Imagine having every data asset in your enterprise optimally accessible.

Optimized for every workflow. Secured for purpose. Archived for every efficiency. Available for every location. Your data. Your way. Realize the power of cloud architecture.

Business Challenge

Companies have large numbers of disparate systems that produce data in all forms. The opportunity cost of ignoring this data is unknown but potentially strategic. The cost of warehousing this data into a BI solution is often prohibitive, especially for on-premise solutions. Traditional data stores, like NFS or SAN are expensive and inflexible. Hadoop solutions are difficult to manage. The trend towards data-driven business puts pressure on IT managers to do something, but the correct path is complex and has strategic implications.

Business Solution

OmniArcs designs governed data lakehouses on cloud object storage (e.g., Azure Data Lake or S3) with a clear separation of compute and storage. We standardize on open formats (Apache Parquet) and an open table catalog (Apache Iceberg) to enable portability, time‑travel, and evolution at scale. A unified catalog (e.g., Unity Catalog/Atlas) enforces access controls, lineage, and metadata.

Pipelines are code‑driven and automated: dbt‑core for transformations (Data‑as‑Code) and Apache Airflow for orchestration. For interactive analytics we support DuckDB and Databricks SQL, and we integrate PostgreSQL (including pg_analytics) for operational reporting and joins to relational systems. Streaming replaces legacy MQ: Kafka (or cloud equivalents) for right‑time integration and CDC. Data contracts keep producers and consumers decoupled.

Customer Outcomes

• Data transparency and discoverability (catalog, lineage)
• Strategic flexibility with open formats (Parquet) and tables (Iceberg)
• High availability and durability by design, independently scalable compute/storage
• Orchestrated, observable pipelines (batch and streaming)
• Tiered storage and lifecycle policies for optimal cost
• Fine‑grained security (Zero Trust) and governance across engines
• Last‑mile alignment for personas (developers, analysts, explorers, consumers)

Timelines & Costs

With sample data and access, we typically produce a preliminary design and working proof of concept in 3–4 weeks, following an assessment and SME working sessions.

Customers We Serve

• Teams building or modernizing BI/analytics without monolithic EDW investments
• Organizations reducing ETL/ops costs via open formats and code‑driven pipelines
• Estates with large NAS/SAN footprints seeking lower‑cost, durable object storage
• Teams with many inputs (batch, API, CDC/streaming) seeking unified delivery
• Legacy Hadoop customers simplifying to a lakehouse with governance