Cassandra advanced – modeling and analytics

mm
Valentina Crisan
Cassandra advanced – modeling and analytics

Acest curs este predat în limba română, iar materialele sunt în limba engleză şi/sau în limba română, după caz.

La cerere, cursul poate fi personalizat.

At the end of this course the participants will be able to:
● Understand through exercises the rules behind Cassandra data modeling, how the performance
differs depending on what we use for answering a query: remodeling of data, indexes, views or a
combination of those;
● Understand what other factors are important, besides data modeling, for getting a good query and
system performance;
● Understand the theory of modeling data for getting best performance out of Cassandra, based on
the design of the application workflow, Chebotko diagram, logical and physical modeling rules;
● Perform pre-aggregation of data in Cassandra and finalize data analytics in Spark SQL

 

Caracteristici curs

  • Capitole 19
  • Durata 3 zile
  • Nivel cunostinte Orice nivel
  • Limba Romana
  • Cursanti 12
  • Day 1: Cassandra Architecture and concepts Recap Q&A + exercises

    • Capitol 1.1 Indexing in Cassandra – recap through exercises Locked 0m
    • Capitol 1.2 Native secondary indexes and the possible combination with Allow Filtering Locked 0m
    • Capitol 1.3 SStable attached secondary indexes Locked 0m
    • Capitol 1.4 Materialized views Locked 0m
    • Capitol 1.5 Through hands on exercises we will compare performance of Secondary indexes vs Materialized views and Denormalized Tables. Locked 0m
  • Day 2: Important factors that impact queries performance, besides data modeling

    • Capitol 2.1 Deletes in Cassandra – theory + exercises Locked 0m
    • Capitol 2.2 Compaction in Cassandra: understand the way Size Tiered, Level Tiered and Time Locked 0m
    • Capitol 2.3 Cassandra Data Modeling process description: Locked 0m
    • Capitol 2.4 Conceptual data modeling, application workflow Locked 0m
    • Capitol 2.5 How to build a Chebotko diagram Locked 0m
    • Capitol 2.6 Logical modeling: principles, mapping rules, mapping patterns Locked 0m
    • Capitol 2.7 Physical modeling: partition size calculation Locked 0m
    • Capitol 2.8 KDM tool for data modeling Locked 0m
    • Capitol 2.9 Exercise to model data according to the data modeling process Locked 0m
  • Day 3: Analytics with Cassandra

    • Capitol 3.1 What you can and cannot do with Cassandra (functional limitations) and why is needed an analytical layer Locked 0m
    • Capitol 3.2 How to do analytics with Cassandra and Spark SQL Locked 0m
    • Capitol 3.3 Full analytics with Spark SQL, reading data from Cassandra Locked 0m
    • Capitol 3.4 Pre-aggregation in Cassandra, predicates push down from Spark to Cassandra, finalize analytics in Spark SQL Locked 0m
    • Capitol 3.5 Final exercise that will combine Spark and Cassandra functionalities Locked 0m
mm
Valentina Crisan