Data Engineering

ETL/ELT Pipeline Development

Reliable Data Pipelines That Scale With Your Business

Design and implement production-grade ETL/ELT pipelines that automate data extraction, transformation, and loading. Built with comprehensive error handling, monitoring, and data quality validation to ensure reliable data flow across your organization.

Batch & Real-Time ProcessingAutomated Error RecoveryData Quality ValidationComprehensive Monitoring
80+
Pipelines Built
100TB+/day
Data Processed
99.9%
Uptime
97%
Client Satisfaction

What is ETL/ELT Pipeline Development?

Foundation for modern data operations

ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) pipelines are the backbone of modern data infrastructure. They automate the movement and transformation of data from source systems to destinations like data warehouses, data lakes, and analytics platforms.

Our ETL/ELT pipeline development focuses on building robust, scalable systems that handle your data processing needs reliably. We design pipelines that process data in batches or in real-time, depending on your business requirements.

Every pipeline we build includes comprehensive error handling, retry logic, and monitoring to ensure data flows consistently and issues are caught before they impact downstream systems. We implement data validation at every stage to maintain data quality throughout the process.

Why Choose DevSimplex for ETL/ELT Pipelines?

Production-grade pipelines built for reliability

Building ETL/ELT pipelines that work in development is easy. Building pipelines that run reliably in production at scale is hard. We bring experience from hundreds of production pipeline implementations to every project.

Our pipelines are designed for failure from the start. We implement retry logic, dead-letter queues, and comprehensive error handling so that when issues occur-and they will-the system recovers gracefully without data loss.

We use modern orchestration tools like Apache Airflow and Prefect, combined with processing frameworks like Spark and cloud-native services. This gives you pipelines that are maintainable, observable, and can evolve with your changing requirements.

Requirements & Prerequisites

Understand what you need to get started and what we can help with

Required(3)

Data Source Access

Access credentials and network connectivity to all source systems.

Target System Setup

Data warehouse or destination system configured and accessible.

Data Requirements

Documentation of expected data formats, volumes, and refresh frequencies.

Recommended(2)

Business Rules

Transformation logic and business rules for data processing.

Historical Data

Sample historical data for testing and validation.

Common Challenges & Solutions

Understand the obstacles you might face and how we address them

Data Quality Issues

Bad data propagating to downstream systems causing incorrect analytics.

Our Solution

Implement validation checks at extraction, transformation, and load stages with automated alerting.

Pipeline Failures

Data delays impacting business operations and decision-making.

Our Solution

Design for failure with retry logic, dead-letter queues, and automated recovery procedures.

Scale Limitations

Pipelines unable to handle growing data volumes.

Our Solution

Distributed processing with Spark and auto-scaling infrastructure.

Your Dedicated Team

Meet the experts who will drive your project to success

Data Engineer

Responsibility

Designs and implements pipeline architecture and transformations.

Experience

5+ years data engineering

DevOps Engineer

Responsibility

Sets up infrastructure, monitoring, and deployment automation.

Experience

Cloud platform certified

Data Analyst

Responsibility

Validates data quality and business logic correctness.

Experience

3+ years analytics

Engagement Model

Dedicated team through implementation, ongoing support available.

Success Metrics

Measurable outcomes you can expect from our engagement

Pipeline Uptime

99.9%

Reliable data delivery

Typical Range

Processing Speed

10x faster

With distributed processing

Typical Range

Error Detection

< 5 min

Time to detect issues

Typical Range

Data Quality

99.5%+

Validation pass rate

Typical Range

ETL Pipeline ROI

Automated pipelines reduce manual effort and improve data reliability.

Manual Effort

80% reduction

Within Immediate

Data Freshness

Real-time to hourly

Within Post-deployment

Data Quality Issues

95% reduction

Within First quarter

“These are typical results based on our engagements. Actual outcomes depend on your specific context, market conditions, and organizational readiness.”

Why Choose Us?

See how our approach compares to traditional alternatives

AspectOur ApproachTraditional Approach
Reliability

Built-in retry logic and error handling

Self-healing pipelines that recover automatically

Manual intervention required

Scalability

Distributed processing with auto-scaling

Handle 100x data growth without redesign

Single-node processing limits

Monitoring

Comprehensive observability built-in

Proactive issue detection and resolution

Basic logging only

Technologies We Use

Modern, battle-tested technologies for reliable and scalable solutions

Apache Airflow

Workflow orchestration

Apache Spark

Distributed processing

Python

Pipeline development

SQL

Data transformation

AWS Glue

Serverless ETL

Ready to Get Started?

Let's discuss how we can help you with data engineering.