Objetivo

Após realizar este Curso Apache Cassandra Developer você será capaz de:

• Arquiteto e engenheiro de bancos de dados Cassandra para obter vantagem competitiva
• Modele dados no Cassandra com base em padrões de consulta
• Acesse bancos de dados Cassandra usando CQL e Java
• Crie um equilíbrio entre velocidade de leitura/gravação e consistência de dados
• Integre Cassandra com Hadoop, Pig e Hive
• Implementar padrões de design Cassandra comumente usados

Publico Alvo

• Administradores de banco de dados,
• profissionais de análise de dados,
• arquitetos de dados,
• gerentes

Pre-Requisitos

• A classe Administrator foi projetada para administradores com conhecimento básico de bancos de dados.

Materiais

Conteúdo Programatico

NoSQL Overview

1. Justifying non-relational data stores
2. Listing the categories of NoSQL Data Stores

Exploring Cassandra

1. Defining column family data stores
2. Surveying Cassandra
3. Dissecting the basic Cassandra architecture

Querying Cassandra

1. Defining Cassandra Query Language, CQL
2. Enumerating CQL data types
3. Manipulating data from the cqlsh interface

Leveraging Cassandra structures and types

1. Drawing comparisons with the relational model
2. Organizing data with keyspaces, tables and columns
3. Creating collections and counters

Modeling data based on queries

1. Designing tables around access patterns
2. Clustering with compound primary keys
3. Improving data distribution with composite partition Keys

Detailing tunable consistency

1. Identifying consistency levels
2. Selecting appropriate read and write consistency levels
3. Distinguishing consistency repair features

Balancing consistency and performance

1. Relating replication factor and consistency
2. Trading consistency for availability
3. Trading consistency for availability

Working with Cassandra collection types

1. Grouping elements in sets
2. Ordering elements in lists
3. Expressing relationships with maps
4. Nesting collections

Storing data for easy retrieval

1. Mapping data to tuples and user defined types
2. Investigating the frozen keyword
3. Applying the Valueless Columns Pattern
4. Strategic implementation of clustering columns

Controlling data life span

1. Expiring temporal data with time-to-live
2. Reviewing how tombstones achieve distributed deletes
3. Executing DELETEs and UPDATEs in the future

Constructing materialized views and time series

1. Modeling time series data
2. Enhancing queries with materialized views
3. Materialized views maintained in the application
4. Driving analytics from materialized views

Managing triggers

1. Creating triggers by implementing ITrigger
2. Attaching triggers to tables
3. Supporting materialized views with triggers

Querying Cassandra data with the Datastax Java Driver

1. Connecting to a Cassandra cluster
2. Running CQL through the Java Driver
3. Batching prepared statements
4. Paginating large queries

Persisting Java Objects with Kundera

1. Defining the Java Persistence Architecture, JPA
2. Configuring Kundera to work with Cassandra
3. Generating schemas automatically
4. Managing JPA transactions in Kundera

Leveraging built-in Cassandra connectors

1. Loading data into Hadoop MapReduce with the Cassandra InputFormat
2. Utilizing the Cassandra Loader to create Pig relations
3. Converting a Cassandra table to a Hive table with the Casssandra serializer/deserializer (SerDe)