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PLOTCON NYC: Building a Flexible Analytics Stack
The document discusses the process of building a flexible analytics stack for organizations, highlighting essential components such as data sourcing, event logging, and transformation. It emphasizes starting small, learning from the process, and progressively evaluating data products and applications. Key tools and technologies involved in the construction of this analytics ecosystem are also mentioned.
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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 anOrganizations 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
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- 15. 15 Business application Website Central warehouse Third party applications Analytics applications Event logging Transformation and aggregation E(T?)L Communicationand information platforms Analytics applications E(T?)L
- 16. 16 How this systemgets built (and what we learned building it)
- 17. 17 How this systemgets built (and what we learned building it)
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- 19. 19 Business application • Web application •Mobile app • Inventory management system • Point of sale system • Sales records in Excel
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- 21. 21 1. You canstart small THINGS WE LEARNED
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- 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
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- 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 stepat a time THINGS WE LEARNED
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- 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 . . . .
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- 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 dataproducts by their data THINGS WE LEARNED
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- 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 THINGSWE LEARNED
- 54. 54 Business application Website Central warehouse Third party applications Analytics applications Event logging Transformation and aggregation E(T?)L Communicationand information platforms Analytics applications E(T?)L
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- 56. 56 4. Start byasking questions THINGS WE LEARNED
- 57. 57 Business application Website Central warehouse Third party applications Analytics applications Event logging Transformation and aggregation E(T?)L Communicationand information platforms Analytics applications E(T?)L
- 58. 58 Website Website Third party applications Analytics applications ETL Event logging Business application Third party applications Communicationand information platforms Analytics applications Transformation and aggregation ETL
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- 61. 61 Smaller team Larger team components Workon the system Work on
- 62. 62 components Architect components Optimize the system Optimize the system Architect Maturedata infrastructure New data infrastructure Smaller team Larger team
- 63. 63 The best plotsdon’t have holes
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