Data lake architecture & integrations
A data lake is a centralized repository that stores large volumes of raw, structured, semi-structured, and unstructured data at scale, enabling organizations to consolidate data from across the enterprise before applying structure and transformation for specific use cases. Solvaria designs and implements data lake architectures and integration frameworks that give enterprise organizations a reliable, governed foundation for analytics, AI, and operational data access—built on cloud platforms they already use.
When data volume and variety outgrow traditional storage
Traditional data warehouses are optimized for structured, pre-defined workloads. As organizations collect more data—from IoT devices, application logs, third-party APIs, and unstructured sources—the warehouse model becomes a bottleneck. Data that doesn’t fit the schema gets dropped, delayed, or stored in disconnected silos. Without a data lake layer to absorb and preserve raw data, organizations lose the ability to analyze historical data in new ways and struggle to support the exploratory workloads that AI and machine learning require.
Solvaria’s approach to data lake architecture
We design data lake environments that complement your existing warehouse architecture—not replace it. Our engineers define ingestion patterns, storage zone structures (raw, curated, and consumption layers), and governance frameworks that keep the lake organized, secure, and usable as it grows. We align lake architecture with your specific analytics and AI workloads, ensuring that data lands where it needs to be and in the format downstream consumers require.
Integration work is equally central to our approach. We connect the data lake to source systems, streaming platforms, SaaS applications, and downstream analytics tools, building the integration layer that makes the lake a live, useful part of your data ecosystem rather than a static archive.
Core capabilities
Data lake architecture design
Design storage zone structures, ingestion patterns, and governance frameworks for scalable, well-organized data lake environments on Azure Data Lake, AWS S3, and Google Cloud Storage.
Ingestion pipeline development
Build batch and streaming ingestion pipelines that collect data from databases, APIs, event streams, and SaaS platforms into the lake reliably and at scale.
Data cataloging and governance
Implement metadata management, data cataloging, and access controls that make data discoverable, traceable, and compliant with governance requirements.
Lake house architecture
Design lake house patterns that combine the flexibility of a data lake with the query performance of a structured warehouse, enabling both exploratory and production analytics from a single environment.
Integration framework design
Architect integration layers that connect the data lake to source systems, streaming platforms, warehouses, and analytics tools, with defined contracts for data freshness and quality.
Streaming data integration
Implement real-time data ingestion from event streams and messaging platforms—including Kafka, Azure Event Hubs, and AWS Kinesis—to support low-latency analytics and operational use cases.
AI and ML data preparation
Structure and curate data lake zones specifically to support machine learning feature engineering, model training, and inference pipelines.
Technologies and platforms we work with
Our engineers design and build data lake environments on Azure Data Lake Storage, AWS S3, and Google Cloud Storage, with deep experience in Databricks and Azure Synapse Analytics for lake house architectures. For integration and streaming work, our team applies Azure Data Factory, Apache Kafka, Azure Event Hubs, and AWS Kinesis alongside dbt and custom pipeline frameworks for batch processing. We connect every lake environment to downstream tools, including Snowflake, Power BI, and ML platforms, ensuring data flows cleanly from ingestion through to consumption.
Related services
Cloud data engineering
Design and manage scalable cloud data pipelines and warehouses that feed into and consume from your data lake.
ELT / ETL pipeline development
Build reliable pipelines that move and transform data between the lake, warehouse, and downstream consumers.
Databricks
Leverage Databricks for large-scale data processing, lake house architecture, and advanced analytics within your data lake environment.
Data repository engineering
Design structured data repositories that sit alongside the lake, serving governed, query-optimized data to reporting and analytics tools.
Let’s talk about your data lake strategy
Engage our team to assess your current data environment and define a lake architecture that scales with your data volumes and analytics objectives.