ETL/ELT Development
Services on AWS
We build the transformation logic that turns raw, messy source data into clean, validated, analytics-ready datasets, whether that means transforming before load or after.
Moving data is only half the job. We handle the cleansing, mapping, and quality checks that decide whether your reports, dashboards, and models can actually be trusted.
What Is ETL/ELT Development?
ETL/ELT development is building the logic that transforms raw data into a clean, consistent, and trusted format before it's used for reporting, analytics, or AI. ETL (extract, transform, load) transforms data before it lands in its destination; ELT (extract, load, transform) loads raw data first and transforms it afterward, usually inside a warehouse or lake using SQL. Most teams end up using both, depending on the source and the destination. For a deeper look at when each approach fits, see our ETL vs. ELT breakdown.
This service covers the transformation layer specifically: cleansing, validation, schema mapping, and quality rules. If what you need is the scheduling and movement of data between systems, that's covered by our AWS data pipeline development services. The two are often scoped together, since a pipeline usually carries transformation logic as one of its steps.
Our ETL/ELT development services help you:
- Turn inconsistent, raw source data into a single trusted format
- Catch bad data before it reaches a dashboard or a model
- Map fields correctly across systems that don’t share a schema
- Apply business rules and calculations consistently, not case by case
- Track where a value came from and what changed it along the way
- Reduce the manual cleanup work analysts do before every report
We build both ETL and ELT patterns, choosing between them based on your data volume, source systems, and where the transformation is cheapest and fastest to run.
ETL/ELT Development Capabilities
Here's what our team builds, from a single transformation job to a full data quality framework across every pipeline you run.
1. Data Transformation & Cleansing
The core of any ETL/ELT job: turning inconsistent source data into a standard format your systems can actually use.
Capabilities:
- •Deduplication, standardization, and type casting
- •Field-level cleansing and normalization rules
- •Data enrichment from reference or lookup datasets
- •Handling of nulls, malformed records, and outliers
2. Data Validation & Quality Frameworks
Bad data that reaches a dashboard costs more to fix than bad data caught in transit. We build the checks that catch it first.
Capabilities:
- •Rule-based validation at each transformation step
- •Anomaly and outlier detection on incoming data
- •Automated data quality scoring and alerting
- •Rejection and quarantine handling for records that fail validation
3. Schema Mapping & Reconciliation
Source systems rarely agree on field names, types, or structure. We build the mapping layer that reconciles them into one consistent target schema.
Capabilities:
- •Source-to-target field mapping across systems
- •Schema drift detection and handling as sources change
- •Multi-source reconciliation into a single schema
- •Mapping documentation for audit and handover
4. Batch ETL Development
Traditional transform-before-load jobs for cases where data needs to be clean and validated before it's allowed to land anywhere.
Capabilities:
- •Scheduled batch transformation jobs at scale
- •Large-volume processing with distributed compute
- •Multi-step transformation chains with dependency handling
- •Historical backfill and reprocessing support
5. ELT & In-Warehouse Transformation
Modern load-then-transform pipelines, where raw data lands first and transformation happens afterward using SQL, closer to where it's consumed.
Capabilities:
- •SQL-based transformation inside the warehouse or lake
- •Version-controlled, testable transformation models
- •Incremental transformation for large, frequently updated tables
- •Transformation logic that’s easy for analysts to read and modify
6. Metadata & Data Lineage Tracking
For teams that need to answer "where did this number come from," we build lineage tracking into the transformation layer itself.
Capabilities:
- •Column-level lineage from source to destination
- •Transformation history and audit logging
- •Metadata tagging for discoverability
- •Lineage documentation for compliance and audit requests
How We Build ETL/ELT Pipelines
Discovery & Source-to-Target Mapping
We review your source systems, target schema, and the specific data quality problems you’re trying to fix, then map fields across the two.
Transformation Logic & Rule Design
We define cleansing rules, validation checks, and business logic before writing any code, so the rules are documented and reviewable.
Development & Automation
We build the transformation jobs, choosing ETL or ELT per source based on volume and where compute is cheapest to run.
Data Quality Testing & Validation
We test transformation output against source data and known edge cases, and confirm validation rules catch the failures they’re meant to.
Deployment & Monitoring
We deploy with logging and alerting on data quality failures, so a bad batch gets flagged before anyone reports off it.
Trusted Data, Not Just Moved Data
ETL/ELT Technologies We Use
Batch Transformation
- AWS Glue ETL
- Apache Spark / PySpark
- AWS EMR
Modern ELT & SQL Transformation
- dbt
- Amazon Redshift
- Amazon Athena
Validation & Quality
- AWS Glue Data Quality
- Python
Metadata & Lineage
- AWS Glue Data Catalog
ETL and ELT aren't competing standards, they're two ways to sequence the same work. Heavy, complex transformations on large source volumes usually run cheaper as ETL, before the data lands. Lighter, SQL-friendly transformations that analysts need to adjust often run better as ELT, inside the warehouse. We pick per source rather than forcing one pattern across your whole stack.
Industries We Build ETL/ELT Pipelines For
Healthcare & HealthTech
Validated, auditable transformation pipelines for clinical and claims data, with lineage tracking built in for compliance review.
Fintech & Banking
Transformation logic built for reconciliation accuracy and audit trails, where a wrong number in a report has real consequences.
SaaS & Technology
Cleansing and enrichment pipelines for product and usage data feeding analytics, billing, and customer-facing features.
E-commerce & Retail
High-volume transformation for transactional and behavioral data that stays accurate during peak traffic, not just in testing.
Enterprise Data Platforms
Reconciliation logic that maps data from multiple legacy systems and business units into one consistent, trusted schema.
Why Choose Eagle in Cloud for ETL/ELT Development?
Rules Documented Before Code
We define transformation and validation logic upfront and document it, so the rules survive a team handover instead of living only in someone’s script.
Both Patterns, Not One by Default
We choose ETL or ELT per source based on volume and cost, instead of defaulting to whichever pattern our team happens to prefer.
Data Quality Built In, Not Bolted On
Validation and quality checks are part of the pipeline from day one, not a monitoring layer added after something breaks.
Lineage You Can Actually Answer Questions With
We track column-level lineage so your team can trace a number back to its source, which matters most during an audit or a dispute.
End-to-End Delivery
From source mapping to transformation logic, testing, and deployment, we own the full build, or plug into your existing data team.
Ready to Turn Raw Data Into
Data You Can Trust?
Let's build the transformation logic that catches bad data before it reaches a dashboard, not after.
Let's Build Your ETL/ELT Pipeline