Most modern data platforms use a lakehouse approach that combines the best of both. We architect a structured lakehouse with raw, curated, and aggregated layers — so you get the flexibility of a data lake for storing diverse data types alongside the performance of a warehouse for analytics and reporting. The platform runs on Snowflake, Databricks, BigQuery, Redshift, or Delta Lake depending on your existing cloud investment, scale requirements, and team skills.

Yes — supporting both patterns is central to our approach. Batch ETL and ELT handle periodic loads from ERP, CRM, databases, and file-based sources, while real-time streaming and change data capture deliver event-driven data from Kafka, APIs, IoT sensors, and transactional systems. Both flows land in the same lakehouse, so your dashboards and models work with historical and live data without needing separate platforms.

We connect to virtually any enterprise data source — SAP, Salesforce, SQL and NoSQL databases, SaaS applications like ServiceNow and Jira, POS and retail transaction systems, web and mobile clickstream and logs, IoT and sensor telemetry, CSV, Excel, JSON, and XML files, and REST and Kafka event streams. Kafka, Spark, Airflow, Fivetran, and dbt handle the ingestion and orchestration, with pre-built connectors and custom adapters where needed.

We integrate with Power BI, Tableau, Looker, and Grafana — and can work with other tools your teams already use. The platform delivers executive and operational dashboards, scheduled and ad-hoc reporting, real-time threshold and anomaly alerts, predictive models and ML forecasting, and embedded in-app analytics. A semantic layer ensures consistent metrics and definitions regardless of which BI tool accesses the data.

Data quality and governance run across every layer of the platform — not as an afterthought. We implement data profiling at ingestion to catch issues early, quality checks on transformations, lineage tracking so you can trace any metric back to its source, a catalog for dataset discovery, access controls and PII detection and masking for sensitive data, and ownership and SLA management so teams know who is responsible for each dataset and how fresh it should be.

That's the most common starting point we encounter. The lakehouse architecture is designed for exactly this — the raw layer preserves source data as-is without requiring upfront cleanup, the curated layer cleanses and standardizes it, and the gold layer provides analytics-ready datasets. We assess your sources during the first phase, prioritize the highest-value data, and build pipelines incrementally so you see results quickly while the platform grows.

A foundational data platform — including source assessment, ingestion pipelines for priority systems, lakehouse setup, initial data models, and core dashboards — typically takes 8–12 weeks. Real-time streaming and advanced capabilities like ML feature stores or predictive models can be added incrementally after the foundation is in place. We deliver in phased milestones so your teams start getting value from dashboards and reports early in the engagement.

Absolutely. The processing layer includes ML feature stores that prepare and serve inputs for predictive models, alongside dbt-based data modeling and real-time aggregation. We build forecasting and classification models that surface predictions directly in your dashboards and reports — like the 35% improvement in stock-out prediction shown in our retail use case. The same governed, trusted data that powers your reporting also feeds your ML models.