Bridging Legacy OPC Classic Servers(DA, AE, HDA) to SnapLogic via OPC UA Wrapper
Despite significant advances in industrial automation, many critical devices still rely on legacy OPC Classic servers (DA, AE, HDA). Integrating these aging systems with modern platforms presents challenges such as protocol incompatibility and the absence of native OPC UA support. Meanwhile, modern integration and analytics platforms increasingly depend on OPC UA for secure, scalable connectivity. This post addresses these challenges by demonstrating how the OPC UA Wrapper can seamlessly bridge OPC Classic servers to SnapLogic. Through a practical use case—detecting missing reset anomalies in saw-toothed wave signals from an OPC Simulation DA Server—you’ll discover how to enable real-time monitoring and alerting without costly infrastructure upgrades
Scalable Analytics Platform: A Data Engineering Journey
Scalable Analytics Platform: A Data Engineering Journey - Explore SnapLogic's innovative Medallion Architecture approach for handling massive data, improving analytics with S3, Trino, and Amazon Neptune. Learn about cost reduction, scalability, data governance, and enhanced insights.Industrial IoT – Turbine Lubrication Oil Level Monitoring & Alert Mechanism via OPC UA and SnapLogic
In the energy sector, turbine lubrication oil is mission-critical. A drop in oil level or pressure can silently escalate into major failures, unplanned shutdowns, and expensive maintenance windows. In this blog, we showcase a real-world implementation using SnapLogic and OPC UA, designed to: 🔧 Continuously monitor turbine lubrication oil levels 📥 Ingest real-time sensor data from industrial systems 📊 Store telemetry in data lakes for analytics and compliance 📣 Real-time Slack alerts to engineers — before failures strike This IIoT-driven solution empowers energy providers to adopt predictive maintenance practices and reduce operational riskIndustrial IoT – OPC UA Real-Time Motor Overheat Detection and Auto-Shutdown Using SnapLogic
Industrial motors are critical assets in manufacturing and process industries, where overheating can result in costly downtime or catastrophic failure. In this blog, we demonstrate how SnapLogic and OPC UA were used to build a real-time, event-driven pipeline that detects motor overheating, initiates an automated shutdown, logs events for auditing, and notifies the maintenance/engineering team
APIM Function Generator - Integrate Your APIM Services as Tools for LLMs
As large language models (LLMs) continue to evolve, they are becoming powerful enablers of intelligent automation, capable of interpreting natural language and performing actions through external tool calling. In SnapLogic, this capability is supported through LLM agent pipelines, where function definitions—whether created manually, built from Snaplogic pipelines , or derived from OpenAPI specifications—can be transformed into callable tools that LLMs use to interact with external systems. SnapLogic’s API Management (APIM) plays a key role in this process. APIM offers a modern and flexible way to design, secure, and publish APIs directly within the Snaplogic platform. With support for service versioning, policy configuration, and a DeveloperHub for API discovery, APIM simplifies the management and reuse of APIs across various projects and use cases. Many users already rely on APIM to expose critical business logic through well-structured, reusable APIs that support a wide range of integrations and workflows. However, connecting these APIs to LLM tool calling has traditionally involved significant manual effort, including: Difficulty discovering APIM service versions and endpoints at the Snap level Rewriting API descriptions and parameter definitions already defined in the APIM service Configuring multiple Function Generator Snaps Manually maintaining tool definitions to stay in sync with updates to the APIM service The APIM Function Generator Snap addresses these challenges by automating the entire setup process. It connects directly to an APIM service version and generates a list of Snaplogic internal format of the function definitions using live metadata to use with the Tool Calling Snap and related downstream Snaps. By removing the need for manual configuration and automatically reflecting any updates made to the APIM service, this Snap helps maintain consistency, reduce setup time, and simplify integration. It provides a fast and reliable way to turn an existing APIM service version into callable tools for LLMs, enabling more efficient automation. APIM Function Generator Snap Snap Properties Project path: Select the project path that contains the target APIM service. Service name: Choose the service name to use. Suggestions are provided based on the selected project path. Version: Specify the version of the selected service. Suggestions are provided based on the selected service name. Base URL: Specify the base URL used to call the API. Suggestions are provided based on the Plex selected in the APIM service version. Preferred content type: Specify the desired content type for API calls. Filter type: Determines how the APIs within the service version are selected. Aggregate input: When enabled, all incoming documents are combined into a single list of function definitions. Filter Type Options The Snap supports three filter types for determining which APIs from the selected APIM service version are included in the function definitions: Use all paths This option includes every available API path and method from the selected service version. It is the simplest way to expose the entire API set without additional filtering. Tags This option allows you to specify one or more tags. Any API that includes at least one of the specified tags—whether at the endpoint, path, or method level—will be included in the generated function definitions. Tag-based filtering is useful for organizing APIs into meaningful groups. For example, you can create separate tool sets for different agents, such as billing, ticketing, analytics, or read-only access. Paths and methods This option enables you to define specific paths and their associated HTTP methods. It offers precise control over which endpoints are exposed to the LLM agent. This filter type is especially valuable for enforcing permissions; for instance, by including only GET methods, you can restrict an agent to read-only operations. This flexible configuration allows you to tailor the function definitions to match your use case, ensuring the right APIs are available for your LLM agent workflows. Output of the Snap The Snap outputs a list of tool definitions. Each tool definition includes: sl_tool_metadata: Contains essential metadata used by the Tool Calling Snap and downstream Snaps to route and invoke the correct API. It enables dynamic resolution of the URL path based on parameters from the LLM output. The tool_type is set to APIM, and the metadata includes the service name, version name, endpoint name, tags(including endpoint tags, path tags, and method tags), and the full URL of the API. json_schema: defines the expected structure of input parameters for the API. This schema guides the LLM in formatting its output correctly and allows the Tool Calling Snap to resolve the complete URL path for calling the API. These fields are generated based on the live metadata from the APIM service version. Example Use Case: Product Recommendation To demonstrate how the APIM Function Generator Snap streamlines the integration of existing APIs into LLM workflows, consider a scenario based on a retail business use case. In this example, the SnapLogic APIM service named APIM Tool Demo, version v1, is used to expose APIs for a clothing store. This service includes three endpoints: /weather: A proxied external API used to retrieve real-time weather data. /inventory: A SnapLogic pipeline task exposed as an API to access product data from the store’s database. /users: Another SnapLogic task API that provides access to user-related information. These APIs might already be powering external applications or internal tools. With the APIM Function Generator Snap, they can now be easily transformed into callable tools for LLMs—eliminating the need for separate specifications or manual configuration. In this use case, the goal is to build an LLM agent capable of recommending clothing items based on the current weather. For example, the LLM may receive a prompt such as: “Please recommend at most 3 items from the women’s clothing category in the store based on the current weather in Paris and provide the reason.” To support this workflow, the /weather endpoint and some APIs in /inventory endpoint were tagged with recommendation in the APIM service. This tag is then used in the APIM Function Generator Snap to filter and include only the relevant APIs when generating tool definitions—making it easy to group related functionality. Agent Driver Pipeline System prompt: You are a friendly and helpful assistant that are equipped with some tools, please use your judge to decide whether to use the tools or not to answer questions from the user. User prompt: Please recommend at most 3 items from the women’s clothing category in the store based on the current weather in Paris and provide the reason. Agent Worker Pipeline Function generation The APIM Function Generator is used for providing the tool definition to the Tool Calling Snap Snap settings: Output: Tool Calling The Tool Calling Snap sends tool definitions to the LLM and outputs tool calls with the full URL and headers resolved using parameters returned by the LLM. This output is then passed to the Router Snap to route each call to the appropriate downstream Snap. Router The Router Snap is useful when different endpoints require specific handling, such as different authentication methods, custom configurations, or when using multiple services or types of Function Generator Snaps. It leverages fields from sl_tool_metadata, such as the endpoint name, to route each tool call to the appropriate path. Example route expression: $.get("sl_tool_metadata").tool_type == "APIM" && $.get("sl_tool_metadata").service_name == "APIMToolsDemo" && $.get("sl_tool_metadata").endpoint_name == "weather" Calling API The API is called using the HTTP Client Snap. The request method and URL are dynamically configured using sl_tool_metadata. The appropriate account should be selected according to the policies defined in the APIM service. With this setup, the LLM can respond to queries by utilizing the APIM tools. Below is the message history, demonstrating that the tool can be called successfully. Final Result Example pipelines Recommendation agent driver Recommendation agent worker Conclusion The APIM Function Generator Snap simplifies the integration of APIM services as tools for LLMs by automating function generation and keeping definitions in sync with live API metadata. This streamlined approach eliminates manual setup, supports flexible filtering, and transforms existing APIs into callable tools, enabling dynamic and metadata-driven automation within LLM workflows.
A Guide to the Enhanced PipelineLoop
Introduction As integration demands grow increasingly latency-sensitive—particularly in pipelines leveraging large language model (LLM) Snaps—minimizing execution delays has become critical to maintaining performance at scale. In response, SnapLogic has released an enhanced version of the PipelineLoop Snap designed to address two key performance bottlenecks: startup overhead and document output latency. This article provides a detailed overview of the enhancements, explains their impact on real-world tool-calling pipelines, and offers practical guidance on how to configure and apply these features to unlock immediate performance improvements. What’s New in the Enhanced PipelineLoop? 1. Optimized Document-Output Time The PipelineLoop Snap has been enhanced with a shorter and more efficient polling interval, allowing it to detect completed child pipelines sooner and push output documents with less delay. While the mechanism remains polling-based rather than fully event-driven, the reduced wait time significantly lowers output latency—particularly in workloads with many iterations. 2. Pre-Spawned Pipeline In traditional PipelineLoop behavior, a new child pipeline is initialized for each incoming document. While this works well for lightweight tasks, it becomes inefficient for tool-calling workloads where initialization time can be significant. To address this, the enhanced PipelineLoop introduces the ability to maintain a pool of pre-spawned child pipelines that are initialized in advance and reused across iterations. The number of warm pipelines is controlled by the Pre-spawned pipelines property. As each child pipeline completes, a new one is automatically initialized in the background to keep the pool full, unless the total iteration count is fewer than the configured pool size. When the loop finishes, any idle pipelines are shut down gracefully. This feature is particularly useful in scenarios where child pipelines are large or involve time-consuming setup steps—such as opening multiple account connections or loading complex Snaps. By setting Pre-spawned pipelines to a value greater than one, the PipelineLoop can eliminate the cold-start delay for most documents, improving throughput under high-traffic conditions. The following steps provide a way to configure the pipeloop with pre-spawned pipeline. Pre-Spawned Pipeline Walkthrough This walkthrough demonstrates how the Pre-spawned Pipelines feature works in practice, using a simple parent-child pipeline configuration. 1. Parent Pipeline Setup The parent pipeline includes a Sequence Snap configured to emit one input document. It uses a PipelineLoop Snap with the following settings: Iteration limit: 10 Pre-spawned pipelines: 3 2. Child Pipeline Setup The child pipeline contains a Script Snap that introduces a 2-second delay before incrementing the incoming value by 1. This simulates a minimal processing task with a noticeable execution time, ideal for observing the impact of pre-spawning. Here is the script. This script processes each input document by adding one to the "value" field after a simulated 2-second delay. The purpose of the delay is not to simulate real processing latency, but rather to make the pre-spawned pipeline activity clearly observable in the SnapLogic Dashboard or runtime logs. 3. Pipeline Execution When the parent pipeline is executed, the system immediately initializes three child pipeline instances in advance—these are the pre-spawned workers. As each finishes processing a document, the PipelineLoop reuses or replenishes workers as needed, maintaining the pool up to the configured limit. 4. Controlled Execution Count Despite having pre-spawned workers, the PipelineLoop respects the iteration limit of 10, ensuring that no more than 10 child executions occur in total. Once all 10 iterations complete, the loop shuts down all idle child pipelines gracefully. This setup highlights the benefit of pre-initialized pipelines in reducing execution latency, particularly for scenarios where child pipeline startup time contributes significantly to overall performance. 3. Parallel Execution Support The enhanced PipelineLoop Snap introduces parallel execution, allowing multiple input documents to be processed simultaneously across separate child pipeline instances. This capability is especially beneficial when dealing with high-throughput workloads, where processing documents one at a time would create unnecessary bottlenecks. By configuring the Parallel executions property, users can define how many input documents should be handled concurrently. For example, setting the value to 3 enables the loop to initiate and manage up to three loop executions at once, significantly improving overall pipeline throughput. Importantly, this parallelism is implemented without compromising result consistency. The PipelineLoop maintains output order alignment, ensuring that results are delivered in the exact sequence that input documents were received—regardless of the order in which child pipelines complete their tasks. This makes the feature safe to use even in downstream flows that rely on ordered data. Parallel execution is designed to maximize resource utilization and minimize processing delays, providing a scalable solution for data-intensive and latency-sensitive integration scenarios. Parallel Execution Walkthrough This walkthrough illustrates how the Parallel Execution capability in the enhanced PipelineLoop Snap improves performance while preserving input order. A basic parent-child pipeline setup is used to demonstrate the behavior. 1. Parent Pipeline Configuration The parent pipeline begins with a Sequence Snap that generates three input documents. A PipelineLoop Snap is configured with the following parameters: Iteration limit: 3 Parallel executions: 3 This setup allows the PipelineLoop to process all three input documents concurrently. 2. Child Pipeline Configuration The child pipeline consists of a Script Snap that introduces a 2-second delay before appending the character "A" to the "value" field of each document. Below is the script used in the Script Snap: The delay is intentionally added to visualize the effect of parallel processing, making the performance gains more noticeable during monitoring or testing. 3. Execution Behavior Upon execution, the PipelineLoop initiates three child pipeline instances in parallel—one for each input document. Although each child pipeline includes a 2-second processing delay, the overall execution completed in approximately 9 seconds which is still significantly faster than the optimal serial execution time of 18 seconds. While the theoretical runtime under ideal parallel conditions would be around 6 seconds, real-world factors such as Snap initialization time and API latency can introduce minor overhead. Despite this, the result demonstrates effective concurrency and highlights the performance benefits of parallel execution in practical integration scenarios. Most importantly, even with concurrent processing, the output order remains consistent with the original input sequence. This confirms that the PipelineLoop’s internal queuing mechanism correctly aligns results, ensuring reliable downstream processing.
Basics of SnapLogic
Introduction SnapLogic is a cloud-based integration Platform-as-a-Service (iPaaS) that provides tools for connecting various applications, data sources, and APIs. It enables businesses to automate and streamline their data integration processes by offering pre-built connectors and a visual interface for designing integration workflows. The SnapLogic platform uses a SnapLogic pipeline, a series of connected "Snaps" (pre-built components) that define the flow and transformation of data between various systems and applications. In a SnapLogic pipeline, data flows from one Snap to another, with each Snap performing a specific function, such as data extraction, transformation, or loading (ETL). SnapLogic Designer The SnapLogic Designer is the user interface that enables you to develop pipelines. You can see the example page below. But in SnapLogic with a feature called “Asset Palette,” you may see the different styles of Side Panel view. But the features are the same as those of the side panel view. The designer page consists of three main parts: Canvas - The field for visualizing and editing the pipeline Side Panel / Asset Palette - The panel contains the menu list. (The left picture is the Side Panel view. The right picture is Asset Palette enabled) Snaps Catalog - lists all available Snaps. https://docs-snaplogic.atlassian.net/wiki/x/ePIV Pipelines Catalog - list all pipelines that you can access. https://docs-snaplogic.atlassian.net/wiki/x/w-IV Patterns Catalog - list all the patterns that you can access. https://docs-snaplogic.atlassian.net/wiki/spaces/SD/pages/3022160260/Patterns+Catalog Toolbar - The list of tools for the pipeline Icons Description Execute Pipeline Execute the pipeline. Validate Pipeline Validate the pipeline. Any unsaved changes will be saved before validation. Clicking the button while a validation is in process cancels that validation. Shift-clicking the button will clear the cache before validating. Edit Pipeline Properties You specify properties when creating a pipeline. Click this button to modify the properties. Check Pipeline Statistics As a pipeline executes, the statistics are updated periodically so that you can monitor its progress. Create Task Create a Task for the current pipeline. Save Pipeline Save the current pipeline. Export Pipeline Export the current pipeline. Copy Pipeline Copy the pipeline from one project to another. Move Pipeline Move the pipeline from one project to another. Delete Pipeline Delete the current pipeline Pipeline Versions Create versions of the pipeline. Compare Pipeline Compare the current pipeline with the target pipeline. Notes Add a note or delete an existing note. Notes are saved with the pipeline. Print Print the pipeline. Snaps Snaps are the building blocks of a pipeline. Each Snap performs a single function, such as reading, parsing, transforming, or writing data. You can view the Snaps available to you (or your account) in the Snaps Catalog on the left-hand side of the SnapLogic Designer. You can drag a Snap from the Snap Catalog onto the Canvas to use it in a pipeline. Snaps Type SnapLogic includes the following basic types of Snaps with distinct icons. Icon Snap Type Description Read Specifies data sources in the pipeline. Examples: File Reader, CSV Generator, Birst Query Parse Takes the input of unstructured data and generates an output of structured data. Examples: XML Parser, Sequence Parser, JSON Parser Transform Modifies data significantly. Examples: Mapper, Aggregate, Join Flow Changes the output or direction of data in a pipeline. Examples: Router, Gate, Union Format Changes the data format. Examples: CSV Formatter, JSON Formatter, Excel Formatter Write Specifies data destinations in a pipeline. Examples: File Writer, REST Post, Email Delete Connecting Snaps The key to creating a Pipeline in SnapLogic is connecting Snaps. There are a few things to consider when placing Snaps in a Pipeline. Connection Shapes Like puzzle pieces, only Snaps with matching connection pairs (circles or diamonds) can be connected between the input and output of two snaps. When you drag a snap and place it next to or in front of another snap, the snap will automatically connect both snaps, and the connection will change color, which means it connects successfully. If the color doesn’t change, you need to recheck that both connection shapes are the same and re-connect it again. Disconnect Linked Snaps Unlinked Snaps can be moved apart or placed next to each other. Make sure the circle or diamond connector is colored Blue, which indicates that the Snaps are linked. To disconnect linked Snaps, click on the Blue connector. This clears the color and allows you to rearrange the Snaps. Remote-Connect Link Snaps You can connect to Snaps, but not next to each other, using a remote-connect link. For example, click and hold on the Mapper Snap connector until it turns Yellow, then drag it to the Copy Snap connector. When both connections turn Blue, release the mouse button. A number is placed in both connectors to let you know they are connected. Note: The number is only temporary until the Pipeline is saved. At this point, a new, permanent number may be assigned. You can also click and hold on one connection, and both Snaps connected by this link will darken. This feature is helpful for large pipelines where it may take much work to visualize the connections quickly. Data model SnapLogic will pass the data between Snaps with two models: Document data The document data models will be represented by a circle shape. This data type uses the JSON format as a container of the data. The support data type in this model is similar to the JSON standard in that it includes string, boolean, number, array, object, and null. Binary data The document data models will be represented by a diamond shape. This data type will wrap the binary data in SnapLogic’s model. Mostly, this will be inputted to the file writer and parser and outputted from the file reader and formatter. Configuration Snaps You have two options to open the configuration dialog. First, left-click on the Snap that you want to configure. The dialog will show up immediately. The second way is right-clicking at the Snap, and the menu displays options available in all Snaps through a dropdown list will be shown. Then click “Edit” in the menu. Each Snap will have different configurations. You can learn more about the configuration of each snap by clicking the question mark icon on the top right of the dialog. Expression The SnapLogic expression language is a utility that is available to Snaps. You can use expressions (JavaScript syntax) to access functions and properties to set field values dynamically. You can also use the expression language to manipulate data. Example $text == "NFL" ? "foo" : "bar" $counter > 1 ? ($counter < 3 ? 50 : 100) : -1 Expressions are available across multiple Snaps. If the Snap exposes the functionality of the expression for a property, then the icon appears in front of the property's text box. You can toggle on or off by clicking on the icon. When the toggle is on, the down arrow within the field will appear. You can click to see the list of functions and properties available. Operations List of supported and unsupported operations available on (document https://docs-snaplogic.atlassian.net/wiki/spaces/SD/pages/1438042/Understand+Expressions+in+the+SnapLogic+Platform) Accessing Pipeline Parameters Parameters allow a pipeline to be reused in multiple situations. For example, a File Writer Snap can be configured to write to a file path specified by a parameter, which allows the same pipeline to write to different files. The parameters for a pipeline can be defined by using the Edit Pipeline properties dialog. The name of each parameter must only contain alpha-numeric characters, and the value will be converted to a string. The value for a parameter defined in the pipeline properties dialog is treated as the default when running the pipeline in Designer. Parameters can also be passed to the Pipeline Execute Snap. Any parameters not passed down from the Task or Snap will use the defaults specified in the properties dialog. To access a pipeline parameter from the expression language, you must prefix the parameter name with an underscore. For example, given the following parameters: Key Value firstName Bob numValue 12 path $.age The "firstName" parameter can then be accessed using _firstName, as in: "Hello, " + _firstName // result: Hello, Bob Since the value of a parameter is always a string, you'll need to convert any string to numeric values before operating on them. For example, simply adding two to the "numValue" parameter will append the character "2" to "12" and yield "122": _numValue + 2 // result: "122" Instead, you need to use the parseInt/parseFloat functions to parse the string into a value and then add two to it: parseInt(_numValue) + 2 // result: 14 You need to parameterize your pipeline with an expression. You can use the eval() function to evaluate an expression stored in a string. For example, to read the document field specified by the "path" parameter, you can use: eval(_path) // result: <the value of the "age" field in the current document> Accessing Input View Variables as Part of Expressions An input view schema attribute can be used as part of the expression using the dollar sign ($) prefix. Example The REST Put Snap provides a URL. The URL can be toggled into an expression, and the expressions could be created by dynamically substituting the variables from an input view, such as: 'http://someplace:someport/somepart/' + $inputvar + '/somemoreparts' Accessing secret value from the secrets manager Any expression-enabled authentication field in a Snap or Account can be used with Secrets Management. You can enter an expression that retrieves a secret stored in your secrets manager, such as an access token, a username, or a password. To use the values from the secrets manager, you must first create secrets myaccesskey and mysecretkey in the Secrets Manager vault. Then, create or modify the Account and enter an expression in the required fields. Learn more: Configure Accounts to use secrets. Account An account represents an object that encompasses details to connect to an endpoint. Accounts play a crucial role in integrating applications. Any Snap that communicates with an external endpoint needs an authenticated account to access the resources on the endpoint. For example, a MySQL Snap requires authenticated access to a MySQL database. In SnapLogic, you create an Account to store credentials and any other information necessary to connect, such as a URL, hostname, and port number. You can create an account from Designer or Manager. In Designer, when working on pipelines, every Snap needing an account prompts you to create a new account or use an existing one. To use an existing account, you can click the dropdown icon to show all the available accounts for the snaps. To create a new account, click the “Add Account” button below the property field and follow the steps. The account will be created in your selected location on the first step. You can manage the created account on the Manager page in that location. Note: You can learn more about account type and each property by clicking the icon question mark in the top right corner. Validation & Execute Pipeline Sometimes, we want to test the pipeline by dry-running it without running the write snaps. You can use the validate function on the toolbar menu. The difference between validate and execute is before each snap runs. It will check the property called “Snap execution.” There are three opinions on how to trigger the snaps. Validate & Execute - this option makes the snaps run on both the validation and execution steps. Execute only - this option makes the snaps run on only the execution step. The snap writer type uses this as a default value. Disabled - this option prevents the snaps from running. Note: By default, the validation will be triggered every time we change the configuration of the snaps in the pipeline. Preview Data After executing or validating the pipeline, they will have a preview icon in the connection joint. The preview dialog will appear when you click on it, showing the snaps' output data. For example, when we click the preview icon, the pipeline above will show output data from JSON Generator snaps. The preview dialog has three types: JSON, Table, and Raw. You can select the dropdown on Preview Type and choose the type you like. JSON Table Raw Create First Pipeline This section will show how you start creating the pipeline, from the requirement to checking the result and running the final pipeline. For the example scenario, we want to calculate the employees list to check who needs to be assigned marketing training. The list of our employees looks like the data below. [ { "Name": "Albert Maro", "Location": "Field", "Extension": 4357, "Email": "amaro@company.com", "Title": "Director, Eastern US", "Department": "Sales", "Dept ID": 1100 }, { "Name": "Anthony Dunn", "Location": "HQ", "Extension": 4387, "Email": "adunn@company.com", "Title": "Social Media Director", "Department": "Marketing", "Dept ID": 1200 }, { "Name": "Rich Harris", "Location": "CO", "Extension": 4368, "Email": "rharris@company.com", "Title": "Principal Developer", "Department": "Engineering", "Dept ID": 1300 } // more data ] The constraint of needing training is an employee in the marketing department working at “HQ.” We want the list of employees with Firstname, Lastname, Email, Title, and Training fields. The result should look like below. [ { "Firstname": "Albert", "Lastname": "Maro", "Email": "amaro@company.com", "Title": "Director, Eastern US", "Training": false }, { "Firstname": "Anthony", "Lastname": "Dunn", "Email": "adunn@company.com", "Title": "Social Media Director", "Training": true }, { "Firstname": "Rich", "Lastname": "Harris", "Email": "rharris@company.com", "Title": "Principal Developer", "Training": false } // more data ] Steps 1. Open the Designer page. 2. Click to create a new pipeline. 3. Change the label to “Employees training” and click save. 4. At this step, we already have a new empty pipeline. Then, find the “JSON Generator” snap from the side panel and drag it to the canvas screen. This snap generates a JSON document for the next snap in the pipeline. We will set it as an input source. 5. Click at the JSON Generator snap to open the configuration dialog and click “Edit JSON.” Then, replace all JSON with the value below. [ { "Name": "Albert Maro", "Location": "Field", "Extension": 4357, "Email": "amaro@company.com", "Title": "Director, Eastern US", "Department": "Sales", "Dept ID": 1100 }, { "Name": "Anthony Dunn", "Location": "HQ", "Extension": 4387, "Email": "adunn@company.com", "Title": "Social Media Director", "Department": "Marketing", "Dept ID": 1200 }, { "Name": "Rich Harris", "Location": "CO", "Extension": 4368, "Email": "rharris@company.com", "Title": "Principal Developer", "Department": "Engineering", "Dept ID": 1300 } // more data ] Click “Ok” and save button ( ) before close the dialog. 6. Wait for the validation to finish. If it doesn’t run validation, click the validation button to manually validate the pipeline. 7. Find the “Mapper” snap and drag it to after the JSON generator. The Mapper snap transforms incoming data with the specific mappings and produces new output data. 8. Click on the Mapper snap to open the configuration dialog. We focus on the five blocks at the bottom of the dialog. Input Schema - shows the schema of input data Mapping table - is the configuration to map from input data to new output data Target Schema -shows the schema of output data. But this snap hasn’t been validated yet, so it shows nothing. Input Preview - shows the current input data Output Preview - shows the current output data Next, set the mapping table with the information below. To add multiple mapping, click in the top right corner. Expression Target path $Name.split(' ')[0] $Firstname $Name.split(' ')[1] $Lastname $Email $Email $Title $Title $Location == "HQ" && $Department == "Marketing" $Trainging The finish configuration will look like this. Click save and close the dialog. 9. Click the preview button after the Mapper snap. The output should be like this. SnapGPT SnapGPT is an interactive tool inside SnapLogic Designer. It uses the power of LLMs to democratize integration by helping users create and manage integrations using natural language prompts. The SnapGPT can do six main functions in SnapLogic. Generate pipelines Describe pipelines Analyze pipelines Ask anything about the SnapLogic Intelligent Integration Platform (IIP) Generate SnapLogic expressions Create SQL queries Usage SnapGPT You can open the SnapGPT panel by clicking on the SnapGPT logo in the header bar. Then, the panel will be displayed with a welcome message. Next, we will show how to use each feature of SnapGPT on the SnapLogic platform. Generate pipelines Prompt direct to the SnapGPT Example prompts: Extract opportunity object records from Salesforce and add them to Snowflake Create a Pipeline using Salesforce Read to fetch my Opportunities, Filter out any opportunities outside of the last fiscal quarter, then write them to Snowflake. Extract opportunity object records from Salesforce closed before “2022-10-01” and add them to Snowflake. Create a pipeline that fetches my SnapLogic Activity Logs from the SnapLogic API. Describe pipelines Open the pipeline you want to describe, then go to the SnapGPT panel and ask, “Describe the pipeline.” Example prompts: Describe the pipeline Analyze pipelines Open the pipeline you want to analyze, then go to the SnapGPT panel and ask, “Analyze the pipeline.” Example prompts: Analyze the pipeline Result: SnapGPT identifies issues with the pipeline and makes suggestions for improvement, and Snaps in the pipeline offers suggestions. Ask anything about the SnapLogic Intelligent Integration Platform (IIP) Example prompts: How do I build a pipeline? When and how should I use the Salesforce SOQL snap? How can one pipeline call another pipeline? Can pipelines use recursion? How is an Ultra pipeline different from a regular pipeline? Generate SnapLogic expressions To begin, simply open a snap and select the icon. This action activates the expression generation feature, enabling me to assist you in creating expressions. I can start creating expressions for you either in our chat or inside the expression-enabled field itself by typing the prompt and then clicking on the SnapGPT icon . Example prompts: Generate an expression to filter my closed lost opportunities. Generate an expression to grab the current date and time. Create SQL queries Open a Snap that supports SQL or SOQL queries and open SnapGPT. For example, if you open the Salesforce SOQL Snap, the suggestion Create SQL query appears above the SnapGPT prompt. SnapGPT generates the query and displays it in the SQL Preview panel. You can review the generated SQL before applying to the snap. Example prompt: Generate a SQL query to get the total amount of opportunities closed within the last quarter grouped by the account's country and deal status.Revolutionizing Software Testing: How LLMs are Powering Automated Test Case and Data Generation
Tired of writing endless test cases and crafting complex test data manually? Discover how Large Language Models (LLMs) are transforming the QA landscape by automating test case and test data generation with remarkable accuracy and speed. In this article, we explore how LLMs—when paired with tools like SnapLogic Agent Creator—can accelerate testing cycles, boost coverage, and reduce QA efforts by up to 90%. Step into the future of intelligent, AI-driven software testingUnlocking the Power of LLMs with OpenAPI Tool Integration
Large Language Models (LLMs) are revolutionizing the way we interact with digital systems, from conversational agents to intelligent automation. But to truly harness their capabilities, especially in enterprise and developer ecosystems, it’s essential to bridge the gap between LLMs and external systems through tools—specifically APIs. This is where OpenAPI plays a pivotal role. What is OpenAPI? OpenAPI (formerly Swagger) is an open-source specification that defines a standard, machine-readable format for describing RESTful APIs. It enables developers and automated systems to understand an API’s structure—including endpoints, request parameters, authentication methods, and response types—without relying on traditional documentation or access to source code. Its adoption spans industries such as technology, finance, and healthcare, thanks to its interoperability with a wide array of tools and frameworks. Why OpenAPI Matters for LLMs Integrating OpenAPI with LLMs enhances their ability to interact with real-world systems. Here's how: Universal Interface: OpenAPI acts as a universal bridge to RESTful APIs, making it possible for LLMs to interact with services ranging from cloud infrastructure to productivity apps. Standardized Format: The standardized schema helps LLMs accurately interpret API functionality—including expected inputs and outputs—without ambiguity. Accelerated Tool Creation: Developers can efficiently build LLM-compatible tools by parsing OpenAPI definitions directly. Seamless Integration: With broad support from API tooling ecosystems, OpenAPI enables quick embedding of LLM agents into existing workflows. Supports Tool Calling: Tool calling allows LLMs to autonomously select and invoke relevant APIs based on user prompts—a key feature unlocked by structured OpenAPI descriptions. Enabling LLM Tool Calling with SnapLogic To connect LLMs with OpenAPI-defined tools, the OpenAPI Function Generator Snap plays a crucial role. This component converts any OpenAPI spec into a tool object that LLMs can use through the Tool Calling pipeline in SnapLogic. Input Options for the Generator Snap The generator supports multiple input methods: URL: Directly fetch the OpenAPI spec from a provided URL. Text Editor: Paste the raw spec into a built-in editor. Input Document: Pass the OpenAPI string as part of an input document via expression. File Upload: Select a spec file stored in the SLDB. Output Structure The generated tool output includes: sl_tool_metadata: Metadata such as security parameters, headers, and base URLs. json_schema: A schema of the input parameters. These tools can be passed into the Tool Calling Snap, which then resolves runtime variables like headers and endpoint URLs dynamically. Developers can chain this with an HTTP Client Snap to perform real API calls based on LLM outputs. Passing Through the Tool Calling Snap When the tool is passed through the Tool Calling Snap, it dynamically processes and resolves several key components using the metadata and user input: Resolved URL: The base URL and path parameters from the OpenAPI spec are combined with user-supplied values to generate the final API endpoint. Headers: Custom headers, or content-type headers are filled in based on the OpenAPI security definitions or context provided by the LLM. This resolved output makes it simple for downstream snaps (like HTTP Client) to directly execute the API call. Action tools with HTTP Client Snap Once the Tool Calling Snap generates the resolved tool data, this output can be piped directly into an HTTP Client Snap for execution: This setup effectively turns a static OpenAPI definition into a fully dynamic and executable workflow, allowing LLMs to autonomously interact with real services. Real-World Use Cases With the right configuration, LLMs can interact with virtually any OpenAPI-compliant service. This opens up a wide range of practical applications across productivity tools, developer APIs, data services, and more. Example Use Case: Load Products from FakeStore API and Save as CSV in GitHub Gist This example shows how an LLM can orchestrate a two-step integration using OpenAPI specs and tool calling via SnapLogic: Fetch Data: Retrieve product data from FakeStore API. Transform & Upload: Format the data as CSV and post it as a public GitHub Gist using GitHub’s Gist API. Main Pipeline (download) Loop Pipeline (download, github openapi file, fake store openapi file) Prompt to LLM: “Load all products from FakeStore API and upload them as a CSV file to GitHub Gist.” Pipeline Flow Breakdown Step 1: FakeStore API Tool Call OpenAPI Tool: FakeStore API spec (loaded via URL or file). LLM Task: Recognize the available /products endpoint and trigger a GET request to retrieve the full list of products. Tool Calling Snap Output: Resolved URL to https://fakestoreapi.com/products, method GET, no authentication needed. Step 2: GitHub Gist API Tool Call OpenAPI Tool: GitHub Gist API spec, with token-based authentication defined in sl_tool_metadata. LLM Task: Use the POST /gists endpoint, and construct the request body with: description: e.g., "FakeStore Products Export" public: true files: A JSON object with one file (e.g., "products.csv": { content: "<csv data>" }) Step 3: Summarize the Result LLM Task: Extract and present key details from the final Gist API response, such as: Total number of products exported Link to the created Gist (e.g., html_url) Confirmation message for the user Final Result:SnapLogic deployment on Kubernetes - A reference guide
Overview SnapLogic supports the deployment of Groundplexes on Kubernetes platforms, thus enabling the application to leverage the various capabilities of Kubernetes. This document explains a few best practice recommendations for the deployment of SnapLogic on Kubernetes along with a sample deployment example using GKE. The examples in this document are specific to the GKE platform however the concepts can be applied to other Kubernetes platforms such as AWS and Azure. Author: Ram Bysani SnapLogic Enterprise Architecture team Helm Chart A Helm chart is used to define the various deployment configurations for an application on Kubernetes. Additional information about Helm charts can be found here. The Helm chart package for a SnapLogic deployment can be downloaded from the Downloads section. It contains the following files: Artifact Comments values.yaml This file defines the default configuration for the SnapLogic Snaplex deployment. It includes variables like the number of JCC nodes, container image details, resource limits, and settings for Horizontal Pod Autoscaling (HPA). Reference: values.yaml Chart.yaml This file defines the metadata and version information for the Helm chart. templates folder This directory contains the Kubernetes manifest templates which define the resources to be deployed into the cluster. These templates are YAML files that specify Kubernetes resources with templating capabilities that allow for parameterization, flexibility, and reuse. templates/deployment.yaml This file defines a Kubernetes Deployment resource for managing the deployment of JCC instances in a cluster. The deployment is created only if the value of jccCount is greater than 0, as specified in the Helm chart's values.yaml file. templates/deployment-feed.yaml This file defines a Kubernetes Deployment resource for managing the deployment of Feedmaster instances. The deployment is conditionally created if the feedmasterCount value in the Helm chart's file values.yaml file is greater than 0. templates/hpa.yaml The hpa.yaml file defines a Horizontal Pod Autoscaler (HPA) resource for a Kubernetes application. The HPA automatically scales the number of pod replicas in a deployment or replica set based on observed metrics such as CPU utilization or custom metrics. templates/service.yaml The service.yaml file describes a Kubernetes service that exposes the JCC component of your Snaplex. It creates a LoadBalancer type service, which allows external access to the JCC components through a public IP address. The service targets only pods labeled as 'jcc' within the specified Snaplex and Helm release, ensuring proper communication and management. templates/service-feed.yaml The service-feed.yaml file describes a Kubernetes service that exposes the Feedmaster components. The service is only created if the value of feedmasterCount in the Helm chart’s values.xml file is > 0. It creates a LoadBalancer type service, which allows external access to the Feedmaster components through a public IP address. templates/service-headless.yaml The service-headless.yaml file describes a Kubernetes service for IPv6 communication. The service is only created if the value of enableIPv6 in the Helm chart’s values.xml file is set to true. Table 1.0 Helm Chart configurations Desired State vs Current State The configurations in the various yaml files (e.g. Deployment, HPA, values, etc.) represent the “Desired” state of a Kubernetes deployment. The Kubernetes controllers constantly monitor the Current state of the deployment to bring it in alignment with the Desired state. Horizontal Pod Autoscaling (HPA) Horizontal Pod Autoscaling (HPA) is a feature in Kubernetes that automatically adjusts the number of replicas (pods) for your deployments based on resource metrics like CPU utilization and memory usage. SnapLogic supports HPA for deployments in a Kubernetes environment. The add-on Metrics server must be installed. Reference: Metrics-Server. Metrics collection is enabled by default in GKE as part of Cloud Monitoring. Note that Custom Metrics and External Metrics, and Vertical Pod Autoscaling (VPA) are not supported for SnapLogic deployments on Kubernetes. Groundplex deployment in a GKE environment - Example In this section, we will go over the various steps for a SnapLogic Groundplex deployment in a GKE environment. Groundplex creation Create a new Groundplex from the Admin Manager interface. Reference: Snaplex_creation. The nodes for this Snaplex will be updated when the application is deployed to the GKE environment. New Snaplex creation GKE Cluster creation Next, we create the GKE cluster on the Google Cloud console. We have created our cluster in Autopilot mode. In this mode, GKE manages the cluster and node configurations including scaling, load balancing, monitoring, metrics, and workload optimization. Reference: GKE Cluster GKE cluster Configure the SnapLogic platform Allowlist Add the SnapLogic platform IP addresses to the Allowlist. See Platform Allowlist. In GKE, this is usually done by configuring an Egress Firewall rule on the GKE cluster. Please refer to the GKE documentation for additional details. Firewall rule - Egress Helm configurations values.yaml The below table explains the configurations for some of the sections from the values.yaml file which we have used in our set up. The modified files are attached to this article for reference. Reference: Helm chart configuration Section Comments # Regular nodes count jccCount: 3 # Feedmaster nodes count feedmasterCount: 0 This defines the number of JCC pods. We have enabled HPA for our test scenario, so the jccCount will be picked from the HPA section. (i.e. minReplicas and maxReplicas). The pod count is the number of pods across all nodes of the cluster. No Feedmaster pods are configured in this example. Feedmaster count can be half of the JCC pod count. Feedmaster is used to distribute Ultra task requests to the JCC pods. HPA configuration is only applicable to the JCC pods and not to the Feedmaster pods. # Docker image of SnapLogic snaplex image: repository: snaplogic/snaplex tag: latest This specifies the latest and most recent release version of the repository image. You can specify a different tag if you need to update the version to a previous release for testing, etc. # SnapLogic configuration link snaplogic_config_link: https://uat.elastic.snaplogic.com/api/1/rest/plex/config/ org/proj_space/shared/project Retrieve the configuration link for the Snaplex by executing the Public API. The config link string is the portion before ?expires in the output value of the API. Example: snaplogic_config_link: https://uat.elastic.snaplogic.com/api/1/rest/plex/config/ QA/RB_Temp_Space/shared/RBGKE_node1 # SnapLogic Org admin credential snaplogic_secret: secret/mysecret Execute the kubectl command: kubectl apply -f snapSecret.yaml Please see the section To create the SnapLogic secret in this document: Org configurations. # CPU and memory limits/requests for the nodes limits: memory: 8Gi cpu: 2000m requests: memory: 8Gi cpu: 2000m Set requests and limits to the same values to ensure resource availability for the container processes. Avoid running other processes in the same container as the JCC so that the JCC can have the maximum amount of memory. # Default file ulimit and process ulimit sl_file_ulimit: 8192 sl_process_ulimit: 4096 The value should be more than the # of slots configured for the node. (Maximum Slots under Node properties of the Snaplex). If not set, then the node defaults will be used. (/etc/security/limits.conf). The JCC process is initialized with these values. # JCC HPA autoscaling: enabled: true minReplicas: 1 maxReplicas: 3 minReplicas defines the minimum number of Pods that must be running. maxReplicas defines the maximum number of Pods that can be scheduled on the node(s). The general guideline is to start with 1:2 or 1:3 Pods per node. The replica Pods are across all nodes of a deployment and not per node. targetAvgCPUUtilization: 60 targetAvgMemoryUtilization: 60 To enable these metrics, the Kubernetes Metrics Server installation is required. Metrics collection is enabled by default in GKE as part of Cloud Monitoring. targetAvgCPUUtilization: Average CPU utilization percentage (i.e. 60 = 60%) This is the average CPU utilization across all Pods. HPA will scale up or scale down Pods to maintain this average. targetAvgMemoryUtilization: Average memory utilization percentage. This parameter is used to specify the average memory utilization (as a percentage of the requested memory) that the HPA should maintain across all the replicas of a particular deployment or stateful set. scaleDownStabilizationWindowSeconds: 600 terminationGracePeriodSeconds: 900 # Enable IPv6 service for DNS routing to pods enableIPv6: false scaleDownStabilizationWindowSeconds is a parameter used in Kubernetes Horizontal Pod Autoscaler (HPA) It controls the amount of time the HPA waits (like a cool-down period) before scaling down the number of pods after a decrease in resource utilization. terminationGracePeriodSeconds defines the amount of time Kubernetes gives a pod to terminate before killing it. If the containers have not exited after terminationGracePeriodSeconds, then Kubernetes sends a SIGKILL signal to forcibly terminate the containers, and remove the pod from the cluster. Table 2.0 - values.yaml Load balancer configuration The service.yaml file contains a section for the Load balancer configuration. Autopilot mode in GKE supports the creation of a Load balancer service. Section Comments type: LoadBalancer ports: - port: 8081 protocol: TCP name: jcc selector: A Load balancer service will be created by GKE to route traffic to the application’s pods. The external IP address and port details must be configured on the Settings tab of the Snaplex. An example is included in the next section of this document. Table 3.0 service.yaml Deployment using Helm Upload the helm zip file package to the Cloud Shell instance by selecting the Upload option. The default Helm package for SnapLogic can be downloaded from here. It is recommended to download the latest package from the SnapLogic documentation link. The values.yaml file with additional custom configurations (as described in Tables 2.0 / 3.0 above) is attached to this article. Execute the command on the terminal to install and deploy the Snaplex release with a unique name such as snaplogic-snaplex using the configurations from the values.yaml file. The release name is a unique identifier, and can be different for multiple deployments such as Dev / Prod, etc. helm install snaplogic-snaplex . -f values.yaml <<Output>> NAME: snaplogic-snaplex NAMESPACE: default STATUS: deployed REVISION: 5 TEST SUITE: None NOTES: You can run this command to update an existing deployment with any new or updated Helm configurations. helm upgrade snaplogic-snaplex . -f values.yaml View the deployed application under the Workloads tab on the Google Cloud Console. Workloads This command returns the HPA details. $ kubectl describe hpa Name: snaplogic-snaplex-hpa Namespace: default Labels: app.kubernetes.io/instance=snaplogic-snaplex app.kubernetes.io/managed-by=Helm app.kubernetes.io/name=snaplogic-snaplex app.kubernetes.io/version=1.0 helm.sh/chart=snaplogic-snaplex-0.2.0 Annotations: meta.helm.sh/release-name: snaplogic-snaplex meta.helm.sh/release-namespace: default Deployment/snaplogic-snaplex-jcc Metrics: ( current / target ) resource cpu on pods (as a percentage of request): 8% (153m) / 60% resource memory on pods (as a percentage of request): 28% (1243540138666m) / 60% Min replicas: 1 Max replicas: 3 Run the kubectl command to list the services. You can see the external IP addresses for the Load balancer service. kubectl get services NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE kubernetes ClusterIP 34.118.224.1 <none> 443/TCP 16d kubectl get services NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE kubernetes ClusterIP 34.118.224.1 <none> 443/TCP 16d snaplogic-snaplex-regular LoadBalancer 34.118.227.164 34.45.230.213 8081:32526/TCP 25m Update Load balancer url on the Snaplex Note the external IP address for the LoadBalancer service, and update the host and port on the Load balancer field of the Snaplex. Example: http://1.3.4.5:8081 Load balancer Listing pods in GKE The following commands can be executed to view the pod statuses. The pod creation and maintenance is fully managed by GKE. $ kubectl top pods $ kubectl get pods kubectl get pods --field-selector=status.phase=Running NAME READY STATUS RESTARTS AGE snaplogic-snaplex-jcc-687d87994-crzw9 0/1 Running 0 2m snaplogic-snaplex-jcc-687d87994-kks7l 1/1 Running 0 2m38s snaplogic-snaplex-jcc-687d87994-pcfvp 1/1 Running 0 2m24s View node details in the SnapLogic Monitor application Each pod represents a JCC node. The maxReplica value is set to 3 so you would see a maximum of 3 nodes (pods) deployed. (Analyze -> Infrastructure tab). Snaplex nodes The below command uninstalls and deletes the deployment from the cluster. All deployed services, metadata, and associated resources are also removed. helm uninstall <deployment_name> Pod registration with the SnapLogic Control Plane Scenario Comments How are the Pod neighbors resolved and maintained by the SnapLogic Control Plane? When a JCC/FeedMaster node (Pod) starts, it registers with the SnapLogic Control Plane, and the Control Plane maintains the list of Pod neighbors. When a JCC/FeedMaster node (Pod) registers, it also publishes its IP address to the Control Plane. An internal list of Pod IP addresses is updated dynamically for neighbor to neighbor communication. DNS resolution is not used. How are the container repository versions updated? The latest Snaplex release build is updated in the docker repository version tagged ‘latest’. The pods will be deployed with this version on startup by referencing the tags from the values.yaml file. If the Snaplex version is updated on the Control Plane to a different version (e.g. main-2872), then the JCC nodes (pods) will be updated to match that version (i.e. main-2872). Reference Groundplex Deployment on Kubernetes https://kubernetes.io/ GKE HPA
