Virtual ClassroomLearning Style
3 DaysCourse Duration
About Individual Course:
The large volume and variety of data that today's businesses process require the need for a highly available, low latency database. Apache Cassandra provides this solution by permitting high-speed reads and writes across a replicated, distributed system. This Apache Cassandra training course provides data modeling experience to take advantage of the linearly scalable peer-to-peer design of Cassandra.
- Architect Cassandra databases and implement commonly used design patterns
- Model data in Cassandra based on query patterns
- Access Cassandra databases using CQL and Java
- Create a balance between read/write speed and data consistency
- Integrate Cassandra with Hadoop, Pig, and Hive
- Knowledge of databases and SQL
- Java programming
Virtual Instructed-Led Outline
Introduction to Apache Cassandra
- Justifying non-relational data stores
- Listing the categories of NoSQL Data Stores
- Defining column family data stores
- Surveying Cassandra
- Dissecting the basic Cassandra architecture
- Defining Cassandra Query Language, CQL
- Enumerating CQL data types
- Manipulating data from the cqlsh interface
Representing Data in the Cassandra Data Model
Leveraging Cassandra structures and types
- Drawing comparisons with the relational model
- Organizing data with keyspaces, tables and columns
- Creating collections and counters
Modeling data based on queries
- Designing tables around access patterns
- Clustering with compound primary keys
- Improving data distribution with composite partition Keys
Configuring Data Consistency
Detailing tunable consistency
- Identifying consistency levels
- Selecting appropriate read and write consistency levels
- Distinguishing consistency repair features
Balancing consistency and performance
- Relating replication factor and consistency
- Trading consistency for availability
- Achieving linearizable consistency with Compare-And-Set
Leveraging Cassandra Idioms and Programming Patterns
Working with Cassandra collection types
- Grouping elements in sets
- Ordering elements in lists
- Expressing relationships with maps
- Nesting collections
Storing data for easy retrieval
- Mapping data to tuples and user defined types
- Investigating the frozen keyword
- Applying the Valueless Columns Pattern
- Strategic implementation of clustering columns
Controlling data life span
- Expiring temporal data with time-to-live
- Reviewing how tombstones achieve distributed deletes
- Executing DELETEs and UPDATEs in the future
Constructing materialized views and time series
- Modeling time series data
- Enhancing queries with materialized views
- Materialized views maintained in the application
- Driving analytics from materialized views
- Creating triggers by implementing ITrigger
- Attaching triggers to tables
- Supporting materialized views with triggers
Accessing Cassandra Programmatically
Querying Cassandra data with the Datastax Java Driver
- Connecting to a Cassandra cluster
- Running CQL through the Java Driver
- Batching prepared statements
- Paginating large queries
Persisting Java Objects with Kundera
- Defining the Java Persistence Architecture, JPA
- Configuring Kundera to work with Cassandra
- Generating schemas automatically
- Managing JPA transactions in Kundera
Integrating Cassandra with Analytical Frameworks
Leveraging built-in Cassandra connectors
- Loading data into Hadoop MapReduce with the Cassandra InputFormat
- Utilizing the Cassandra Loader to create Pig relations
- Converting a Cassandra table to a Hive table with the Casssandra serializer/deserializer (SerDe)
|Learning Style||Virtual Classroom|
|Course Duration||3 Days|
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