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dianest · 2 weeks ago

Join us as we explore the SQL Transformations Connector! See how it enables you to modify, augment, and handle data directly within your Workato recipes, optimizing workflows, cutting down on manual tasks, and driving powerful automation across your business.

🧩 What is SQL Transformations?

The SQL Transformations Connector enables you to execute SQL queries and transform your recipe data, all without needing a database! With SQL Transformations, you can process complex datasets, perform calculations, and prepare data for downstream apps, making your recipes more powerful and flexible.

💡Sample Use Cases

1. Detect Data Deltas and Change Data Capture (SQL Transformations, On-prem Files, and Amazon S3)

What it does:

Identify changes in data across sources

Automate data synchronization

Learn More: CDC - Fetch data extract from source, compare with historical data to find the delta, and stream it ...

2. Pipeline Multi-Source Data Enrichment and Transformation (SQL Transformations, Salesforce, and SFTP)

What it does:

Combine and enrich data from multiple platforms

Standardize data for analytics or reporting

Learn More: ETL - Extract opportunities from Salesforce, enrich with product price details, and load to destinat...

3. Extract, Validate, and Cleanse Bulk Leads (SQL Transformations and Marketo)

What it does:

Cleanse and validate large datasets before import

Ensure data quality for marketing operations

Learn More: Validation & Cleansing data - Cleanse leads before adding them to Marketo

4. Automate Data Quality Checks (SQL Transformations and Workato File Storage)

What it does:

Run automated checks for missing or invalid data

Trigger alerts for error

Learn More: 01 - Fetch daily orders for Point-of-Sale (POS)

Learn More:

Recipes to Get Started:

ETL — Fetch daily orders from e-commerce database, standardize and load to buffer

🛠️ Common Questions & Tips

Q: Do I need a database to use SQL Transformations?
A: Not at all! SQL Transformations works directly on your recipe data in memory, so no external database is required.

Q: What SQL functions are supported?
A: The connector supports standard SQL operations including SELECT, WHERE, JOIN, GROUP BY, and common functions like SUM, COUNT, and CONCAT. Check Workato's documentation for the complete list.

Q: Can I combine data from multiple apps?
A: Absolutely! You can join and merge data from different sources within a single recipe, making it perfect for cross-system reporting and data enrichment.

Q: Any tips for optimizing SQL Transformations?
A: Start with small datasets to test your queries, use clear column names, and leverage SQL functions for calculations and data reshaping. Always validate results before sending data downstream.

📚Go Deeper with SQL Transformations

💬Action Item: Join the Conversation!

We’d love to learn more about your experience with SQL Transformations connector. What use cases have you tried, what worked well, and what tips or challenges can you share with the community?

🌟 We will be randomly choosing one comment on this post to win exclusive Workato swag! Don’t miss your chance, share your thoughts or questions below!

Read more:

Let’s get the discussion started! 👇

rajeshjanapati · a week ago

Hi @dianest ,

Thankyou for sharing these monthly deep-dives on new Workato features, it’s a great initiative and truly helpful for the community.

Regarding SQL Transformations in Workato, here are some key highlights based on my experience:

A powerful ETL/ELT engine ⚙️
Enables seamless transformation of bulk datasets directly within Workato recipes without relying on external databases.
No storage limitations 📦✨
Since SQL Transformations run fully in-memory, you are not constrained by traditional database storage restrictions.
Support for file-based data 📁🔗
A unique capability to include files (CSV, Excel, etc.) as part of rows during transformations, an excellent add-on feature.
Advanced data cleansing & quality operations 🧹📊
Ability to filter, cleanse, deduplicate, validate, and normalize large datasets efficiently. You can also remove or transform specific fields as needed.
Connect multiple data sources effortlessly 🔌🌐
Supports combining data from Workato Data Tables, Workato FileStorage, Content Streams, and various app connectors.
Join & compare cross-source data 🔀📈
Ability to join or compare datasets from different sources, for example, matching FileStorage data with Data Tables, all within a single SQL query.
Improves recipe and task efficiency 🚀📉
Offloading complex logic into SQL Transformations helps optimize recipes, reduce task consumption, and simplify overall workflows.
Easy Excel ↔ CSV conversions 🔄📄📊
SQL Transformations makes it very simple to convert Excel files to CSV and vice-versa, something that is more complex when using only native Workato actions.

Overall, SQL Transformations is one of the most powerful additions to the Workato platform for heavy data processing and cross-system data preparation.

Thanks & Regards,

J Rajesh Kumar.

Shivaaaa · a week ago

Hi @dianest ,

When Should You Use SQL Transformations?

✔ When handling large datasets

✔ When performing complex mappings

✔ When joining data from multiple systems

✔ When optimizing recipe performance

✔ When preparing data before sending to SAP, Marketo, Salesforce, etc.

✔ When converting or cleaning file-based data

SQL Transformations is easily one of the most impactful additions to Workato.
It brings true SQL power into your recipes, simplifies complex integrations, and handles large volumes with ease.

If you work with large data, complex mappings, or multi-source integrations — SQL Transformations is a must-use tool.

shivakumara · a week ago

Hi @dianest ,
Thank you for bringing new deep dives post.
Here are my thoughts with few examples

SQL Transformations in Workato – Extended Highlights & Benefits

Core Advantages

In-Memory ETL/ELT Engine
Perform complex transformations without external databases or infrastructure.
No Storage Constraints
Fully in-memory execution ensures scalability for large datasets.
File-Based Data Handling
Merge CSV, Excel, and other file formats with app data seamlessly.

🔍 Advanced Capabilities

Data Quality Enhancements
Deduplicate, validate, normalize, and cleanse data at scale.
Multi-Source Integration
Combine data from:
- Workato Data Tables
- FileStorage
- App connectors (Salesforce, NetSuite, etc.)
Cross-Source Joins & Comparisons
Example: Match Salesforce Opportunities with CSV-based campaign data.

⚡ Performance & Efficiency

Recipe Optimization
Offload heavy logic to SQL, reducing task usage and improving speed.
Dynamic Schema Handling
Automatically adapts to schema changes, minimizing maintenance.
Built-In Analytics
Perform aggregations (SUM, AVG, COUNT) and grouping directly in SQL.

🛠 Additional Unique Features

Conditional Transformations
Apply business rules dynamically (e.g., region-based filtering).
Data Enrichment
Add calculated fields like revenue projections or performance scores.
Error Handling & Validation
Flag invalid rows and route them for review before final output.
Format Conversions
Effortless Excel ↔ CSV conversions without complex steps.

Practical Use Case

Scenario:
Quarterly report combining Salesforce Opportunities and Marketing Campaign data.

Solution Steps:

Pull Salesforce data via connector.
Fetch campaign data from CSV in FileStorage.
Apply SQL Transformations to:
- Join datasets on Campaign_ID.
- Filter inactive campaigns.
- Aggregate revenue by region.
- Normalize date formats.
Export final dataset as Excel for leadership review.

Impact:

Eliminates external ETL tools and manual Excel work.
Cuts processing time by 60% and task usage by 40%.

Extra Example

Use Case:
Convert multiple Excel sheets into a single normalized CSV for ingestion into Snowflake.

SQL Transformations merges sheets, removes duplicates, and standardizes column names—all in one step.

Thanks and Regards,
Shivakumara K A