S.C.A.L.E.™ · The enterprise data platform your AI runs on
The enterprise data foundation every AI initiative sits on
S.C.A.L.E.™ is a Terraform-driven enterprise data platform accelerator. Production medallion lakehouse on AWS or Azure, your choice of engine (Databricks, Snowflake, Fabric, or self-hosted), with connectors, governance, and compliance built in. Stood up in weeks instead of quarters.
Works with
AWS 
Azure 
SAP 
NetSuite 
Salesforce 
Dynamics 
ServiceNow 
SQL Server What S.C.A.L.E. stands for
Five pillars of your enterprise data foundation
Each letter is a capability S.C.A.L.E. ships out of the box - the five things every enterprise data platform needs, delivered as one accelerator instead of five separate projects.
S - Structured Setup
Prebuilt, production-ready lakehouse
C - Cloud-Ready Core
Multi-cloud, secure, compliant
A - Automated Acquisition
Prebuilt connectors, instant ingestion
L - Lifecycle Layering
Medallion pipelines, governed flow
E - Execution Engine
Arcastra™-powered orchestration
Why it matters
What 'production-ready' looks like
Your enterprise data platform, architected
S.C.A.L.E. is the enterprise data foundation the rest of your stack sits on. Your source systems flow in through Automated Acquisition, land in a governed enterprise data lake (Lifecycle Layering with a Bronze / Silver / Gold medallion), wrapped by a multi-cloud Cloud-Ready Core, and are served out through Arcastra™-powered Execution to Arcastra™ AI agents, BI, ML models, and reverse ETL. One foundation, every downstream use case.
Your enterprise source systems
Owned by you · outside SCALE
SAP ECC / S/4HANA NetSuite Salesforce Dynamics 365 / BC ServiceNow SQL Server / Azure SQL Structured Setup
SCALE - the prebuilt, production-ready accelerator
Cloud-Ready Core
- secure, compliant, multi-cloud foundation
AWS
Azure
Standalone Automated Acquisition
- pulls raw data from every source system above
Connectors
SuiteQL · RFC/BAPI · Bulk API · OData · pyodbc
Extraction
Chunked reads · full + incremental · watermarks
Reliability
Retries · backoff · failure alerts
State
DynamoDB · Azure Table · SQLite
Lifecycle Layering
- the data lake medallion + governed pipelines
Bronze
Raw, as landed
Silver
Cleaned, conformed
Gold
Consumption-ready
Execution Engine
- Arcastra™-powered orchestration out to every consumer
Downstream consumers
Served by SCALE · deployed where you need them
Arcastra™ AI agents
- Document Intelligence
- AnalystIQ
- Voice & Chatbot
BI & reporting
- Power BI
- Tableau
- Looker
AI / ML models
- Training data
- Fine-tuning
- RAG pipelines
Reverse ETL
- Hightouch · Fivetran
- Back to operational systems
The infrastructure stance
A production medallion lakehouse, deployed by Terraform
Proof for the architect the CIO forwards this to. S.C.A.L.E. plugs into whatever data platform framework you choose - Databricks, Snowflake, Microsoft Fabric, or a self-hosted open-source stack - and deploys it into your AWS or Azure account with Terraform. You pick the engine; S.C.A.L.E. handles the lake, the connectors, the governance, and the compliance guardrails. Reviewable, repeatable, and yours to own.
Terraform-defined everything
S3 + Glue + Lake Formation on AWS. ADLS Gen2 + Synapse / Fabric / Databricks on Azure. Auditable, reviewable, re-runnable. Parameterised lifecycle, partitioning, and per-layer storage tiering.
Your choice of engine
AWS Glue, Lambda, EMR, or Athena. Azure Synapse, Databricks, Microsoft Fabric, or Snowflake. Pick the engine that fits your workload, skill set, and cost model - SCALE deploys around whatever you choose.
Governance built in
AWS Lake Formation + IAM policies or Microsoft Purview + Unity Catalog, wired to AWS Secrets Manager or Azure Key Vault. Not a governance SKU you install later.
Connectors
Enterprise connectors for every system your enterprise actually runs on
Each connector below ships production-hardened - full load on first run, automated incremental delta after. Underneath, they share the same extraction framework: chunked reads, per-entity watermarks, exponential-backoff retries, and failure alerting. Adding a new source - your in-house ERP, an industry-specific system, the SaaS tool you acquired last quarter - reuses the framework. Days, not a custom project.
SAP
ECC and S/4HANA. RFC/BAPI for transactional extracts, HANA SQL cursor for large-volume reads. Watermark state per entity.
NetSuite
SuiteQL date-range chunking. Full load + automated incremental delta. Handles the scale your finance team has actually put in there.
Salesforce
Bulk API 2.0. Proper async extraction at CRM scale, not the REST API pattern that falls over at 10M rows.
Dynamics 365 / Business Central
Dataverse Web API and OData endpoints. Entity-level incremental extraction across F&O, Customer Engagement, and BC with per-entity watermarks.
ServiceNow
OData streaming with retry + backoff. Incidents, changes, CMDB - all flowing to the lake cleanly.
SQL Server / Azure SQL
pyodbc parallel workers. Watermarks in DynamoDB / Azure Table / SQLite. Failure alerting to SNS, Teams, or email.
The enterprise data foundation every AI initiative assumes you already have
Your board is asking about AI. Every ambitious initiative - Document Intelligence, forecasting, RAG chat, reverse ETL activation - assumes a clean, governed, compliant enterprise data platform underneath. S.C.A.L.E. is that foundation. Get it right first and every initiative on top of it lands predictably. Skip it and projects stall at 80% for the same reason the last one did. Pair S.C.A.L.E. with Algoscale's data lake consulting team for enterprise-specific controls and last-mile audit prep.
Compliance, enforced at the infrastructure layer - not in PowerPoint
Your compliance posture stops being a quarterly audit scramble. S.C.A.L.E. auto-applies HIPAA, PCI-DSS, SOX, GDPR, and ISO 27001 guardrails based on the industry vertical you pick at setup. Encryption policies, access boundaries, audit logging, and resource tagging are written into Terraform modules - not manually documented after the fact.
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