PLOTCON NYC: Building a Flexible Analytics Stack
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ABSTRACT Board rooms and business reports have long been stuffed with basic graphs and ugly charts. Now, inspired by the work that appears in today's newspapers and scientific journals, and powered by tools like Plotly, businesses are doing much more to find answers and insight in their data. This talk will outline both how companies, and particularly leading tech startups, are combining--and often building--technologies that collect, prepare, move, transform, and visualize data, and the problems these businesses are solving with this new stack of data tools.
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PLOTCON NYC: Building a Flexible Analytics Stack
- 1. Benn Stancil November 16, 2016 1 Building a flexible analytics stack
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- 13. 13 Editors in an Organizations Each Month Sales month/month Lost accounts Year over year 125 8 $20,187,921.56 Gross revenueNew accounts month/month 1.75% 0.5% 0.5% Accounts by State Search…Sales Editors in an Organizations Each Month Sales Personal SPACES General Home Financial Reports Sept. Board Meeting Marketing Retention Reports Churn analysis
- 15. 15 Business application Website Central warehouse Third party applications Analytics applications Event logging Transformation and aggregation E(T?)L Communication and information platforms Analytics applications E(T?)L
- 16. 16 How this system gets built (and what we learned building it)
- 17. 17 How this system gets built (and what we learned building it)
- 19. 19 Business application • Web application • Mobile app • Inventory management system • Point of sale system • Sales records in Excel
- 21. 21 1. You can start small THINGS WE LEARNED
- 25. 25 Business application Website Analytics applications Analytics applications • Google Analytics • Mixpanel • Chartbeat • Kissmetrics • Amplitude
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- 31. 31 Website Central warehouse Event logging Analytics applications • PostgreSQL • MySQL • Oracle • MS SQL Server • Redshift • BigQuery • Hive • Spark
- 32. 32 Website Central warehouse Event logging Analytics applications • PostgreSQL • MySQL • Oracle • MS SQL Server • Redshift • BigQuery • Hive • Spark
- 33. 33 Website Central warehouse Event logging Analytics applications • Google Analytics • Amplitude • Heap • Segment • Snowplow • Custom built
- 36. 36 Business application Central warehouse E(T?)L • Fivetran • Segment • ETLeap • Stitch Data • Data Virtuality • Xplenty • Alooma • Blendo • AWS Data Pipeline • Custom built
- 37. 37 2. One step at a time THINGS WE LEARNED
- 39. 39 Business application Website Central warehouse Analytics applications Event logging E(T?)L Analytics applications • Salesforce • Zendesk • Stripe • Mailchimp • Twilio • JIRA • Hubspot • Marketo • NetSuite • Square • Github • Adwords • FB Ads . . . .
- 41. 41 Business application Third party applications E(T?)L E(T?)L • Fivetran • Segment • ETLeap • Stitch Data • Data Virtuality • Xplenty • Alooma • Blendo • AWS Data Pipeline • Custom built
- 42. 42 3. Evaluate data products by their data THINGS WE LEARNED
- 45. 45 Transformation and aggregation • Informatica • Pentaho • Alooma • Keboola • Airflow • Custom built • Analytics applications
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- 51. 51 Analytics applications • Salesforce • Zendesk • Mailchimp • JIRA • Hubspot • Marketo • NetSuite • Adwords • FB Ads . . . . • Email • Slack • Internal wikis • Customer sites
- 52. 52 Analytics applications • Salesforce • Zendesk • Mailchimp • JIRA • Hubspot • Marketo • NetSuite • Adwords • FB Ads . . . . • Email • Slack • Internal wikis • Customer sites • Analytics applications • Custom built • Processes
- 53. 53 4. Be unavoidable THINGS WE LEARNED
- 54. 54 Business application Website Central warehouse Third party applications Analytics applications Event logging Transformation and aggregation E(T?)L Communication and information platforms Analytics applications E(T?)L
- 56. 56 4. Start by asking questions THINGS WE LEARNED
- 57. 57 Business application Website Central warehouse Third party applications Analytics applications Event logging Transformation and aggregation E(T?)L Communication and information platforms Analytics applications E(T?)L
- 58. 58 Website Website Third party applications Analytics applications ETL Event logging Business application Third party applications Communication and information platforms Analytics applications Transformation and aggregation ETL
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- 61. 61 Smaller team Larger team components Work on the system Work on
- 62. 62 components Architect components Optimize the system Optimize the system Architect Mature data infrastructure New data infrastructure Smaller team Larger team
- 63. 63 The best plots don’t have holes
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