Analytics Data Integration: Sync Out for Snowflake, Data Prep Is Generally Available, Expanded Data Prep Formula Library

Export your raw local Salesforce data to your Snowflake data lake without the need for a third-party ETL tool. The new visual recipe builder, Data Prep, is generally available with even more features. Add more robust calculated columns to your datasets.

Avoid Stale Salesforce Data in Snowflake with Sync Out (Beta)
Fresh Salesforce data is vital if you maintain a central Snowflake data lake for processing, analysis, business automation, or storage. For example, give your shipping logistics team the freshest data by merging your account data from your system of record, Salesforce, with your ERP’s shipping data in your data lake. Sync Out for Snowflake exports your raw local Salesforce data via Analytics to Snowflake using the Einstein Analytics connector for Snowflake. With Sync Out for Snowflake, keep your Salesforce data in Snowflake up to date using scheduled Data Sync without the need for a third-party ETL tool.
Write Your Einstein Analytics Datasets to Snowflake from Recipes (Generally Available)
The Snowflake Output connector lets you push your prepared data from Analytics into Snowflake when you use Data Prep recipes. You design powerful Data Prep recipes that combine data from multiple sources, add formula fields, and transform data into datasets tailored to your business needs. With the Einstein Analytics connector for Snowflake, datasets are liberated from Analytics and written as a table for you without the need to rebuild relationships and business logic. This feature is now generally available.
Prepare Data with the Next Generation of Data Prep (Generally Available)
The latest version of Data Prep expands on the intuitive, visual interface that allows you to build recipes with clicks. Use the new graphical view of a recipe to see at a glance where data comes from, how it flows through the recipe, and where the results are written. To validate the recipe as you build, preview how raw data is transformed every step of the way. The new version of Data Prep is called Data Prep and the old version is called Data Prep Classic.
Build Calculated Formula Columns with More Functions in Data Prep
You can now use EA SQL functions with the Data Prep formula transformation to add calculated columns derived from other fields to your dataset. EA SQL is a collection of standard and custom functions for numeric, string, and date data. Previously, you were limited to 18 functions when you built formulas in Data Prep. Now there are more than 40 functions, including [case] to create buckets or flags based on field value, and [datediff()] to calculate the duration between dates.
Calculate Dates with Click-Not-Code Date and Time Transformations
Want to create Data Prep recipe fields based on date column calculations with clicks instead of manual queries? Check out the new predefined date formula transformations. With the Now transformation, you can insert a column with the current date and time in a specified format. Use Date Difference to calculate the duration between two selected date columns, like the time it takes for support to close cases by subtracting the created date from the closed date. And use the Add or Subtract Days or Months function to time travel to visit the dinosaurs. Just kidding, it lets you add or subtract days or months from a date column.
Quickly Calculate Statistics with Aggregate Functions
Calculate more aggregates in recipes using the new Aggregate node functions STDDEV (sample standard deviation), STDDEVP (population standard deviation), VAR (sample variance), and VARP (population variance). For example, a realty company calculates the average price of all homes sold in San Francisco in April 2020. To determine how spread out these home prices are, the realty company calculates the population standard deviation using the STDDEVP function.
View More Previews in Recipes
Each user can initiate up to 4,000 previews an hour in Data Prep. Previously, the maximum previews per hour per user was 2,000. Each time you click a node or transformation to preview its results, the preview count increases.
Manage Row-Level Security for Data Prep Recipes
Sharing inheritance is now available for datasets created from Data Prep recipes. Sharing inheritance increases access accuracy and reduces the need for complicated security predicates for most objects and situations. For example, a quarterly pipeline dashboard automatically shows each rep only their own opportunities, and their managers can see opportunities of the team, following the opportunity sharing rules. Previously, sharing inheritance was only available for datasets created by a dataflow.
Edit a Dataset with One Click from Data Manager
Clicking a dataset link in the Data tab of Data Manager now opens the dataset properties page, where you can edit the dataset. Previously, the link created a recipe with the dataset as the source.
Monitor and Update Auto-Installed Analytics Apps from Setup
Easily see which Analytics apps are automatically installed in your Salesforce org with the new Auto-Installed Apps page. You can update the app, view logs on the app status, delete apps no longer in use, and monitor auto-install requests.

No matches found