Expert Data Lake Engineering Services

A cloud data lake is a centralized, scalable repository hosted on cloud infrastructure such as AWS S3, Azure Data Lake Storage, or Google Cloud Storage that stores structured, semi-structured, and unstructured data in its native format. Unlike traditional databases, it separates storage from compute, enabling cost-effective storage at massive scale while supporting diverse analytics, ML, and AI workloads on demand.

Cloud data engineers design, build, and maintain the infrastructure that moves and stores data at scale. Their work includes architecting data lakes and warehouses, engineering ETL/ELT pipelines for batch and real-time ingestion, implementing data governance and security controls, optimizing query performance, and ensuring data is clean, reliable, and accessible for analytics and AI applications across the enterprise.

An enterprise data lake is a large-scale, governed data lake built to serve the complex needs of a sizable organization. It consolidates data from dozens of internal and external sources ERP systems, CRMs, IoT sensors, APIs, and more into a single platform with robust security, role-based access, compliance controls, and the structure required to support enterprise-grade analytics, reporting, and AI workloads.

A foundational data lake with core ingestion pipelines and governance layers typically takes 8–16 weeks, depending on data source complexity, compliance requirements, and the cloud environment. Cybic follows a phased delivery model standing up a functional, governed core first, then expanding incrementally so your teams see value early without waiting for a multi-month full build.

Cybic builds production data lakes on AWS (S3, Glue, Athena, EMR), Microsoft Azure (ADLS Gen2, Synapse, Data Factory), and Google Cloud (Cloud Storage, Dataproc, BigQuery). We also integrate leading analytical layers including Snowflake and Databricks. Our architecture is infrastructure-agnostic, meaning we select and configure the platform that best fits your existing stack, cost model, and compliance requirements.

Governance is embedded at the architecture level, not added later. Cybic implements role-based access controls (RBAC), encrypted data protection in transit and at rest, full audit trails, and data lineage tracking. Every lake we build is structured to comply with relevant standards including GDPR, HIPAA, CCPA, and SOC 2 with documented policies and data ownership frameworks aligned to your organization's requirements.

Yes. Cybic specializes in legacy EDW modernization and data lake migration. We assess your current warehouse infrastructure, design a migration pathway that minimizes disruption, re-engineer ETL/ELT pipelines for cloud-native performance, and integrate the resulting data lake with your BI, analytics, and AI tooling. Migrations are executed in phases to maintain operational continuity throughout the process.

A data warehouse stores structured, pre-processed data optimized for predefined business intelligence queries. A data lake stores raw data in any format structured, semi-structured, or unstructured at much lower cost, and applies schema at query time. This flexibility makes data lakes ideal for AI and ML workloads. Many enterprises use both in a complementary lakehouse architecture to serve BI and advanced analytics simultaneously.