Smartsheet’s Transition to Snowflake and Databricks: The Why and Immediate Impact
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Join this session to hear why Smartsheet decided to transition from their entirely SQL-based system to Snowflake and Databricks, and learn how that transition has made an immediate impact on their team, company and customer experience through enabling faster, informed data decisions.
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Smartsheet’s Transition to Snowflake and Databricks: The Why and Immediate Impact
- 1. Paivand Jalalian 4/24/19 Transition to Snowflake & Databricks Why and Immediate Impact
- 2. Agenda 1. What is Smartsheet and why is data analytics important to us? 2. How do Snowlake and Databricks help us achieve our purpose? 3. What kind of impact do Snowflake and Databricks make?
- 3. What is Smartsheet? Why is Data Analytics Important?
- 4. The Smartsheet Platform for Work Execution Empowering organizations to plan, capture, manage, automate, and report on work at scale. 4 $47M Q3 FY19 Revenue(1) 59% YoY Revenue Growth (1) 77K+ Domain-Based Customers (1),(4) Notes 1. As of October 31st, , 2018. Year-over-year revenue growth from Q3 FY18 and Q3 FY19. 4. We define domain-based customers as organizations with a unique email domain name such as @cisco. All other customers, which we designate as ISP customers, are typically small teams or individuals who register for our services with an email address hosted on a widely used domain such as @gmail, @outlook, or @yahoo.
- 5. One Platform, Many Uses Project Management • Project tracking • Resource management • Executive reporting • Gantt charts Marketing • Events • Campaigns • Website content • Product launches Human Resources • Candidate tracking • New hire onboarding • Exit processing • Corporate calendar It & Operations • Inventory / Assets • System migration • Issues triage • Maintenance Company Management • Company objectives • Balanced scorecard • Employee vacations • Meeting action tracking Finance • Contract process • Quarterly reviews • Corporate metrics • Budget rollups Sales • Sales pipeline • Customer contacts • Sales training • Sales rep activities Product Development • Development projects • QA scenarios • Production process • Feature prioritization Specialty Solutions • Store / branch communications • Rental property maintenance • Construction projects • Client engagement management 5
- 6. Data analytics is not important. It’s imperative. Informed Decisions Internal Data Analysis Achieve our Purpose Empower everyone to improve how they work. Targeted Customer Experience Outbound Data Analysis
- 7. How do Snowlake and Databricks Help Us Achieve Our Purpose?
- 8. Snowflake Platform (Cloud) Pipeline to S3 + Airflow (~5min) Distributed System Yes Yes (Minutes) ANSI SQL - easy to learn Rare Quick especially with adjustment of cluster, ~ 20 Minutes With views, as complex as needed ANSI sql, Java, + Connection to Databricks for ML, python, etc Replication & Data Latency Availability Easy Scalability Elasticity Ease of Use Occurence of table locks? Query large tables, ex. Aggregating 3B row table Permissions Syntax Legacy MySQL Platform (On-Prem) Easy & fast direct from app (~1 min) Replica, constant maintenance No - reaching limits of system No - query tuning required MySQL - easy to learn Frequently Slow, Killed after running for 1.5 hours Simple based on DB and action Restricted to Mysql Data Platform Comparison Differences in key features
- 9. Data Warehouse Analytics (Non-ML) Databricks for machine learning, Snowflake for everything else. Advanced Analytics
- 10. • Query speed (scaleable) + query large datasets • Conditional Permissions • Creation of views + copy DBs, schema’s, tables with in seconds • Un-drop tables • Departmental usage w/ monitoring • Connection to Tableau • Utilize different languages & packages • Create UDFs & procedures (loops) • Schedule jobs • Easy Visualizations • Intuitive UI/UX • Share Notebooks • Versioning via Git • Allows self service via “Run” permissions Key Benefits 10 Snowflake Platform ensures data structure and integrity Databricks Flexibility Databricks + Snowflake together provides the unique ability to implement advanced analytics while maintaining structure and integrity of underlying data.
- 11. Use Cases and Impact
- 12. Anomaly Detection ● Query 100M+ rows of telemetry data in Snowflake ● Pivots, aggregations & visualizations in Databricks ● Distribute Databricks dashboard to necessary parties + Results and insights derived quickly + Easy/fast distribution of data + Increase speed to action Use Cases Text Analytics of Unstructured Customer Comments ● Raw comment data stored in Snowflake ● NLP model in Databricks Notebook (R) ● Connector for end-to-end solution + Time savings human effort minimized + Consistency in categorizations + Ability to pull out patterns to derive insights Solution Impact
- 13. The combination of Snowflake & Databricks has not only allowed us to finally keep up with the growing scale of our company but get ahead.
- 14. Questions?