Learn Project Best Practices

Below is a collection of key best practices for any data and analytics developer working on a Tableau CRM or Einstein Discovery project. All the insight is based on years of experience and many projects of high complexity.

Planning for a successful project

Any data project should be carefully planned and considerations made before getting started with any build.


Maintaining Data Sync and Data Prep Considerations - Data Orchestration Part 5

Maintaining Data Sync and Data Prep Considerations – Data Orchestration Part 5

Exploring how to maintain data sync and taking a closer look at data prep tools (dataflow and recipe), their limits, and scheduling of data prep.
Data Prep Scheduling - Data Orchestration Part 6

Data Prep Scheduling – Data Orchestration Part 6

Considerations you should have when scheduling your dataflows as well as some obstacles you may encounter.
Dependent Data Jobs - Data Orchestration Part 7

Dependent Data Jobs – Data Orchestration Part 7

There are scenarios where dataflow and recipes are dependent on each other and should be run in a specific sequence. In this part, we will provide a practical approach to these scenarios.
Typical Tableau CRM Asset Lifecycle

Typical Tableau CRM Asset Lifecycle

Understand the different stages of an analytics project aka the asset lifecycle plus roles and responsibilities at each stage.

Managing Salesforce environments

How should you manage production and sandbox environments and how do you best deploy from one environment to another.


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Designing beautiful analytics apps

How do you design dashboards with an intended purpose that are beautiful and a pleasure to use.


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Building for enterprise environments

If you use Salesforce enterprise features that you want to include in your data and analytics use cases.


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Managing & optimizing assets

Keep your environment tidy and performance-optimized. Use tools to get the job done quickly.


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