1.
Learn to Use Databricks
for Data Science
Sean Owen, Principal Solutions Architect
Austin Ford, Sr. Product Manager

2.
Data Science is a tough job
▪ Today, companies are becoming
more and more data-driven, and
the ones getting the most out of
their data will be the ones to
succeed
▪ As a result, Data Science is now a
core capability of many
businesses
▪ Unfortunately, it comes with a
challenging, complex workflow at
scale

3.
What does a data science workflow look like?
I need the correctly sized compute
resource for my task
I need to be able to find and access
the right data sources to fuel my
analysis
I need to be sure my toolbox is ready
with the packages and libraries
required for my work
1. Setup
I’ve been given a business
question to answer with data.
Before I can even get started on
the data science, I need to set
up my development
environment.

4.
What does a data science workflow look like?
I uncover insights through
statistical inference, modeling, or
other methods
I start with exploratory data
analysis to familiarize myself with
the data and form hypotheses
I synthesize the results of my work
and the answers to the original
business question
2. Data Science
Once the initial overhead of
setup is complete, the real work
begins.
At any point, I could be sent
back to the Setup phase to add
another data source, change
the size of my compute
resource, or pull in another
library.

5.
What does a data science workflow look like?
I share the results with my business
stakeholders via email or Slack
I formulate the results into a report
or dashboard so they can be
consumed
I get feedback about my work from
my stakeholders and iterate with
them to have the biggest impact
3. Sharing Results
The most important step comes
once I finish the analysis:
sharing the results with my
stakeholders.

6.
Our answer: The Databricks Lakehouse Platform
We want to remove the overhead so
you can focus on the most important
part of your work — data science

7.
Structured Semi-structured Unstructured Streaming
BI &
SQL Analytics
Machine Learning
Real-time Data
Applications
Data Management & Governance
Open Data Storage
Data Science &
Engineering
Lakehouse Platform
Simple | Open | Collaborative
Reliable | Scalable | Secure

8.
Structured Semi-structured Unstructured Streaming
BI &
SQL Analytics
Machine Learning
Real-time Data
Applications
Data Management & Governance
Open Data Storage
Data Science &
Engineering
Lakehouse Platform
Simple | Open | Collaborative
Reliable | Scalable | Secure
Our focus today

9.
Databricks makes setup easy
1. Setup
The Lakehouse brings all
your company’s data
together into a single place
so you don’t have to go
digging through a variety of
data sources
Easily choose the right
compute resource for your
task and switch as needed
single-machine VMs
GPUs Spark clusters
Databricks’ runtimes come
prepackaged with the most
common data science tools,
and customization is easy
Add Python libraries on top of a
runtime with a single line of code

10.
Databricks has the tools to enable you to focus on
your work
2. Data Science
Multi-language,
collaborative notebooks
with co-presence,
commenting, and co-editing
Built-in visualizations that
take you from raw data to
insights in two clicks
Auto-logged revision
history and a git integration
to ensure reproducibility and
enable version control

11.
Databricks lets your share results and iterate
quickly
3. Sharing Results
Easily share your notebooks
with stakeholders, who can
view them as reports
Create a dashboard directly
from your notebook’s results
Iterate with your
stakeholders directly in the
notebook through comments
and co-presence

12.
Getting practical: hands-on with an expert
Sean Owen
Principal Solutions Architect