Azure DataBricks for Data Engineering by Eugene Polonichko

1. Azure DataBricks for Data
Engineering
Eugene Polonichko
Senior Software Developer at Eleks,
Data Platform MVP
2 0 1 8 U k r a i n e
https://www.linkedin.com/in/eugenepolonichko
/

2. About me
Eugene Polonichko has over 7 years of experience
with SQL Server. He mainly focused on BI projects
(SSAS, SSIS, PowerBI, Cognos, Informatica
PowerCenter, Pentaho, Tableau). Eugene is a
passionate speaker and SQL community volunteer
presenting regularly at PASS SQL Saturday events
and local user groups around Ukraine and Europe.
Eugene is PASS Chapter Leader and he has a status
MVP Data Platform
https://www.linkedin.com/in/eugenepolonichko/
https://twitter.com/EvgenPolonichko

3. Agenda
1. What is Azure Databricks?
• Azure Databricks
• Apache Spark
• Componets of Apache Spark
• Architecture of Azure Databricks
• Azure integration
2. Azure Databricks
• Cluster
• Workspace
• Notebooks
• Visualizations
• Jobs and Alerts
• Databricks File System
• Business Intelligence Tools
3. For data engineer
• Scenario
• Prices

4. What is Azure Databricks?

5. Azure Databricks
Azure Databricks is an Apache Spark-
based analytics platform optimized for
the Microsoft Azure cloud services
platform. Designed with the founders of
Apache Spark, Databricks is integrated
with Azure to provide one-click setup,
streamlined workflows, and an interactive
workspace that enables collaboration
between data scientists, data engineers,
and business analysts.

6. Apache Spark-based analytics platform
Azure Databricks comprises the complete open-source Apache Spark cluster technologies and capabilities.
Spark in Azure Databricks includes the following components

7. Apache Spark-based analytics platform
• Spark SQL and DataFrames: Spark SQL is the Spark module for working with
structured data
• Streaming: Real-time data processing and analysis for analytical and
interactive applications. Integrates with HDFS, Flume, and Kafka.
• MLib: Machine Learning library consisting of common learning algorithms
and utilities, including classification, regression, clustering, collaborative
filtering, dimensionality reduction, as well as underlying optimization
primitives.
• GraphX: Graphs and graph computation for a broad scope of use cases
from cognitive analytics to data exploration.
• Spark Core API: Includes support for R, SQL, Python, Scala, and Java.

8. Architecture of Azure Databricks

9. Total Azure integration
• Diversity of VM types
• Security and Privacy
• Flexibility in network topology
• Azure Storage and Azure Data Lake integration
• Azure Power BI
• Azure Active Directory
• Azure SQL Data Warehouse, Azure SQL DB, and
Azure CosmosDB:

10. Azure Databricks

11. Clusters
Azure Databricks clusters provide a unified platform for various use cases such as running production ETL
pipelines, streaming analytics, ad-hoc analytics, and machine learning.
Job
Interactive

12. Workspace
The Workspace is the special root folder for all of
your organization’s Azure Databricks assets.
The Workspace stores:
• notebooks
• libraries
• dashboards
• folders

13. Notebooks
A notebook is a web-based interface to a document that
contains runnable code, visualizations, and narrative text.
• Create a notebook
• Delete a notebook
• Control access to a notebook
• Notebook external formats
• Notebooks and clusters
• Schedule a notebook
• Distributing notebooks

14. Visualizations
Databricks supports a
number of visualizations out
of the box.
All notebooks, regardless of
their language, support
Databricks visualization
using the display function.
display(<dataframe-name>)

15. Jobs and Alerts
A job is a way of
running a
notebook or JAR
either immediately
or on a scheduled
basis
The number of jobs is limited to 1000.

16. Alerts
You can set up email
alerts for job runs. You
can send alerts up job
start, job success, and job
failure (including skipped
jobs), providing multiple
comma-separated email
addresses for each alert
type. You can also opt out
of alerts for skipped job
runs.

17. Databricks File System
Databricks File System (DBFS) is a
distributed file system installed on
Databricks Runtime clusters. Files in
DBFS persist to Azure Blob storage
You can access files in DBFS
using the Databricks CLI,
DBFS API, Databricks
Utilities, Spark APIs, and local
file APIs.
# List files in DBFS
dbfs ls
# Put local file ./apple.txt to dbfs:/apple.txt
dbfs cp ./apple.txt dbfs:/apple.txt
# Get dbfs:/apple.txt and save to local file ./apple.txt
dbfs cp dbfs:/apple.txt ./apple.txt
# Recursively put local dir ./banana to dbfs:/banana
dbfs cp -r ./banana dbfs:/banana
Python
Copy
#write a file to DBFS using python i/o apis
with open("/dbfs/tmp/test_dbfs.txt", 'w') as f:
f.write("Apache Spark is awesome!n")
f.write("End of example!")
# read the file
with open("/dbfs/tmp/test_dbfs.txt", "r") as f_read:
for line in f_read:
print line

18. Business Intelligence Tools
Business Intelligence (BI) tools can
connect to Azure Databricks clusters
to query data in tables. Every Azure
Databricks cluster runs a
JDBC/ODBC server on the driver
node. This section provides general
instructions for connecting BI tools
to Azure Databricks clusters, along
with specific instructions for
popular BI tools.

19. For Data Engineers

20. Scenario

21. Scenario

22. Thank you