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Implement Partitioning with Azure

by Niraj Joshi

This course will teach you the data partitioning and sharding strategies in relational as well as non-relational cloud stores. Analyze data distribution before migration to Azure.

What you'll learn

Partitioning is an important strategy to segregate the data based on the partition key and distribute the data evenly across partitions for efficient querying and analysis.

In this course, Implement Partitioning with Azure, you’ll learn to apply efficient partitioning, sharding, and data distribution techniques over Azure Cloud Portal for specific services:

  1. explore how to design non-relational data stores
  2. discover designing relational data stores
  3. learn how to implement partitioning and sharding strategies over Azure Synapse
When you’re finished with this course, you’ll have the skills and knowledge of data distribution strategies across multiple cloud stores and even implement the global distribution of the data to increase throughput, the performance of the data stores, and availability of the dataset using the Azure Cloud Portal.

Course FAQ

What will I learn in this Azure tutorial?

This course teaches the importance of partitioning, how to select a partition key, what Azure Synapse is, and MPP architecture of Azure Synapse.

What is partitioning?

Partitioning is the creation of one or more regions on secondary storage, so that each region can be managed separately.

Are there any prerequisites for this course?

Before taking this course, it would be beneficial to have an understanding of Azure basics.

What are cloud stores?

Cloud stores or storage is a model of computer data storage where the digital data is stored in logistical pools, said to be on "the cloud".

What is Azure Synapse?

Azure Synapse analytics is a limitless analytics service that brings together data integration, enterprise data warehousing, and big data analytics.

About the author

Niraj is a AWS/Azure DevSecOps Cloud Specialist with over a decade of work experience into Data Modeling with Databases like Cassandra, MongoDB, SparkSQL, ElasticSearch and SQL Server. He has over 7 years of work ex into Computer Vision, Artificial Intelligence, DevOps, Machine Learning and Big Data Stack, he has been a consultant to companies like CISCO, ERICSSON, Dynamic Elements and JP Morgan He has excellent data visualization/ analytics skills and quite proficient in languages like Python ,... more

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