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Course Outline
Introduction
Overview of MemSQL
Understanding the MemSQL Architecture
Quick Start with MemSQL Using MemSQL Ops
Understanding Essential MemSQL Concepts
- Overview of MemSQL Commands
- Working with Rowstore and Columnstore
- Implementing Data Distribution
- Using Shard Keys
- Implementing Distributed Joins
- Using Reference Tables
- Understanding Application Cluster Topologies
Installing and Upgrading MemSQL
- Designing a Cluster
- Doing Manual Installation
- Expanding a Cluster
- Implementing an Upgrade
- Securing MemSQL
Working with Schema Design and Query Optimization
- Working with Transactions
- Working with Geospatial Data
- Understanding Index Types
- Using Sparsity and Normalized Forms
- Hands-on: Using a Reference Table to Query JSON with Variant Array Lengths
- Working with Shard Key Strategies
- Identifying a Sharding Strategy
- Understanding Analyze, Explain, and Profile
- Implementing Schema Optimization for Query Performance
- Using Query Hints
Diving Deep into Administering MemSQL Operations
- Using the MemSQL Ops Command Line Interface
- Administering a Cluster
- Understanding Administrator Key Concepts
- Backing Up and Restoring Data
- Scaling Cluster Size
- Dealing with Cluster Failures
- Managing High Availability
- Monitoring MemSQL
- Working with the Trace Log
- Using Durability and Recovery
- Running Diagnostics
Working with MemSQL Procedural SQL (MPSQL)
- Using Table-Valued Functions
- Using User-Defined Functions
- Using User-Defined Aggregate Functions
- Using Stored Procedures
Implementing Performance Benchmarking and Fine-Tuning
- Using Experimental Metrics
- Performance Testing with dbbench
- Hands-on: Working with a Database Workload Generator
- Using Management Views
- Implementing Workload Profiling
- Hands-on: MemSQL Top
Working with MemSQL Pipelines and Real-Time Data Ingestion
- Using the MemSQL Connector for Apache Spark
- Using MemSQL Pipelines with Apache Kafka and AWS S3
Creating Real-Time Applications
- Working with Business Intelligence Dashboards
- Using MemSQL Pipelines for Machine Learning
- Implementing a Real-Time Dashboard
- Implementing Predictive Analytics
Troubleshooting MemSQL
Summary and Conclusion
Requirements
- Experience with Linux, relational database systems, and SQL platforms
- Experience with Scala, Java, or Python programming
28 Hours
Testimonials (3)
Trainer had good grasp of concepts
Josheel - Verizon Connect
Course - Amazon Redshift
What I liked most was the trainer's mastery of the subject, his patience and clarity when explaining the concepts, and especially his constant willingness to answer all the questions that arose. It was a really enriching and very enjoyable learning experience.
Patricio Condado - SOKODB
Machine Translated
how the trainor shows his knowledge in the subject he's teachign
