
    Operators: Searching in JSON (Non-Existing Fields)
    JOINs
    JOINs: Output of JOIN - NESTed Data
    NEST: Create NESTed JSON
    UNNEST: Remove NESTing From JSON
    USE KEYS: Improving Efficiency
    Functions: Comparing DateTime Fields and Values
    DML
    CRUD Operations: INSERT/UPSERT
    CRUD Operations: UPDATE/DELETE
    What have you learned?
Optional Appendices (reviewed based on available time and interest)
A. More Features (no labs)
    How is Couchbase optimized for mobile development?
    Why is mobile data synchronization critical?
    How does Couchbase solve this problem?
    Full-Text Search
    Full text search
    Full text search - Couchbase CBFT
    Full-Text Search
    How does Couchbase Enterprise Edition secure your data?
    How is administrative data secured in motion?
    How is client data secured in motion?
    How is client data secured at rest?
    How is administrative activity audited?
    What external systems integrate with Couchbase?
    Apache Hadoop
    Apache Spark
    ODBC/JDBC drivers
    Full text search
    Spring developer framework
    Talend platform
    Unity mobile
    Many, many more open source integrations
B. Aggregations With Views (no labs)
    Query Data in Couchbase
    Retrieving Data with Document Key
    Querying Data using View Queries
    Query Consistency with Views
    stale=ok
    stale=update_after
    stale=false
    Querying geographic data with spatial views
    Querying data with N1QL
    Components of N1QL Query Engine
    GSI vs. Views
    Global Secondary Indexes vs. Views
    GSI and Views Differences: Partitioning Model
    GSI and Views Differences: Scaling Model
    GSI and Views Differences: Performance
    GSI and Views Differences: Managed Cache and Storage
    GSI and Views Differences: High Availability
    Indexing And Querying via Views
    Components of N1QL Query Engine
    Indexing and Querying via Views
    Best Practices - Selection, Projection, Aggregation
    Database Design Considerations for Views
    Number of Design Documents per Bucket
    Separated Buckets for Indexing/Querying
    XDCR - Separate Cluster for Indexing
    Configuration Settings and their Effects on Views
    Indexing Settings
    Rebalance Settings
    Compaction Settings

Note: The above listed topics, labs, and all other content of this course are subject to change and/or update without prior notice from time to time, as we optimize our hands-on training for guaranteed customer success.

Upcoming Classes

Classes in bold are guaranteed to run!

Spain
Location May 2017 Jun 2017 Jul 2017 Aug 2017 Sep 2017
Madrid – Retiro – SPAIN May 29 – Jun 1
Enroll
Jun 12 – Jun 15
Enroll
Jul 10 – Jul 13
Enroll
Jul 24 – Jul 26
Enroll
Sep 25 – Sep 28
Enroll
Barcelona - SPAIN Jun 12 – Jun 15
Enroll
Jul 10 – Jul 13
Enroll
Sep 25 – Sep 28
Enroll
Virtual

Instructor-led online training

Location May 2017 Jun 2017 Jul 2017 Aug 2017 Sep 2017
VIRTUAL Class - EMEA GMT May 30 – Jun 1
Enroll
Jun 27 – Jun 29
Enroll
Aug 1 – Aug 3
Enroll
Sep 12 – Sep 14
Enroll
VIRTUAL Class - Pacific / Mountain Time Jul 11 – Jul 13
Enroll
Aug 22 – Aug 24
Enroll
United Kingdom
Location May 2017 Jun 2017 Jul 2017 Aug 2017 Sep 2017
London - Paul Street - UK May 30 – Jun 1
Enroll
Jun 27 – Jun 29
Enroll
Aug 1 – Aug 3
Enroll
Sep 12 – Sep 14
Enroll
France
Location May 2017 Jun 2017 Jul 2017 Aug 2017 Sep 2017
Paris - rue de Milan FRANCE Jun 28 – Jun 30
Enroll
Paris - avenue de l'Opéra - FRANCE Jun 28 – Jun 30
Enroll
United States
Location May 2017 Jun 2017 Jul 2017 Aug 2017 Sep 2017
Seattle/Bellevue-Kirkland Jul 11 – Jul 13
Enroll
Aug 22 – Aug 24
Enroll
San Francisco-MontgomeryM Jul 11 – Jul 13
Enroll
Aug 22 – Aug 24
Enroll
Los Angeles - Pacific/LAX - CA Jul 11 – Jul 13
Enroll
Aug 22 – Aug 24
Enroll
Denver - Downtown Jul 11 – Jul 13
Enroll
Chicago - Downtown Loop M Aug 22 – Aug 24
Enroll
Dallas TX - Lincoln Center Aug 22 – Aug 24
Enroll
New York - Broad St Sep 26 – Sep 28
Enroll
Australia
Location May 2017 Jun 2017 Jul 2017 Aug 2017 Sep 2017
Sydney-NSW-AUSTRALIA Aug 8 – Aug 10
Enroll
Singapore
Location May 2017 Jun 2017 Jul 2017 Aug 2017 Sep 2017
Singapore-Central-SINGAPORE Sep 5 – Sep 7
Enroll

Public Training

Madrid

VIRTUAL Class - EMEA GMT

London, London

Barcelona

Paris, Ile-de-France

Paris

Kirkland, WA

VIRTUAL Class - Pacific / Mountain Time

San Francisco, CA

El Segundo, CA

Denver, CO

, Sydney NSW

Chicago, il

Dallas, TX

Singapore, Singapore

New York, NY

Classes marked with Confirmed are guaranteed to run. Sign up now while there is still space available!