This Cassandra Database training course will teach you to architect and engineer Cassandra databases for competitive advantage, model data in Cassandra based on query patterns and access Cassandra databases using CQL and Java. In addition, you learn how to create a balance between read/write speed and consistency, integrate Cassandra with Hadoop, Pig and Hive and implement commonly used Cassandra design patterns.
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. Attend this hands-on Cassandra Database course and learn to leverage all its features.
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Leveraging Cassandra structures and types
Modeling data based on queries
Detailing tunable consistency
Balancing consistency and performance
Working with Cassandra collection types
Storing data for easy retrieval
Controlling data life span
Constructing materialized views and time series
Querying Cassandra data with the Datastax Java Driver
Persisting Java Objects with Kundera
Leveraging built-in Cassandra connectors
Apache Cassandra provides a solution to the large volume of variety of data by permitting high-speed reads and writes across a replicated, distributed system.
A data science algorithm will ingest data from an appropriate storage technology like a relational database, Cassandra, MongoDB, Hadoop distributed file system into R or Python for data wrangling and model building. If the amount of data is large execution is performed in parallel using Spark. Spark could be deployed on Cassandra itself or on Hadoop’s HDFS file system or other file systems. The results are often visualized by the end user on dashboards.
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