"Combining Databricks, the unified analytics platform with Snowflake, the data warehouse built for the cloud is a powerful combo. Databricks offers the ability to process large amounts of data reliably, including developing scalable AI projects. Snowflake offers the elasticity of a cloud-based data warehouse that centralizes the access to data. Databricks brings the unparalleled utility of being based on a mature distributed big data processing and AI-enabled tool to the table, capable of integrating with nearly every technology, from message queues (e.g. Kafka) to databases (e.g. Snowflake) to object stores (e.g. S3) and AI tools (e.g. Tensorflow). Key Takeaways: How Databricks & Snowflake work; Why they're so powerful; How Databricks + Snowflake symbiotically catalyze analytics and AI initiatives"

1. WIFI SSID:SparkAISummit | Password: UnifiedAnalytics

2. Garren Staubli
Solutions Architect
Databricks + Snowflake:
Catalyzing Data and AI
#UnifiedAnalytics #SparkAISummit
Slides & Resources: garrens.com/DataSnowCat
garren@databricks.com | @gstaubli

3. Agenda
3#UnifiedAnalytics #SparkAISummit | Slides & Resources: garrens.com/DataSnowCat
Introductions
Scenario
Challenges
Solutions
Demo

4. 4#UnifiedAnalytics #SparkAISummit | Slides & Resources: garrens.com/DataSnowCat
Introductions - Me
2011 2012 2013 2014 2015 2016 2017 2018 2019
MySQL
Ruby
AWS
Pig & Hive
Python
Linux
Scala, Python & Java
Apache Spark & ML
Hadoop
NoSQL

5. Databricks Delta
Databricks Workspace
Collaborative Notebooks, Production Jobs
Databricks Runtime
Transactions Indexing
ML FrameworksML Frameworks
Introductions - Databricks
Cloud
Data & ML
Lifecycle
Data Engineering Data Science
Accelerate innovation by unifying data science and engineering

6. 6#UnifiedAnalytics #SparkAISummit | Slides & Resources: garrens.com/DataSnowCat
Introductions - Snowflake

7. 7#UnifiedAnalytics #SparkAISummit | Slides & Resources: garrens.com/DataSnowCat
Forget Oil. Data is worth
more than Gold

8. 8#UnifiedAnalytics #SparkAISummit | Slides & Resources: garrens.com/DataSnowCat
Scenario

9. 9#UnifiedAnalytics #SparkAISummit | Slides & Resources: garrens.com/DataSnowCat
Data Mining Data Science ML Engineering
QADevOps
Production
Delivery*
* not Digiorno
Scenario - Annotated

10. 10#UnifiedAnalytics #SparkAISummit | Slides & Resources: garrens.com/DataSnowCat
Scenario - Reality

11. 11#UnifiedAnalytics #SparkAISummit | Slides & Resources: garrens.com/DataSnowCat
Challenges
WAREHOUSES NOSQL
LAKES STREAMS
Sources
APIs Apps BI
Data-Driven
Production

12. 12#UnifiedAnalytics #SparkAISummit | Slides & Resources: garrens.com/DataSnowCat
Challenges - Reality
Partitions: 20
Rows per second: 10,000
Format: JSON
Extract Transform
ML
Load
Analysis
Insights

13. Flat, RDBMS, Streams, etc
Unified batch & stream APIs
Autoscaling with usage
Python, Scala, SQL, R & Java
JVM w/ optimization
Multilevel APIs (SQL + RDDs)
13#UnifiedAnalytics #SparkAISummit | Slides & Resources: garrens.com/DataSnowCat
Challenges & Solutions - ETL
Partitions: 20
Rows per second: 10,000
Format: JSON
Sources
Syntax
Scale
Languages
Performance
Expressiveness

14. 14#UnifiedAnalytics #SparkAISummit | Slides & Resources: garrens.com/DataSnowCat
Challenges & Solutions - ETL
Partitions: 20
Rows per second: 10,000
Format: JSON
Malformed Records
Errors
Changing Fields
Writes
- Performance
- Semantics
- Reliability
Ignore/infer + log records
Handle + retry w/ checkpoint
Schema Evolution
Partitioned + optimized files
Exactly once
ACID transactions

15. 15#UnifiedAnalytics #SparkAISummit | Slides & Resources: garrens.com/DataSnowCat
Challenges & Solutions - ML
Partitions: 20
Rows per second: 10,000
Format: JSON
Data Access
Syntax
Collaboration
Models
- Iteration
- Reproducibility
- Deployment
Apache Spark + Delta
Koalas
Databricks Notebooks
- Tracking
- Projects
- Models

16. Interactive queries
Instant Scaling
SQL
Tableau, PowerBI, etc
Optimized DWaaS
Decoupled storage + compute
16#UnifiedAnalytics #SparkAISummit | Slides & Resources: garrens.com/DataSnowCat
Challenges & Solutions - Analysis
Partitions: 20
Rows per second: 10,000
Format: JSON
Time to value
Intermittent demand
Language
Common Tooling
Ease of Use
Cost control

17. 17#UnifiedAnalytics #SparkAISummit | Slides & Resources: garrens.com/DataSnowCat
Final Solution Architecture
Partitions: 20
Rows per second: 10,000
Format: JSON
Machine Learning
BI Reporting
Dashboards

18. 18#UnifiedAnalytics #SparkAISummit | Slides & Resources: garrens.com/DataSnowCat
Demo

19. Review
19#UnifiedAnalytics #SparkAISummit | Slides & Resources: garrens.com/DataSnowCat
Introductions
Scenario
Challenges
Solutions
Demo

20. 20#UnifiedAnalytics #SparkAISummit | Slides & Resources: garrens.com/DataSnowCat
Solution
WAREHOUSES NOSQL
LAKES STREAMS
Sources
Processing
WAREHOUSES NOSQL
BI
Persistence
APIs Apps BI
LAKES
DELTA
Integration
DELTA
ML

21. DON’T FORGET TO RATE
AND REVIEW THE SESSIONS
SEARCH SPARK + AI SUMMIT