Building robust data pipeline at scale

Thursday 11th April
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Databricks Delta is a unified data management system that brings reliability and performance, 10-100 times faster than Apache Spark on Parquet to cloud data lakes. Designed for both batch and stream processing, it also addresses concerns regarding system complexity.


It provides ACID transactions, optimized layouts and indexes for building data pipelines to support big data use cases, from batch and streaming ingests, fast interactive queries to machine learning.  Its advanced architecture enables high reliability and low latency through the use of techniques such as schema validation, compaction, data skipping, etc. to address pipeline development, data management and as well query serving. All these innovations bring unprecedented performance, reliability and simplification to your cloud data lakes.


Join the Databricks team for a breakfast session dedicated to Delta. Our team of experts will share with you th basics and best practices on this unified data management system. You will learn how to: 

  • Create high performance data pipelines while providing reliability and data quality
  • Simplify data pipelines
  • Improve data scientist productivity with advanced features such as Time Travel.



Agenda:

08:30am-9:00am - Check-in and breakfast
09:00am-9:10am - Welcome - Nick Cochran, VP Customer Success EMEA, Databricks
09:10am-09:30am - Why Delta - Nick Peart, VP Marketing EMEA, Databricks
09:30am-10:00am - Delta Story: Black Swan Data - Robert Lovelock, Chief Architect, Black Swan Data
10.00am-10.15am - Networking break
10.15am - 11.15am - Databricks Delta: the unified analytics engine - Andrew Weaver, Solution Architect, Databricks
11:15am-11.30am - Q&A

Location:

Databricks Office

27 Baker Street, W1U 8EQ London


Date:
Thursday 11th April
From 08.30am


Speakers:
                            
Andrew Weaver,                   Robert Lovelock,
Solution Architect               Chief Architect
Databricks                               Black Swan Data


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