--- schema_version: "1" id: cf00d07bd4f27f1341cfb86f637f0c76012ec86caf7f59dc7013503091926832 title: Why Should I Use Recipes Instead of Dataflows? collection: admin version: current locale: en-us portal_url: https://docs.salesforce.com/admin/current/en-us/analytics/264-0-0/bi_integrate_prepare.md canonical_url: https://help.salesforce.com/s/articleView?id=analytics.bi_integrate_prepare.htm&release=264.0.0&type=5 source_site: help.salesforce.com content_type: documentation content_hash: c71e178721b74b8d53e2eaa155d0f217d7e87cc131e316aab3301e57191a4544 description: Recipes and dataflows both prepare data. Compared to dataflows, recipes are newer and are recommended for functionality and ease of use. Recipes allow you to preview the data as you transform it, while dataflows only show your node schema. For example, recipes have more join types and transformations with built-in machine learning - such as Predict Missing Values and Detect Sentiment - that aren’t available in dataflows. Recipes can also aggregate data to a higher level. products: - CRM Analytics - Cross Cloud release: "264" --- # Why Should I Use Recipes Instead of Dataflows? Recipes and dataflows both prepare data. Compared to dataflows, recipes are newer and are recommended for functionality and ease of use. Recipes allow you to preview the data as you transform it, while dataflows only show your node schema. For example, recipes have more join types and transformations with built-in machine learning - such as Predict Missing Values and Detect Sentiment - that aren’t available in dataflows. Recipes can also aggregate data to a higher level. Recipes and dataflows aren’t mutually exclusive. You can use both to meet more complex data preparation requirements. For example, you can use a dataflow to generate an intermediate dataset, and then use that dataset as the source for a recipe to perform additional transformations. ## Recipes Recipes are great for Salesforce admins and data experts who want to preview and transform data. With a recipe you can: - Work in an easy-to-use graphical interface. - Preview your data and how it changes as you apply each transformation. - Quickly remove columns or change column labels. - Analyze the quality of your data with column profiles. - Aggregate and join data. - Use built-in machine learning such as detect sentiment, data clustering, and time series forecasting. - Bucket values without having to write complex SAQL expressions. - Create calculated columns with a visual formula builder. - Use a point-and-click interface to easily transform values to ensure data consistency. For example, you can bucket, trim, split, and replace values without a formula. - See the history of all your changes, and back up or move forward to replay it. - Push your prepared data to other systems with output connectors. When you make a direct data reference in a recipe, no data is stored in CRM Analytics. When a recipe uses direct data, a query gets the current data. In Data Manager, SFDC\_Local connections represent data stored in CRM Analytics. Therefore, a direct data reference isn’t included in SFDC\_Local connections. When a recipe runs, the last run datetime for the SFDC\_Local objects that it uses aren’t updated. If a recipe runs using SFDC\_Local data, the recipe uses the data saved in the object during the last sync. Therefore, schedule the local object to sync before the recipe runs, for example, by using event scheduling. ## Dataflows Dataflows are great for CRM Analytics developers who prefer to work in a visual editor and, if necessary, JSON. With a dataflow you can: - Design complex data preparation flows with the visual Dataflow Editor. - Edit the underlying JSON for finer control. - Perform calculations across rows to derive new data for trending analysis. - Apply complex filters. The end-of-life date for dataflows hasn’t been set yet. You can continue to use dataflows if they’re effective and don’t require any of the advanced recipe features. If you’re starting a new data prep process, consider using recipes first. ## Transformation Comparison at a Glance Still unsure which tool to choose? Use this table to compare which transformations are available in recipes and dataflows. ![Note](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-analytics-bi-264-0-0-production-enus/ec791473-f5b4-4f64-a656-9104f455d966/images/icon_note.png) Note CRM Analytics supports two versions of Data Prep. Data Prep is the newest version that replaces the previous version, Data Prep Classic.
Feature Recipe Dataflow
Aggregate Check mark  
Append Check mark Check mark
Bucket Check mark

Check mark

(computeExpression transformation)
Calculate Expressions (across rows)

Check mark

(Formula transformation)

Check mark

(computeRelative transformation)
Calculate Expressions (same row)

Check mark

(Formula transformation)

Check mark

(computeExpression transformation)
Cluster Check mark  
Convert Column Types Check mark Check mark
Date and Time Check mark  
Delta   Check mark
Detect Sentiment Check mark  
Drop Columns Check mark

Check mark

(sliceDataset transformation)
Edit (Column) Attributes Check mark  
Extract Date Component Check mark  
Extract Dataset Data

Check mark

(Input node)

Check mark

(edgemart transformation)
Extract Salesforce Data

Check mark

(Input node)

Check mark

(sfdcDigest transformation)
Extract Synced Data

Check mark

(Input node)

Check mark

(digest transformation)
Filter Check mark Check mark
Flatten Hierarchies Check mark Check mark
Format Dates Check mark  
Join Check mark  
Lookup Check mark

Check mark

(augment transformation)
Predict Missing Values Check mark  
Predict Values Check mark

Check mark

(prediction transformation)
Profile Column Check mark  
Split Check mark  
Time Series Forecasting Check mark  
Update Values

Check mark

(update node)

Check mark

(update transformation)
#### See Also - [Clean, Transform, and Load Data with Data Prep](https://help.salesforce.com/s/articleView?id=analytics.bi_integrate_data_prep_overview.htm&release=264.0.0&type=5 "Data Prep provides an intuitive, visual interface in CRM Analytics that allows you to easily point-and-click your way to build recipes that prepare data and load it into a target. Use the graph of a recipe to see at a glance where the data comes from and how it flows through the recipe to the target. To validate the recipe as you build, preview how raw data is transformed at every step of the way.") - [Design Datasets with Dataflows and the Dataset Builder](https://help.salesforce.com/s/articleView?id=analytics.bi_integrate_design_datasets_with_dataflows_and_builder.htm&release=264.0.0&type=5 "Use a dataflow to create one or more datasets based on source data from existing datasets or synced data. A dataflow is a set of instructions that specifies what input data to include, how to transform that data, and which datasets to load the transformed data into.")