This document announces an Einstein Analytics presentation by Rikke Hovgaard on October 27, 2018 as part of the Farmington Hills Salesforce Developer User Group. The presentation will cover topics such as SAQL, comparing datasets, and time series data in Einstein Analytics. It provides details on the speaker, agenda, and how to access the Zoom meeting for the presentation.
1. Farmington Hills
Salesforce Developer User Group
Apex Hours
Einstein Analytics
#SalesforceApexHours #FarmingtonHillsSFDCDug
Speaker
Date
Venue/Link
Rikke Hovgaard, Amit Chaudhary
Saturday, OCT 27, 2018 10:00 AM EST ( 7:00 PM IST)
https://zoom.us/j/405325057
2. Who am I ?
Amit Chaudhary (Salesforce MVP)
• Active on Salesforce Developer Community
• Blogging at http://amitsalesforce.blogspot.in/
• Co-Organizer of – FarmingtonHillsSFDCDug
• Follow us @Amit_SFDC or @ApexHours
#SalesforceApexHours #FarmingtonHillsSFDCDug
3. Our Speaker
Rikke Hovgaard
Einstein Analytics Solution Architect with Salesforce
(ACE team)
User Group Leader
Blogging at www.salesforceblogger.com
Follow me on Twitter @HovsaRikke
#SalesforceApexHours #FarmingtonHillsSFDCDug
5. #FarmingtonHillsSFDCdug #SalesforceApexHours
Apex Hours
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Forward-Looking Statement
7. #FarmingtonHillsSFDCdug #SalesforceApexHours
Apex HoursDataflow
At minimum has
these
plus
Combine/create
any based on these
SQL Oracle
External Data/ ETL
tools/APIs
Data
Lake
SFDC Objects
Data Landscape - With Connect
CSV
(gets added to Dataflow
dependent on DF)
External on it’s own schedule
Other SFDC
Orgs
Redshift, Other
Connectors
Marketing Cloud
CSV - Manual
Connect
Trend Report Data in Analytics
Dataset Builder
Data Recipes (not
part of Dataflow)
Start from or
combine from any
of these dataset
Recipes
8. Apex Hours
#FarmingtonHillsSFDCdug #SalesforceApexHours
Dataflow + Recipe
▶ What: tools to transform and register datasets
▶ Recipe very accessible and user friendly, dataflow more
powerful
▶ Example: create a compute expression
▶ Try it yourself:
▶ http://www.salesforceblogger.com/2018/04/24/my-most-used-computeexpression/
10. #FarmingtonHillsSFDCdug #SalesforceApexHours
Apex Hours
Technical Jargon - The Dashboard
Sales Wave App
Service Wave App
Event Monitoring App
Dataset
XMD
Dataset
XMD
Chart type/properties
+
Query
Chart type/properties
+
Custom Query (SAQL)
+
JSON
Dashboard
SFDC/
oData
SOQL/ApexSteps
Dbrd aka
asset XMD
*NEW (summer18) for
conditional formatting and
related - API accessible only
11. Apex Hours
#FarmingtonHillsSFDCdug #SalesforceApexHours
SAQL
▶ What: Salesforce Analytics Query Language – what is used at
run time.
▶ Use when you more complexity. Start out building as much as
possible in UI then switch to SAQL if needed.
▶ Example: YTD comparison, two datasets, timeseries
▶ Try it yourself:
▶ http://www.salesforceblogger.com/2017/11/20/ytd-comparison-with-compare-
tables/
▶ http://www.salesforceblogger.com/2018/10/16/a-look-to-the-future-with-timeseries/
13. Apex Hours
#FarmingtonHillsSFDCdug #SalesforceApexHours
SOQL Steps
▶ What: A different step type that use data directly from
Salesforce
▶ Good for real time – but has an impact.
▶ Example: Logged in user
▶ Try it yourself:
▶ http://www.salesforceblogger.com/2017/06/27/how-to-filter-dashboard-by-logged-
in-user/
14. Apex Hours
#FarmingtonHillsSFDCdug #SalesforceApexHours
Bindings
▶ What: A way to link steps together or with widgets.
▶ Example: Dynamic Gauge, Dynamic Groupings etc.
▶ Try it yourself:
▶ http://www.salesforceblogger.com/2017/06/19/how-to-make-your-gauge-chart-
dynamic/
▶ http://www.salesforceblogger.com/2017/02/28/the-power-of-static-steps/
▶ http://www.salesforceblogger.com/2017/02/23/toggle-selector-for-map-types-in-a-
wave-dashboard/
16. Apex Hours
#FarmingtonHillsSFDCdug #SalesforceApexHours
That’s all folks…
▶ https://trailblazercommunitygroups.com/events/details/salesfor
ce-farmington-mi-developers-group-presents-salesforce-apex-
hours-einstein-analytics-part-2#/
▶ Embarking on your EA journey?
http://www.salesforceblogger.com/2018/02/05/embarking-on-
your-einstein-analytics-journey-start-here/
▶ Remember to create the Learning Adventure App
Key Message Salesforce is a publicly traded company. Customers should make buying decisions only on the products commercially available.
Talk Track Before I begin, just a quick note that you should base your purchasing decisions on products and services that are currently available.
There are a few concepts from EA that are good to know. I've tried to compare them with std reporting though it two tools that work differently. Datasets are our data, the way we structure our objects, so it's similar to what report types do. To create this structure we use the dataflow. Once we do a single exploration with measures and groupings that is a lens similar to a report. Dashboards is well dashboards. And instead of folders in EA we use apps, but works in the same way as folders controlling access.
Dataset Builder addes instructions (nodes with specific transformations) to Dataflow file
User Dataflow file creates datasets by running against SFDC DB/other SFDC orgs/Redshift and/or existing datasets
External ETL can directly create datasets in wave
Recipies in Data Prep run their own dataflow file against the existing datasets and create new ones
All of the datasets are availale for querying (bulding dashboards and lenses on top of them)
