Extract. Transform. Load. Empower.
ETL is the backbone of modern data architecture. It's how you get data from disparate sources—databases, APIs, files, streams—into a form that drives analytics, reporting, and machine learning.
Our ETL services build robust, scalable data pipelines that handle any volume, any velocity, and any variety. Whether you need batch processing, real-time streaming, or complex transformations, we deliver data you can trust.
300+
Pipelines Built50+
Data Sources10TB+
Daily Throughput99.9%
Data Accuracy
ETL Capabilities
Comprehensive data integration solutions
Data Extraction
Pull data from any source: databases, data warehouses, cloud apps, APIs, flat files, streaming platforms, and more.
- Batch & Real-time
- Incremental Extraction
- Change Data Capture
- API Integration
Data Transformation
Clean, enrich, and reshape data with powerful transformations—from simple mapping to complex business logic.
- Data Cleansing
- Aggregations
- Business Logic
- Data Validation
Data Loading
Load transformed data into target systems: data warehouses, data lakes, databases, or applications.
- Full & Incremental Loads
- Upsert/Merge
- Schema Evolution
- Partitioning
Real-time Streaming
Process streaming data from Kafka, Kinesis, Event Hubs, and more for real-time analytics and action.
- Stream Processing
- Windowing
- Enrichment
- Real-time Dashboards
Orchestration & Scheduling
Automate and monitor your data pipelines with robust orchestration, error handling, and alerting.
- Dependency Management
- Retry Logic
- Monitoring
- Alerting
Data Quality & Governance
Ensure data quality with validation rules, anomaly detection, and comprehensive data lineage.
- Data Validation
- Anomaly Detection
- Data Lineage
- Audit Trails
ETL vs. ELT: Choosing the Right Approach
We help you decide based on your data architecture and needs
📤 ETL (Extract, Transform, Load)
Traditional approach where data is transformed before loading into the target system. Best for:
- Complex transformations requiring significant compute
- Regulatory/compliance requirements
- Legacy data warehouses
- When target system has limited processing power
📥 ELT (Extract, Load, Transform)
Modern approach where data is loaded first, then transformed in the target system. Best for:
- Cloud data warehouses (Snowflake, BigQuery, Redshift)
- Massive data volumes
- Agile, iterative development
- When you need raw data for multiple use cases
We're agnostic—we implement the approach that fits your architecture, whether it's ETL, ELT, or a hybrid.
Connect to Anything
Wide range of supported sources and destinations
📡 Common Data Sources
- Oracle
- SQL Server
- MySQL
- PostgreSQL
- MongoDB
- IBM DB2
- Salesforce
- SAP
- Marketo
- HubSpot
- Shopify
- Flat Files (CSV, JSON, XML)
🎯 Common Destinations
- Snowflake
- Amazon Redshift
- Google BigQuery
- Azure Synapse
- Databricks
- PostgreSQL
- MySQL
- Amazon S3
- Azure Blob
- Google Cloud Storage
Our ETL Development Process
Building robust, maintainable data pipelines
We follow engineering best practices to build ETL pipelines that are reliable, scalable, and easy to maintain.
Requirements & Discovery
We understand your data sources, business logic, target systems, and SLAs. We define data quality rules and success metrics.
Architecture Design
We design the pipeline architecture—batch vs. streaming, ETL vs. ELT, tool selection, and error handling strategy.
Pipeline Development
We build extraction, transformation, and loading logic with modular, reusable components and comprehensive error handling.
Testing & Validation
We test with sample and full data volumes, validate transformations, and ensure data quality meets requirements.
Deployment & Orchestration
We deploy pipelines to production, set up scheduling, monitoring, and alerting.
Monitoring & Optimization
We monitor performance, optimize for speed and cost, and evolve pipelines as requirements change.
Success Stories
Real results from our ETL implementations
Real-time Inventory Pipeline
Built a streaming ETL pipeline processing 10M+ daily inventory updates from 500+ stores into a central data lake for real-time analytics.
Regulatory Reporting Pipeline
Developed a complex ETL pipeline consolidating data from 20+ source systems for regulatory reporting, reducing reporting time from weeks to hours.
Clinical Data Integration
Built HIPAA-compliant ETL pipelines integrating EHR, lab, and claims data into a research data warehouse, enabling advanced analytics.
Tools & Technologies
Industry-leading ETL tools and platforms
Informatica
Talend
SSIS
dbt
Fivetran
Stitch
Airflow
Prefect
Dagster
Kafka
Spark
Flink
Ready to Build Your Data Pipelines?
Let's discuss how our ETL expertise can help you integrate, transform, and operationalize your data.
Frequently Asked Questions
Common questions about ETL services
ETL stands for Extract, Transform, Load. It's a process that extracts data from source systems, transforms it (cleans, enriches, aggregates), and loads it into a target system like a data warehouse.
In ETL, data is transformed before loading into the target. In ELT, data is loaded first and transformed within the target system. ELT is common with modern cloud data warehouses that have powerful processing capabilities.
We use distributed processing frameworks (Spark, Flink), incremental extraction, parallel loading, and partitioning. We optimize pipelines for both performance and cost.
Yes, we build real-time streaming pipelines using Kafka, Kinesis, and stream processing engines for use cases requiring sub-second latency.
We implement data validation rules, anomaly detection, and reconciliation. We also maintain data lineage for auditability and debugging.
Absolutely. We offer maintenance, monitoring, and optimization services to ensure your pipelines continue to perform as data volumes and requirements evolve.