Building reliable data processing pipelines with Azure Databricks Delta live tables

English Intermediate Data & AI

Azure Databricks Delta Live Tables is a framework for building reliable, maintainable, and testable data processing pipelines. You define the transformations to perform on your data, and Delta Live Tables manages task orchestration, cluster management, monitoring, data quality, and error handling on its own. Instead of defining your data pipelines using a series of separate Apache Spark tasks, Delta Live Tables manages how your data is transformed based on a target schema you define for each processing step. You can also enforce data quality with Delta Live Tables with a feature called "expectations". "Expectations" allow you to define expected data quality and specify how to handle records that fail those expectations. In this session, we will learn how to develop a data processing pipeline with Azure Databricks Delta live tables. Who is the target attendee? Data Architect/ETL Developers/Consultants Why would that person want to attend your session? - To learn the modern way to develop the data processing pipelines What can the attendee walk away with? - What is an Azure Databricks Delta live table? - Delta Live Tables concepts. - How to Develop Data processing pipelines with Delta Live Tables?


Rajaniesh Kaushikk

Enterprise Cloud Solution Architect

Rajaniesh Kaushikk is a TOGAF Certified Enterprise Architect with 20 years of extensive experience in successfully delivering complex software application Architecture and solutions for Fortune 500 companies. He is an expert in Digital Transformation, Architecting, and developing modern applications in Azure, IoT, Edge, Microservices, and BOTS. Rajaniesh is a mentor, a speaker, and the man behind Rajaniesh is Microsoft certified trainer and Microsoft Certified Architect Expert. Rajaniesh loves writing technical blogs and community contributions via various forums and won several recognitions. Rajaniesh can be reached via his blog and youtube channel: ​

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