nicholas-chiodo
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Tuesday
Workato's robust architecture is designed to effortlessly handle the complex needs of enterprises, making it ideal for processing and managing large datasets and files. Whether you're managing customer data, updating inventory, or preparing financial reports, Workato scales seamlessly to meet these demands. We aim to empower you to tackle these use cases by equipping you with the essential tips and tricks needed to achieve your goals, ensuring efficiency and effectiveness in every task.
Efficient Techniques for Handling Large Datasets
Batch Processing
Workato's batch processing capabilities allow you to efficiently split large datasets into smaller, manageable batches. This approach not only prevents resource bottlenecks but also enhances the stability and performance of your data transfers, especially when managing large volumes of data.
Learn more about Batch Processing
Buffering and Storage
Workato's buffering capabilities manage the flow of data between systems operating at different frequencies or volumes. For instance, it can aggregate and buffer data from HubSpot, which is then streamed to Snowflake once a day. Utilize FileStorage to perform create and append actions, allowing you to aggregate and buffer data from HubSpot into CSV rows within a FileStorage file. Then, bulk stream this data once a day into Snowflake for data table loading, resulting in a single job to load all aggregated data.
Data streaming
Workato uses streaming mechanisms for scalable and high-speed data transfers. You can also use FileStorage to store output data as files and use them across jobs or different recipes.
Learn more about File Streaming
Technique for Handling Large Files
Setup a Workato Recipe to Handle Large CSV Files
The following steps describe how to build a Workato recipe for efficiently managing large CSV files by downloading, storing, and processing files in manageable chunks using SQL Transformations and FileStorage.
Trigger: HTTP Webhook for Split Request
Begin with an HTTP webhook trigger that listens for a 'Split Request', signaling the automation to start. Webhooks provide the flexibility to initiate data processing precisely when needed.
Step 1: Download/Export File from Google Drive
Once triggered, the recipe downloads the specified large CSV file from Google Drive. This ensures the correct data file is retrieved and brought into the Workato environment for subsequent processing.
Step 2: Store File in Workato FileStorage
Store the file in Workato FileStorage, securing it within Workato’s environment and preparing it for further operations. Specify parameters such as file name, path, and encoding to ensure proper formatting.
Step 3: Declare Variables
Declare key variables like Offset, Count, and Total Records before processing. These variables are crucial for effective data pagination - Offset and Count help segment the data, while Total Records tracks the dataset size.
Step 4: Query Data with SQL Transformations
Utilize SQL Transformations by Workato to efficiently handle large datasets. Start by counting the total number of records in the CSV file, leveraging SQL's power for scalability and performance.
Step 5: Update Total Records Variable
Update the Total Records variable based on the SQL query output, informing the recipe of the dataset’s size and enabling calculation of the required iterations for complete processing.
Step 6: Calculate Number of Pages
With Total Records known, calculate the number of pages required to paginate through the data. Pagination ensures data is processed in chunks, minimizing performance bottlenecks.
Step 7: Create List for Iteration
Create a list based on the calculated pages, guiding the recipe through each iteration to process data in chunks.
Step 8: For-Each Loop to Process Data
Enter a for-each loop, iterating over the list. In each iteration, update the Offset variable and perform another SQL query to fetch a data subset and create the segmented file within FileStorage. This approach ensures efficient, independent processing of each data chunk.
By understanding the purpose behind each step, you can appreciate the thoughtful design of this recipe. Whether dealing with large datasets or optimizing data processing workflows, this recipe offers a robust and flexible solution. Click below to check out a demo of this recipe design in action and also the recipe template that you can customize to meet your specific needs!
Conclusion
By integrating these tips and leveraging Workato's powerful features, handling large datasets and files becomes a manageable and efficient task. With batch processing, buffering, storage, data streaming and advanced file handling techniques, you can ensure seamless and high-performance data operations.
