Who should use the Data Aggregation workflow?
Teams or solo builders working on marketing tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · Marketing
Practical execution plan for data aggregation with clear steps, mapped tools, and delivery-focused outcomes.
Deliverable outcome
A fully documented, monitored, and maintainable data aggregation pipeline.
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
A fully documented, monitored, and maintainable data aggregation pipeline.
Use each step output as the input for the next stage
Step map
Instead of relying on a single generic AI model, this pipeline connects specialized tools to maximize quality. First, you'll use Gemini for Google Workspace (formerly Duet AI) to a complete inventory of all data sources with field mappings and access credentials ready. Then, you pass the output to Pipedream to all data sources are being extracted automatically on a schedule, with error alerts active. Then, you pass the output to Morph to clean, standardized data ready for merging, with duplicates removed and formats consistent. Then, you pass the output to dbt Cloud (AI-Powered) to a single, unified dataset containing all customer interactions from all sources. Then, you pass the output to Tableau AI to interactive dashboards displaying aggregated marketing metrics, updated automatically. Then, you pass the output to Airbyte AI to aggregated data updated within seconds of source changes, enabling real-time marketing triggers. Finally, Datadog is used to a fully documented, monitored, and maintainable data aggregation pipeline.
Identify and Map Data Sources
A complete inventory of all data sources with field mappings and access credentials ready.
Set Up Data Extraction Pipelines
All data sources are being extracted automatically on a schedule, with error alerts active.
Clean and Normalize Incoming Data
Clean, standardized data ready for merging, with duplicates removed and formats consistent.
Merge and Unify Data into a Single View
A single, unified dataset containing all customer interactions from all sources.
Build Aggregation Views and Dashboards
Interactive dashboards displaying aggregated marketing metrics, updated automatically.
Implement Real-Time Data Sync (Optional)
Aggregated data updated within seconds of source changes, enabling real-time marketing triggers.
Document and Maintain the Aggregation Pipeline
A fully documented, monitored, and maintainable data aggregation pipeline.
List all systems containing customer or marketing data (CRM, email platform, ad platforms, website analytics, support tickets). For each source, document the data fields, update frequency, and access method (API, CSV export, database connection). This ensures you know what data exists and where it lives before attempting to move it.
Why Gemini for Google Workspace (formerly Duet AI): Gemini for Google Workspace provides automated table generation and semantic data classification, which directly supports identifying and mapping data sources within Google Sheets, and integrates with Google Workspace's secure credential management.
Configure automated extraction jobs for each source using ETL tools or custom scripts. For APIs, set up scheduled pulls (e.g., daily at 2 AM) with pagination handling. For flat files, set up a monitored drop folder. Test each extraction with a small sample to confirm data arrives correctly.
Why Pipedream: Pipedream is designed for API event processing and data pipeline ETL, making it ideal for setting up extraction pipelines with Python, cron, and Slack webhooks.
Apply data cleaning rules: remove duplicates, standardize date formats, fix encoding issues, and handle missing values. Normalize fields like phone numbers, country codes, and email addresses to a consistent format. This step prevents garbage-in-garbage-out in downstream aggregation.
Why Morph: Morph specializes in data cleaning, integration, and transformation, directly matching the need to clean and normalize incoming data.
Join cleaned data from all sources using the unified schema and a common identifier (e.g., customer email or ID). Use a data warehouse or data lake as the central repository. For real-time needs, set up a streaming merge (e.g., Kafka + materialized view). This creates the aggregated dataset.
Why dbt Cloud (AI-Powered): dbt Cloud (AI-Powered) provides automated SQL generation and semantic layer definition, which directly supports merging and unifying data into a single view using SQL and dbt.
Create pre-aggregated summary tables (e.g., customer lifetime value, total orders per customer, last touchpoint) for fast querying. Connect these to a BI tool to build dashboards that show key metrics like total revenue, churn risk, or campaign attribution. This makes the data actionable.
Why Tableau AI: Tableau AI provides data analysis, visualization, and predictive modeling, directly supporting the creation of aggregation views and dashboards.
If real-time aggregation is needed, set up streaming pipelines using tools like Kafka, AWS Kinesis, or Google Pub/Sub. Configure change data capture (CDC) from source databases and stream into a real-time view (e.g., Redis or materialized view in ClickHouse). This step is optional for most marketing use cases.
Why Airbyte AI: Airbyte AI supports vector database synchronization and automated data chunking, which can be adapted for real-time data sync pipelines.
Write clear documentation for each step: source schemas, transformation logic, merge rules, and dashboard definitions. Set up monitoring for data freshness and quality (e.g., row count anomalies). Schedule periodic reviews to add new sources or adjust mappings as business needs evolve.
Why Datadog: Datadog provides infrastructure monitoring, log aggregation, and analysis, directly supporting the monitoring needs for maintaining the aggregation pipeline.
§ Before you start
Teams or solo builders working on marketing tasks who want a repeatable process instead of one-off tool experiments.
No. Start with the top pick for each step, then replace tools only if they do not fit your pricing, compliance, or output needs.
Open the mapped task page and compare top options side by side. Prioritize output quality, integration fit, and predictable cost before scaling.
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