The document discusses strategies for modern data warehousing and analytics on Azure including using Hadoop for ETL/ELT, integrating streaming data engines, and using lambda and hybrid architectures. It also describes using data lakes on Azure to collect and analyze large amounts of data from various sources. Additionally, it covers performing real-time stream analytics, machine learning, and statistical analysis on the data and discusses how Azure provides scalability, speed of deployment, and support for polyglot environments that incorporate many data processing and storage options.

1. ModernDataWarehouse
• Hadoop as an
ETL/ELT
optimization engine
• Integration of
streaming engines
for data ingestion
• Lambda and Hybrid
architectures that
include multiple
processing and
storage layers
DataLakes
• A good starting
point for an
organization to
enter into Big Data
• Collect data points
as files in a
massively
distributed file
system for analysis,
processing and
warehousing
AdvancedAnalytics
• Real-time stream-
based analytics
• Machine Learning
• Leverage statistics-
based packages in
R and Python for
exploratory analysis
• Uncover the
valuable data
points from Data
Lake

2. Scale
• Elasticity. Don’t
engineer for worst-
case scenario all the
time.
• Moving data to
Azure means taking
advantage of
Microsoft’s
bottomless storage
and enormous
compute resources.
SpeedofDeployment
• Customers want to
take advantage of
Azure’s ability to
automate
deployments
• A huge advantage of
Cloud Data solutions
in Azure is to quickly
spin-up & spin-
down environments
in dev/test, QA,
production as well as
pause & stop
PolyglotEnvironments
• Azure Data solutions
are able to
incorporate complex
heterogeneous data
environments that
include a large
spectrum of engine
and storage options
from RDBMS to
NoSQL to Hadoop
• Take advantage of
the Azure
Marketplace