AI, Data & Intelligence
ELT Development
Extract, Load, Transform (ELT) architecture using dbt, Snowflake, Databricks and BigQuery for cloud data warehousing.
Capability overview
What elt development involves
ELT development utilizes the massive parallel compute power of modern cloud data warehouses to transform data after loading. We build ELT architectures using Fivetran, Airbyte, Snowflake, BigQuery, Databricks and dbt.
We extract raw data directly into cloud data lakes or staging tables, applying modular SQL transformations inside the data warehouse.
Our ELT pipelines simplify data engineering workflows, accelerate data availability and provide full version control over data transformation models.
Throughout the elt development engagement, our engineering team works alongside your internal stakeholders to establish custom operational workflows, clear delivery milestones and automated validation gates. We focus on cloud elt pipeline architecture and dbt model engineering, ensuring that every component is documented, secure and aligned with your broader technology strategy. Furthermore, we establish continuous telemetry monitoring and iterative optimization roadmaps specifically tailored for your elt development infrastructure and raw data ingestion setup requirements.
We provide dedicated engineering oversight, automated telemetry tracking and structured technical handovers for cloud elt pipeline architecture and dbt model engineering, ensuring long-term operational resilience.
What is included
What the engagement covers
Cloud ELT Pipeline Architecture
Designing high-throughput ELT ingestion and transformation architectures on Snowflake, BigQuery or Databricks.
dbt Model Engineering
Building modular dbt data models, staging layers, mart tables and incremental materializations.
Raw Data Ingestion Setup
Configuring Airbyte, Fivetran or custom serverless ingestion connectors to load raw data streams.
Data Warehouse Optimization
Optimizing ELT query performance, cluster sizing, micro-partitioning and credit consumption.
How we work
How we deliver elt development
ELT Architecture Design
Designing staging schemas, raw data storage formats and transformation layer models.
Ingestion Connector Configuration
Setting up automated ingestion connectors to sync databases and SaaS APIs into staging tables.
dbt Model Authoring
Writing modular SQL transformation models in dbt with documentation and data quality assertions.
Automated Testing & CI/CD
Configuring automated dbt test runs and GitHub Actions CI/CD deployment pipelines.
Warehouse Performance Tuning
Reviewing query profiles, optimizing join strategies and tuning warehouse auto-suspend rules to control costs.
Related capabilities
Related capabilities in Data Engineering & Platforms
Data Warehousing
Cloud data warehouse design, star schema modeling, Snowflake and BigQuery implementation, and warehouse performance tuning.
Data Lakes
Building scalable cloud data lakes on AWS S3, Azure Data Lake and Google Cloud Storage for structured and unstructured data.
Data Lakehouse Architecture
Building modern data lakehouses using Databricks Delta Lake, Apache Iceberg and Snowflake for real-time analytics and AI.
Data Integration
Connecting disparate enterprise applications, databases, SaaS platforms and cloud systems into unified data pipelines.
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Questions & answers
Questions about ELT Development
Cannot find what you need? Our team responds to technical and commercial questions within one business day.
Ask a questionELT preserves raw data, utilizes scalable cloud warehouse compute, allows easy transformation updates and enables analysts using SQL.
ELT development is quoted as a fixed-fee implementation project or through recurring data engineering squad retainers.
We use dbt (Core & Cloud), Snowflake, Databricks, BigQuery, Fivetran, Airbyte, Meltano and Apache Airflow.
Next step
Discuss elt development with Acmez
Share what you need to change, build, integrate or support. We will map the practical next step.