One Data Foundation.
Engineered With AI.
Bring company-wide data into centralized data lakes and warehouses. Rudder Analytics engineers use AI to build pipelines, clean data, and strengthen testing. Reporting, operations, and AI initiatives run on a shared, reliable foundation.
Raw and curated data, with governed access
Consistent models for reporting and BI
Prepared datasets for business workloads
Bring structured records, documents, and other data together while retaining raw data for future use.
Organize validated data into consistent models for financial, operational, and management reporting.
Combine the flexibility of a data lake with managed tables for analytics where a lakehouse fits the workload.
Company-Wide Data Engineering
From solution architecture and technology selection to migration and ongoing operations. AI supports the engineering work across the stack, with scope tailored to the business.
Data Platform Architecture
Design data lakes, warehouses, and lakehouses around data volume, access needs, and cost. Use AI to develop architecture drafts and model options for engineering review.
Pipeline Development
Connect business systems through managed connectors and custom code. Apply AI to SQL and Python development, source mappings, and troubleshooting across ETL and ELT workflows.
AI-Driven Data Cleaning
Classify records, standardize inconsistent values, and flag missing or conflicting attributes. Combine AI suggestions with business rules and review for ambiguous cases.
Migration & Modernization
Plan lift-and-shift migrations for compatible workloads or rework pipelines for a new platform. AI assists code analysis and conversion; reconciliation and rollback plans govern cutover.
Performance & Cost
Improve slow processing, inefficient queries, and storage use. Use AI to investigate bottlenecks, then benchmark changes against the same workload before adoption.
Governance & Operations
Establish access controls, lineage, monitoring, and release practices. Use AI to support incident analysis and documentation within the approved data-handling environment.
Consistent Data
Across Every SKU
Product data arrives with different category names, incomplete attributes, and conflicting formats. Those inconsistencies carry through to catalog operations and category-level reporting.
AI can propose a category, subcategory, and product template for each SKU. It can also standardize attribute values against an approved taxonomy and identify gaps across the fields required by that template.
Business rules validate the result. Uncertain mappings go to review, and missing facts stay flagged rather than being invented.
Product Record Checks
For a catalog with 12–15 required attributes per product type.
| Field | Incoming Value | Proposed Action |
|---|---|---|
| Category | Men’s tees | Map to Apparel → Tops → T-shirts |
| Template | General | Apply the T-shirt attribute schema |
| Color | navy / Navy Blue | Standardize to Navy Blue |
| Material | Not supplied | Flag for completion |
Approved mappings can become reusable rules. Exceptions retain a clear review trail.
Reliable Data. Tested Performance.
A completed pipeline run is only the starting point. Engineers use AI to draft and extend tests, then check that the data is correct, arrives on time, and remains reliable under load.
Measure improvements against the current workload: processing time, data freshness, exception rates, and compute and storage costs.
Data Accuracy
Reconcile source and target data. Test transformations, category mappings, duplicates, and required fields.
Performance
Measure throughput and end-to-end latency at expected and peak load. Verify reporting deadlines.
Recovery
Test connection failures, retries, schema changes, and safe reprocessing without duplicate records.
Access & Traceability
Verify permissions, sensitive-data handling, and the ability to trace changes from source to output.
Cloud & Data Platform Ecosystem
Data platforms across AWS, Microsoft Azure, and Google Cloud (GCP). Databricks, Snowflake, and Apache technologies support storage, processing, and analytics according to the architecture.
Connectors & Integration
Fivetran, Stitch, and Talend for supported sources and destinations. Custom API integrations for business applications, including Pipefy workflows.
Transformation & Orchestration
SQL, Python, PySpark, dbt, and Apache Airflow for custom processing, dependencies, scheduling, and repeatable data delivery.
Technology Fit
Build on tools that already serve the business. Evaluate new platforms for integration coverage, security, capacity, and total operating cost.
Project Delivery & Ongoing Support
Choose a focused project, a complete data platform, or continuing engineering support. Define the responsibilities, handover, and measures of success at the outset.
Focused Projects
A source integration, catalog-cleaning workflow, lift-and-shift migration, or performance issue.
Receive the implementation, test results, and documentation your team needs to run it.Complete Data Platforms
Company-wide architecture, tech stack, data lake, warehouse, and the pipelines that connect them.
Receive the architecture, configured platform, connected pipelines, and operating guidance.Ongoing Engineering
Maintenance, incident support, new integrations, and capacity or cost improvements as requirements evolve.
Keep a prioritized improvement plan, current operating instructions, and regular performance reviews.Your Next Data Priority
Centralize company data, improve its quality, or move to a new platform. Start with the requirement and define the engineering scope around it.
