Snowflake services
Snowflake data engineering for clean, AI-ready data
HabileLabs works with Snowflake as part of its data engineering practice. We build ETL pipelines on Apache Spark and Kafka, clean, transform and schedule data workflows with Apache Airflow and dbt, and deliver clean, validated data to machine learning workflows.
- Snowflake with Apache Airflow and dbt
- Real-time ETL on Apache Spark and Kafka
- Workshops before any build
What we build
What Snowflake data engineering covers
Snowflake data engineering is the work around the warehouse: moving data in, transforming and scheduling it, and keeping it accurate and secure. Our data engineering practice covers four areas around it.
Pipelines into Snowflake
ETL pipelines that load data into Snowflake and keep it clean, validated and AI-ready, including real-time pipelines on Apache Spark and Kafka.
Data engineering servicesTransformation and orchestration
Data workflows cleaned, transformed and scheduled with Apache Airflow and dbt, for model accuracy.
AI strategy and data readinessData lakes and warehouses
Data lakes, data warehouses or hybrid models, designed around your business goals.
Database migrationData quality and governance
Frameworks that keep data accurate, complete and secure, so decisions rest on trusted information.
Data protection and governance
Snowflake is one of the platforms in our data practice, alongside Databricks, Microsoft Fabric and the AWS data stack.
Data readiness for AI
Making Snowflake data ready for AI and machine learning
A modern data platform builds the foundation, but it does not make data AI-ready on its own. Data quality, governance, lineage and access controls still need their own work.
Check the data each use case needs
We check the data each GenAI use case needs, then plan the pipelines that make it ready.
Orchestrate and transform it
Enterprise data pipelines orchestrated and transformed for GenAI, with Snowflake, Apache Airflow, dbt, Spark and Hadoop.
Deliver clean, validated data
Real-time ETL pipelines on Apache Spark, Kafka and Snowflake deliver clean, validated data to ML workflows.
Govern it
Frameworks for data quality, security and compliance, covering master data management, data cleansing and metadata management.
How it runs
How a Snowflake engagement runs
The six steps of our data engineering process, starting with workshops on your goals and your data.
01
Consultation and assessment
Workshops on your business goals and your data come first, so the approach fits your needs instead of a template.
02
Design and development
A data lake, data warehouse or hybrid model designed around your goals, with the pipelines that will feed it.
03
Implementation
Pipelines and transformations built and integrated with your existing systems and processes.
04
Testing and optimization
Pipelines tested rigorously, then monitored and optimized continuously.
05
Scalability and future-proofing
An architecture built to scale with growth and new technology.
06
Ongoing support
Support and maintenance after launch, so the infrastructure keeps delivering value.
Keep exploring
Other platforms and services in our data practice
Microsoft Fabric and Power BI
Data brought into one place on Microsoft Fabric, modeled once, with dashboards that refresh on their own.
Power BI and FabricData engineering
Data architecture, integration and ETL pipelines, big data processing and real-time streaming on AWS, Azure and Google Cloud.
Data engineering servicesDatabase migration and modernization
Legacy and self-managed databases moved to managed platforms, with the data intact.
Database migrationTableau
Tableau dashboards and BI, from the team that also builds on Power BI and Amazon QuickSight.
Tableau services
Snowflake FAQs
Snowflake data engineering, answered
What we build on Snowflake, how it fits with our other platforms, and how a project starts.
Yes. Snowflake is one of the data platforms we work with, alongside Databricks, Microsoft Fabric and the AWS data stack. We use it with Apache Airflow and dbt to clean, transform and schedule data workflows, and with Apache Spark and Kafka for real-time ETL pipelines.
ETL pipelines into Snowflake, transformation and orchestration with Apache Airflow and dbt, data lake, data warehouse or hybrid model design, and data quality and governance frameworks that keep data accurate, complete and secure.
We check the data each use case needs and plan the pipelines that make it ready for GenAI. Those pipelines deliver clean, validated data to ML workflows, and data quality, governance, lineage and access controls get their own work. HabileLabs offers a free AI readiness assessment.
It depends on your data, your use cases and the platforms you already run, which is why we start with workshops instead of a template. Databricks is the platform we work with most, and HabileLabs is a Databricks partner. We also work with Snowflake, Microsoft Fabric and the AWS data stack.
HabileLabs is a Databricks partner and a Microsoft Solutions Partner for Data & AI. On AWS, HabileLabs is an AWS Partner, Advanced Tier Services, and holds the Amazon RDS Service Delivery validation.
With workshops on your business goals and your data. We then design the target, build and integrate the pipelines, test and optimize them, and support the platform after launch. You can start with a free consultation.
Next step
Planning pipelines into Snowflake?
Tell us where your data sits today, what needs to reach Snowflake and what it should feed: reports, models or AI. We start with a workshop on your goals and your data.
- Workshops before any build
- Pipelines with Apache Airflow and dbt
- Clean, validated data for ML workflows