ATHENA
Full-Stack Web
Builds modern, responsive websites and web applications using cutting-edge technologies.
Capabilities
- React, Next.js, Astro
- Node.js, Express, Hono
- PostgreSQL, MongoDB
- TailwindCSS, SCSS
- REST & GraphQL APIs
OR
The Oracle of Data ORACLE
Data & Analytics
Data warehouses, ETL pipelines and BI dashboards — from raw data to business intelligence. Snowflake to Power BI.
Cloud DWH
Snowflake • BigQuery • Redshift • Synapse
BI Tools
Power BI • Tableau • Looker • Metabase
47+
Data Projects
12 PB
Data Processed
99.9%
Pipeline Uptime
<100ms
Query Latency
About ORACLE
The Oracle of Your Data
In ancient times, the Oracle of Delphi provided wisdom and insight — our ORACLE agent does the same for your enterprise data. From raw data to actionable business intelligence — ORACLE sees patterns that remain hidden to others.
With expertise in Snowflake, BigQuery, dbt and Power BI, ORACLE builds scalable data platforms that process petabytes of data and deliver insights in milliseconds. From data strategy to self-service analytics — everything from one source.
Cloud Data Warehousing
Snowflake, BigQuery, Redshift — optimized for performance and cost.
Modern Data Stack
dbt, Airflow, Fivetran — next-generation ELT pipelines.
Self-Service BI
Power BI, Tableau, Looker — dashboards for every user.
oracle_analytics.sql
WITH daily_metrics AS (
SELECT
date_trunc('day', created_at) as day,
sum(revenue) as total_revenue,
count(distinct user_id) as active_users
FROM {{ ref('stg_orders') }}
GROUP BY 1
)
SELECT * FROM daily_metrics
Query completed in 0.23s
12 PB Processed
99.9% Uptime
Technologies
The Modern Data Stack
ORACLE masters all leading data technologies.
Data Warehouses
Snowflake BigQuery Redshift Azure Synapse Databricks ClickHouse
ETL & Transformation
Apache Airflow dbt Apache Spark Fivetran Airbyte Dagster
BI & Visualization
Power BI Tableau Looker Metabase Apache Superset Grafana
Streaming & Real-Time
Apache Kafka Spark Streaming Apache Flink Kinesis Pub/Sub Redis Streams
Project Types
What ORACLE Builds for You
From data warehouses to real-time analytics.
Data Warehouses
Snowflake, BigQuery or Redshift data warehouses with optimized performance.
Multi-Cloud Support
Auto-Scaling
Query Optimization
Cost Management
ETL/ELT Pipelines
Data pipelines with Airflow, dbt and Spark for reliable transformation.
Incremental Loads
Data Quality Checks
Lineage Tracking
Scheduling
BI Dashboards
Interactive reports with Power BI, Tableau or Looker for data-driven decisions.
Self-Service BI
Embedded Analytics
Mobile Reports
AI Insights
Real-Time Streaming
Event streaming with Kafka and Spark Streaming for real-time analytics.
Sub-Second Latency
Event Sourcing
Stream Processing
CDC Pipelines
Data Lakes
Scalable data lakes on S3, ADLS or GCS with Delta Lake or Iceberg.
Lakehouse Architecture
Schema Evolution
Time Travel
ACID Transactions
Analytics APIs
REST APIs for data queries and reporting with caching and rate limiting.
GraphQL & REST
Query Caching
API Gateway
Semantic Layer
Benefits
Why ORACLE?
Scale, Speed, Insights.
Petabyte-Scale
Scales from GBs to petabytes of data.
Real-Time
Sub-second latency for streaming analytics.
Multi-Cloud
AWS, Azure, GCP — or hybrid.
Cost-Optimized
Intelligent tiering and query optimization.
Process
From Raw Data to Insights
Structured implementation with fast results.
1
Discovery
Analyze data sources, use cases and analytics requirements.
2
Architecture
Define data models, ETL flows and warehouse design.
3
Build
Implement pipelines, transformations and dashboards.
4
Scale
Set up self-service BI, data governance and monitoring.
1
Discovery
Analyze data sources, use cases and analytics requirements.
2
Architecture
Define data models, ETL flows and warehouse design.
3
Build
Implement pipelines, transformations and dashboards.
4
Scale
Set up self-service BI, data governance and monitoring.
FAQ
Frequently Asked Questions
Everything you need to know about Data & Analytics.
Snowflake, BigQuery or Redshift — which data warehouse is right?
It depends on your use case: Snowflake is ideal for multi-cloud and data sharing, BigQuery for serverless scaling in the Google ecosystem, Redshift for AWS-centric architectures. ORACLE analyzes your requirements and recommends the optimal solution.
What does a data warehouse project cost?
Implementation starts at CHF 25,000. Ongoing cloud costs depend on data volume and query frequency. We optimize for cost efficiency from day one with auto-scaling and intelligent storage tiers.
How long does analytics platform implementation take?
An MVP with data warehouse, ETL pipelines and initial dashboards is achievable in 6-8 weeks. A complete enterprise solution with data governance, self-service BI and ML integration takes 3-6 months.
What is the difference between ETL and ELT?
With ETL, data is transformed before loading; with ELT, transformation happens in the warehouse. Modern cloud data warehouses like Snowflake and BigQuery are optimized for ELT — cheaper, faster and more flexible. ORACLE recommends ELT with dbt for most projects.
Can existing data infrastructure be migrated?
Yes! ORACLE migrates on-premise data warehouses (Oracle, SQL Server, Teradata) to cloud solutions. We offer phased migration with parallel operation to minimize risks.
How is data quality ensured?
We implement data quality checks in every pipeline: schema validation, freshness checks, anomaly detection. Tools like Great Expectations or dbt tests ensure consistently high data quality.
Ready for data-driven decisions?
ORACLE transforms your data into actionable business intelligence — scalable, fast and reliable.
Our Agents
The mazdek Agent Team
Discover all specialized AI agents and their capabilities.