DataZ — Data Engineering & DataOps
DataZ — Enterprise DataOps for the Modern Data Stack
Governed – AI-Ready – Production-Grade
Enterprise-grade DataOps for the modern data stack. From Lakehouse architecture and real-time streaming to AI-ready pipelines and data mesh, DataZ operationalizes your data with the discipline of platform engineering.
What is DataZ
Software Engineering Discipline for Data
DataZ brings software engineering discipline to data — combining GitOps-driven CI/CD, Apache Airflow orchestration, dbt transformations, real-time streaming, and full-stack observability into a unified DataOps practice. We help enterprises build production-grade, AI-ready data platforms on Snowflake, Databricks, and cloud-native Lakehouse architectures — with the governance and reliability of modern platform engineering.
Lakehouse · DataOps · AI-Ready
Supported Platforms
Key Capabilities
Full-Stack DataOps Engineering
DataZ covers every layer of the modern data stack — from Lakehouse architecture and real-time streaming to AI-ready pipelines and enterprise governance.
Architecture
Lakehouse & Modern Data Stack
- Design and deploy Lakehouse architectures on Snowflake, Databricks, and Delta Lake with Apache Iceberg open table formats
- Implement medallion architecture (Bronze/Silver/Gold layers), data vault 2.0 modeling, and semantic layers
- Integrate dbt Core / dbt Cloud for modular, tested, version-controlled SQL transformations with full model lineage via Astronomer Cosmos
DataOps
GitOps CI/CD for Data
- True CI/CD for data artifacts using GitLab, GitHub Actions, or Azure DevOps with feature-branch workflows
- Blue/green pipeline deployments with zero-downtime rollouts, automated rollback, and merge-request-based approval workflows
- Environment-gated promotions across DEV, QA, and PROD with full audit trails
Streaming
Real-Time Streaming & Event-Driven Pipelines
- Build real-time data pipelines with Apache Kafka, Azure Event Hubs, and Snowflake Dynamic Tables
- Implement Change Data Capture (CDC) from MSSQL, PostgreSQL, and Oracle into Snowflake Streams and Tasks
- Support hybrid batch and streaming patterns with Kafka Connect, KSQL, and event-driven Airflow DAG triggers via the REST API
Quality
Data Quality & Observability
- Data quality as code with Great Expectations, dbt tests, and custom Airflow callbacks
- Full pipeline observability via OpenLineage, Marquez, and OpenTelemetry into federated Prometheus with Grafana dashboards
- End-to-end data lineage from source to BI layer with alerting through Alert Manager to PagerDuty, Slack, and MS Teams
AI/ML
AI-Ready & ML Pipeline Engineering
- Build feature engineering pipelines for ML workloads using Snowpark, Databricks Feature Store, and Azure ML
- Orchestrate model training, validation, and inference pipelines via Airflow with MLflow experiment tracking
- Integrate vector embeddings and RAG pipelines for LLM-powered data products at production scale
Governance
Data Governance & Mesh Enablement
- Implement data contracts between producers and consumers with schema registries and automated compatibility checks
- Enable data mesh principles with domain-oriented, self-serve data product ownership
- Integrate Unity Catalog and Snowflake RBAC for column-level security, row-level filtering, and full compliance audit trails
Modern Data Stack
What We Deliver Across the Modern Data Stack
Snowflake Optimization
Airflow & Orchestration
dbt Transformation Layer
Streaming & CDC
Business Outcomes
A Data Platform That Delivers Measurable Results
From faster pipeline releases to AI-ready data products — DataZ turns data infrastructure into a competitive advantage.
Faster Data Product Delivery
GitOps-driven CI/CD cuts pipeline release cycles from weeks to hours — data engineers ship with the confidence of a software engineering team.
Trustworthy, Governed Data
Data contracts, schema validation, and automated dbt tests catch quality issues before production. Full lineage means every stakeholder knows exactly where their data comes from.
Optimized Cloud Spend
Auto-scaling Airflow workers, Snowflake credit governance, right-sized AKS pods, and monthly billing reviews eliminate waste across the entire data infrastructure.
AI and Analytics-Ready
Curated, tested, and versioned data products power ML models, LLM pipelines, and self-service BI without additional data preparation cycles.
Compliance by Design
Column-level security, row-level filtering, OKTA SSO, Azure Key Vault, and automated audit trails ensure regulatory compliance without slowing down data teams.
Scalable Self-Service Platform
Data mesh principles enable domain teams to own and publish their own data products with guardrails — without central bottlenecks or platform team dependency.
Industries
Built for Data-Intensive Industries
DataZ is trusted by teams where data quality, security, and speed directly impact business outcomes.
Financial Services
ESG, enterprise data, and trade allocation pipelines on Snowflake with full GitOps CI/CD, blue/green deployments, and audit-ready governance.
Healthcare
HIPAA-compliant data pipelines with automated quality gates, end-to-end lineage tracking, and fine-grained access controls for patient data.
E-commerce & Retail
Real-time personalization and inventory pipelines powered by streaming CDC from transactional systems into Snowflake and Databricks analytics layers.
Technology & AI
Feature engineering pipelines, ML model orchestration, vector embeddings, and LLM-powered data products with full lineage and version control.
Client Spotlight
Real Results from DataZ Deployments
eCloudControl deployed DataZ, establishing a full GitOps CI/CD pipeline for Apache Airflow and dbt Core with Astronomer Cosmos. Over 200+ production tasks spanning ESG, enterprise data, and trade allocation domains were onboarded within weeks. Snowflake Streams and Tasks replaced batch jobs, cutting data latency from hours to minutes. A full observability stack with OpenTelemetry, Prometheus, and Grafana gave operations real-time visibility for the first time. Blue/green deployments eliminated downtime during model updates. Azure Key Vault-backed secret management and RBAC ensured every environment was secure and audit-ready.
Head of Data Engineering
Global Financial Services Firm
With DataZ, our Snowflake environment finally has full governance, end-to-end lineage, and real-time observability. Blue/green deployments eliminated downtime during model updates. We went from PoC to production in weeks — not quarters.
VP of Data Platform
Enterprise Data Modernization Program
Pricing & Engagement
Fixed-Price Start. Consumption-Based Scale.
Every DataZ engagement begins with a fixed-price discovery workshop — we audit your data estate, map pipeline bottlenecks, and deliver a migration blueprint in 5 days. Ongoing DataOps delivery is consumption-based, scaling with your data volumes and team size. No long-term lock-in. Teams migrating infrastructure alongside their data stack can pair DataZ with AppZ cloud migration; for ongoing 24/7 operations, ManageZ managed SRE takes over post-delivery.
Get a quoteAlso in the eCloudControl Platform
Common Questions
DataZ — Frequently Asked Questions
- What is DataZ and what data platforms does it support?
- DataZ is eCloudControl's enterprise DataOps platform that brings software engineering discipline to data. It supports Snowflake, Databricks, Delta Lake, Apache Airflow, dbt Core, Apache Kafka, Azure Data Factory, ADLS Gen2, MS SQL Server, and 14+ modern data stack tools. DataZ delivers Lakehouse architecture, GitOps CI/CD for data pipelines, real-time streaming, and AI-ready data products.
- How does DataZ implement Lakehouse architecture?
- DataZ implements medallion architecture (Bronze/Silver/Gold layers) using Snowflake, Databricks, or Delta Lake with Apache Iceberg open table formats. Data Vault 2.0 modeling and semantic layers are included. dbt Core or dbt Cloud handles modular, version-controlled SQL transformations with full model lineage tracked via Astronomer Cosmos — so every data product is tested, traceable, and reproducible.
- What is DataZ's approach to data governance and compliance?
- DataZ implements data contracts between producers and consumers with schema registries and automated compatibility checks. Unity Catalog and Snowflake RBAC provide column-level security, row-level filtering, and full compliance audit trails. This supports HIPAA, GDPR, PCI DSS, and SOC 2 requirements. Every pipeline action is version-controlled with environment-gated promotion across DEV, QA, and PROD.
- How does DataZ handle real-time streaming and Change Data Capture?
- DataZ builds real-time pipelines with Apache Kafka, Azure Event Hubs, Debezium CDC, and Snowflake Dynamic Tables. Change Data Capture (CDC) from MSSQL, PostgreSQL, and Oracle is streamed directly into Snowflake Streams and Tasks, replacing batch jobs. A typical DataZ deployment cuts data latency from hours to minutes.
- How long does a DataZ engagement take from kickoff to production?
- Every DataZ engagement starts with a fixed-price 5-day discovery workshop that audits your data estate, maps pipeline bottlenecks, and delivers a migration blueprint. The first production pipelines go live in 4–6 weeks. Ongoing DataOps delivery is consumption-based, scaling with your data volumes. A recent financial services deployment onboarded 200+ production tasks spanning ESG, enterprise data, and trade allocation domains within weeks of kickoff.
- How does DataZ handle data quality and governance?
- DataZ treats data quality as code using Great Expectations, dbt tests, and custom Airflow callbacks that validate schema, completeness, and referential integrity at every pipeline stage. Unity Catalog and Snowflake RBAC enforce column-level security, row-level filtering, and full audit trails. Data contracts between producers and consumers with schema registries prevent breaking changes. Full lineage via OpenLineage and Marquez means every data product is traceable from source to BI layer — supporting HIPAA, GDPR, and SOC 2 compliance requirements.
- What is DataZ's pricing model?
- Every DataZ engagement starts with a fixed-price 5-day discovery workshop — we audit your data estate, map pipeline bottlenecks, and deliver a blueprint with a cost estimate. Ongoing DataOps delivery is consumption-based, priced per data domain and pipeline volume, so cost scales with the size of your estate rather than a fixed monthly fee. There is no long-term lock-in. Contact us for a free discovery workshop and a DataZ cost estimate specific to your environment.
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