Live Webinars

Databricks Fundamentals |
Level 100 Introductory webinars

At Databricks, our mission is to make big data simple. We solve the conundrum of how to store, manage and get insight from the sheer variety of data types that exist today using a simple, open and collaborative platform that we refer to as the Databricks Lakehouse. 

 

No matter at what stage of your data journey you’re in, our online sessions will help data practicioners get a better understanding of the fundamental concepts of the Databricks Lakehouse platform, the problems we’re helping to solve, and how data teams are able to work collaboratively and be more productive using one common platform for all data uses-cases.

 

Come away from these sessions with answers to: 

  • Who is Databricks and what is a Lakehouse?
  • Which cloud platforms are Databricks deployed on?
  • What types of companies use Databricks today?
  • What use-cases and challenges is Databricks helping to solve in data engineering, data science and  business insights?
  • How much does it cost?
  • How can I get in touch with local my Databricks representative?

 

Introduction to Lakehouse and Delta (Non-technical) 
Session dates: 28 September, 13 October

Time: 11 AM - 12 PM AEST


This session provides a non-technical overview of the Databricks Lakehouse and demonstrate how our customers today utilise our platform for a broad range of use cases including core ETL, data discovery and exploration, data warehousing, data product deployment, and insight publishing using dashboards for internal and external audiences.

 

Join us in this virtual meetup where we’ll share best practices tips on how you can get started quickly with Databricks and a chance to get your most pertinent questions answered on pricing, availability, deployment, and how to engage with local representatives.

 

Introduction to Lakehouse and Delta: Technical Overview 

Session dates: 22 September 2021, 3 November 2021

Time: 11 AM - 12 PM AEST


Lakehouse is the modern data management paradigm that enables companies to get the most out of their data by leveraging the best of data lakes and data warehouses. In this workshop, we’ll walk through the evolution of data management and discuss how the lakehouse model enables teams to work in a unified system that provides performant queries, support for streaming, data science, machine learning and BI capabilities -- all built on a single source of truth.


We’ll also explore Delta Lake -- an open source storage layer that brings reliability to data lakes. Delta Lake provides ACID transactions, scalable metadata handling, and unifies streaming and batch data processing. We’ll talk about how Delta Lake makes the Lakehouse vision possible.

 

A Guide to Databricks for Data Engineering
Session dates: 6 October 2021, 30 November 2021

Time: 11 AM - 12 PM AEST


The rise of the lakehouse architectural pattern is built upon tech innovations enabling the data lake to support ACID transactions and other features of traditional data warehouse workloads. Join this session for a walk through of the Databricks Lakehouse architecture for Data Engineering.

 

Ran live by local experts, we’ll go through the technology foundation of Delta Lake (Apache Spark™), building highly scalable data pipelines, tackling merged streaming and batch workloads, powering data science with Delta Lake and MLflow, and even go behind the scenes to get a thorough understanding of the origins of Delta Lake.


A Guide to Databricks for Data Science
Session date: 20 October 2021, 14 December 2021

Time: 11 AM - 12 PM AEST

 

As organisations continue to become more data-driven, a collaborative environment for easier access and visibility into the data, models trained against the data, reproducibility, and insights uncovered within the data is critical.


Explore how Databricks lets data scientists and ML engineers quickly move from experimentation to production-scale machine learning model deployments — all on the same platform. In this workshop, we’ll work with a single data set throughout the lifecycle as well as scikit-learn, MLflow and Apache Spark™ on Databricks.

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