Goals
- Structure and design Cassandra databases to stay ahead of your competitors
- Apply query models to model the data of your Cassandra databases
- Access Cassandra databases with CQL and Java
- Find the right balance between read / write speed and data consistency
- Integrate Cassandra with Hadoop, Pig and Hive
- Implement the most common Cassandra design patterns
Program
Why Use Non-Relational Data Warehouses
The Different Categories of NoSQL Data Warehouses
Define data warehouses with families of columns
Query Cassandra
Examine the main components of Cassandra’s architecture
Define CQL (Cassandra Query Language)
List the different types of CQL data
Manipulate data from the cqlsh interface
Draw a parallel with the relational model
Organize data with keyspaces, tables and columns
Create collections and counters
Create tables based on access patterns
Create clusters with composite primary keys
Improve data distribution with composite partition keys
Identify the different levels of consistency
Choose the read / write consistency levels of the data Differentiate the features for adjusting the consistency levels
Understand the link between consistency and replication factors
Sacrifice consistency in favor of availability
Develop linear consistency with Compare-And-Set
Group items into sets
Classify items into lists
Map relationships
Nest collections
Duration
3 days
Price
£ 1775
Audience
Database managers, technicians, data scientists, analysts, salespeople looking to integrate Cassandra into their current environment
Prerequisites
Knowledge of the fundamentals of databases, SQL and Java programming language is strongly recommended
Reference
BUS100296-F
Map data with tuples and user-defined types Understand the frozen keyword
Apply the Valueless Columns pattern Strategic
implementation of cluster columns
Time data expiration with time-to-live
Use tombstones for distributed deletions
Execute DELETE and UPDATE statements later
Model time series
Improve queries with materialized views
Maintain materialized views in the application
Analyze data from materialized views
Create triggers with ITrigger
Associate triggers with tables
Manage materialized views with triggers
Connect to a Cassandra cluster
Execute CQL statements through the Java driver
Process prepared statements in batches
Paginate large queries
Define the JPA (Java Persistence Architecture)
Configure Kundera for Cassandra
Generate schemas automatically
Manage JPA transactions in Kundera
Load data into Hadoop MapReduce with Cassandra InputFormat function
Use Cassandra load tool to create relationships with Pig Convert Cassandra table to Hive table with Cassandra serialization / deserialization
Sessions
Contact us for more informations about session date