How would we create a reusable CDC function

Creating a Reusable CDC (Change Data Capture) Function in SnapLogic Great question! SnapLogic offers several approaches to building a reusable CDC pipeline. Here's a comprehensive guide: --- 🔄 What is CDC in SnapLogic? SnapLogic supports CDC natively through dedicated Snap Packs: | Snap Pack | Database | Capability | |---|---|---| | Oracle CDC | Oracle 19c, 9i | Captures changes via Oracle LogMiner | | SQL Server CDC | SQL Server 2022 | Reads from SQL Server transaction logs | Both support Insert, Update, and Delete change capture within a specified time range or via continuous polling. --- 🏗️ Recommended Architecture for a Reusable CDC Function 1. Build a Child Pipeline (CDC Core Logic) Encapsulate your CDC logic in a dedicated child pipeline: - Use the appropriate CDC Snap (Oracle CDC or SQL Server CDC) as the source - Add transformation/mapping logic (e.g., Mapper Snap) - Pass pipeline parameters for dynamic configuration such as: - Table name - Start/End timestamps - Database connection details 2. Use Pipeline Execute Snap to Call It From any parent pipeline, invoke the CDC child pipeline using the Pipeline Execute Snap: - Set the Pipeline field to the relative or absolute path of your CDC child pipeline - Pass parameters dynamically per execution - Choose the appropriate execution mode: - LOCAL_NODE – same node as parent (fastest) - LOCAL_SNAPLEX – any node in the same Snaplex - SNAPLEX_WITH_PATH – a specific Snaplex 3. Parameterize for Reusability Make the child pipeline flexible by using pipeline parameters (_paramName) for:

_tableName       → Target table to monitor
_startTime       → CDC window start
_endTime         → CDC window end
_pollInterval    → For polling mode

--- 📦 Make It a Pattern (Maximum Reusability) To share the CDC pipeline across teams and projects: 1. Create a Pattern Project in SnapLogic Manager - Navigate to your Project Space → Create Project → Check Pattern Project 2. Move or import your CDC pipeline into the Pattern Project 3. It will now appear in the Patterns Catalog in the Designer's Asset Palette 4. Any team member can search and reuse it without rebuilding from scratch --- ✅ Best Practices - 🔒 Store credentials in a shared Snap Account — don't hardcode them - 📁 Use relative paths in Pipeline Execute for portability across environments (Dev → QA → Prod) - 🔁 Enable "Reuse executions" in Pipeline Execute Snap if processing multiple documents for efficiency - 📋 Document pipeline parameters clearly so consumers know what to pass - 🚫 Avoid recursion — a pipeline cannot call itself --- 🗂️ Summary Flow

Parent Pipeline
    └── Pipeline Execute Snap
            └── CDC Child Pipeline (Reusable)
                    ├── Oracle CDC / SQL Server CDC Snap
                    ├── Mapper / Transform Snaps
                    └── Output to Target (DB, File, API, etc.)

By combining parameterized child pipelines with the Pattern Catalog, you get a fully reusable, shareable CDC function that can be dropped into any integration workflow. Would you like me to generate a starter CDC pipeline for you?

This is for redshift

It looks like you're working with Amazon Redshift in SnapLogic! Could you please provide more details about what you'd like to do? For example: - 🔄 Generate a pipeline to read from or write to Redshift? - 🔍 Query data from a Redshift table? - 📥 Load/insert data into Redshift? - 🔧 Configure a specific Snap for Redshift? - ❓ Ask a question about using Redshift with SnapLogic? Please share more details so I can assist you effectively! 😊

How would we create a reusable CDC function for redshift

Great question! While SnapLogic doesn't have a native Redshift-specific CDC Snap (unlike Oracle CDC or SQL Server CDC), you can absolutely build a reusable CDC pattern for Redshift using a combination of Snaps and SnapLogic's reusability features. Here's how: --- 🔄 Building a Reusable CDC Pipeline for Redshift 🧠 Core CDC Strategy for Redshift Since Redshift doesn't expose transaction logs directly to SnapLogic, CDC is typically implemented using one of these approaches: 1. Timestamp-based CDC – Track changes using a last_modified or updated_at column 2. Watermark/Bookmark pattern – Store the last processed timestamp or ID and query incrementally 3. SCD Type 2 (Slowly Changing Dimensions) – Use the built-in Redshift SCD2 Snap to track historical changes --- 🏗️ Recommended Pipeline Architecture Child Pipeline (Reusable CDC Core) Design this as a parameterized child pipeline that can be called by any parent: | Step | Component | Purpose | |------|-----------|---------| | 1 | Pipeline Parameters | Accept table_name, schema_name, watermark_column, last_run_timestamp | | 2 | Redshift Select | Query only changed records using WHERE updated_at > _last_run_timestamp | | 3 | Mapper | Normalize/transform the changed records | | 4 | Router | Route by operation type (INSERT / UPDATE / DELETE) if a change-type column exists | | 5 | Redshift Bulk Upsert / Insert / Update | Apply changes to the target table | | 6 | Redshift Execute | Update the watermark table with the latest processed timestamp | Parent Pipeline (Orchestrator) - Uses a Pipeline Execute Snap to call the child CDC pipeline - Passes table-specific parameters dynamically - Can loop over multiple tables using a ForEach Snap --- 🔁 Making It Reusable — Key Techniques 1. Pipeline Parameters Define parameters in the child pipeline such as: - _source_table - _target_table - _watermark_column - _last_run_ts - _schema_name 2. Watermark Table Maintain a small control table in Redshift (or SLDB) to store the last successful run timestamp per table:

sql
CREATE TABLE cdc_watermarks (
  table_name VARCHAR(255),
  last_run_ts TIMESTAMP
);

- Read the watermark at the start of each run - Update it after a successful load 3. Pipeline Patterns Catalog Once your CDC child pipeline is built and tested: - Save it in a Pattern Project in SnapLogic Manager - It becomes reusable from the Patterns Catalog in the Designer for any future CDC use case 4. SCD2 for Historical Tracking If you need full change history (not just latest state), use the Redshift SCD2 Snap paired with Redshift Bulk Upsert: - Natural keys remain constant - Changed fields trigger new rows with start_date, end_date, and active flags --- ✅ Best Practices - 🔒 Use Pipeline Parameters for all table/schema names to keep the child pipeline generic - 📅 Always update the watermark after a successful run to avoid reprocessing - ⚠️ Add error views on Redshift Snaps to handle failed records gracefully - 🔄 Schedule the parent pipeline using SnapLogic's task scheduler for automated incremental loads - 📦 Use Reuse Mode in the Pipeline Execute Snap for better performance when processing multiple tables --- Would you like me to generate a reusable CDC pipeline for Redshift based on any of these patterns? If so, let me know which approach fits your use case (timestamp-based, SCD2, etc.) and I can build it for you!

sure

It seems like you might be responding to a previous conversation, but I don't have any prior context here. 😊 Could you clarify what you'd like help with? For example: - 🔧 Build a pipeline – I can generate one for you - 📖 Learn about SnapLogic – I can answer questions or guide you - ✏️ Modify an existing pipeline – I can help refine it - 💡 Expression help – I can assist with SnapLogic expressions Just let me know how I can assist!