Solution

Data Integration That Unifies Every System You Run

Data integration turns fragmented cloud, on-prem, and SaaS sources into one governed, real-time data layer your teams can trust. DS Stream designs, builds, and operates it: batch, streaming, and CDC pipelines with governance built in, so analytics, AI, and operations run on the same clean, consistent data.

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Key takeaways

Unified data management runs ingestion, transformation, governance, quality, and delivery on one integrated platform instead of a patchwork of ETL, iPaaS, and custom scripts.
The platform covers batch, streaming, and CDC pipelines across cloud, on-prem, and SaaS, with native connectors for Snowflake, Databricks, BigQuery, Kafka, SAP, Salesforce, and 200+ systems.
Schema evolution, data contracts, automated lineage, governance, RBAC, and audit are part of every integration layer, not bolted on later.
DS Stream's delivery model puts an MVP integration platform live in 90 days, with first production use cases typically in 6 to 8 weeks.
Reported impact ranges include 50 to 70% faster onboarding of a new data source, 40 to 60% lower total cost of ownership, and 90%+ pipeline reliability.
You choose the operating model after go-live: fully managed by DS Stream, co-managed, or enablement where your team runs the platform.

What is unified data management and how is it different from traditional ETL?

Unified data management is an operating model where ingestion, transformation, governance, quality, and delivery run on one integrated platform instead of separate tools. Traditional ETL only moves data. Unified data management adds data contracts, lineage, master data, observability, and self-service, so the platform scales across domains without breaking.

What the integration layer includes:

Batch, streaming, and CDC pipelines across cloud, on-prem, and SaaS
Native connectors for Snowflake, Databricks, BigQuery, Kafka, SAP, Salesforce, and 200+ systems
Schema evolution, data contracts, and automated lineage out of the box
Governance, RBAC, and audit built into every integration layer
Cloud-native, event-driven architecture with sub-second latency where it matters

Why is your data still trapped in silos?

Most enterprises run 80 to 400 systems, but data does not flow between them. Integrations get built ad-hoc by different teams, pipelines break silently, definitions drift across domains, and every new analytics or AI project rebuilds the same plumbing. The cost is slow decisions, duplicated work, and models trained on broken data.

Point-to-point integrations nobody owns - CRM, ERP, billing, and the data warehouse connected end-to-end with no clear owner.
Overnight legacy ETL - batch jobs running while the business needs real-time data integration and management.
Multiple sources of truth - customer, product, and transaction data defined differently across departments.
Stalled cloud migrations - on-prem systems that cannot be reliably connected to cloud data integration solutions.
Engineers stuck on maintenance - data engineers spending 60 to 70% of their time maintaining pipelines instead of delivering new value.
Blocked analytics and AI - teams held up by data quality, missing fields, and undocumented schema changes.

What exactly do you get with this solution?

You get full-lifecycle data integration services across every source and target you run: ingestion, transformation, orchestration, quality, and delivery. One team, one reference architecture, one operating model, replacing a patchwork of ETL, iPaaS, and custom scripts, with governed data pushed back into operational systems.

Scope of delivery:

End-to-end data integration services across cloud, on-prem, and SaaS sources
Modern cloud data integration tools selected for your stack, not a fixed vendor list
Real-time and batch integration in the same architecture
Data quality, data contracts, and master data management
Governed by design: catalog, lineage, RBAC, and audit in every layer

What we deliver

Reference Architecture for Cloud and Data Integration

Every layer of the reference architecture is adapted to the sources, latency, and compliance requirements you already run.

Ingestion layer

Batch, streaming, and CDC connectors for 200+ systems
SAP, Oracle, Salesforce, Workday, Kafka, and file-based sources
Secure agents and private connectivity for on-prem systems

Storage and compute

Lakehouse on Snowflake, Databricks, or BigQuery
Medallion architecture across bronze, silver, and gold layers
Cost controls applied at the platform level

Transformation and orchestration

dbt for SQL transformations
Spark or Flink for streaming workloads
Airflow or Dagster with retries, SLAs, and dependency-aware scheduling
All version-controlled in Git

Governance

Collibra, Unity Catalog, or Purview for catalog and lineage
RBAC and data contracts for critical domains
Single audit trail across every data movement

Observability

Pipeline-level monitoring and anomaly detection
Data freshness SLAs enforced in production
Alerts routed to the data owner, not a central queue

Delivery

Reverse ETL back into operational systems
APIs and event streams for downstream products
Sub-second latency where the use case needs it

How we deliver unified data management in 90 days

Five steps, each with a defined output. The first four cover the 90-day platform build and scale-out; the fifth is the ongoing operating model.

Discovery and data landscape assessment (1 to 2 weeks)
We map your sources, targets, existing pipelines, data owners, and priority use cases. Output: integration inventory, pain-point heatmap, and prioritized roadmap.
Target architecture and data contracts (2 to 3 weeks)
We design the reference architecture, select cloud data integration tools, and define data contracts for critical domains. Output: architecture decision record, tool stack, and contract templates.
Build MVP integration platform (4 to 6 weeks)
We implement the first production pipelines, governance layer, CI/CD, and observability, covering two to three high-value use cases end-to-end. Output: a live platform with monitored pipelines and onboarded domain teams.
Scale and migrate (8 to 16 weeks)
We migrate legacy pipelines, onboard new domains, and decommission redundant tools. Output: consolidated platform, reduced license spend, and a documented operating model.
Operate and optimize (ongoing)
We run the platform or enable your team with SRE practices, cost optimization, and continuous improvement. Output: SLAs met, cost per pipeline declining, new sources onboarded in days.

Who gets the most value from unified data management

Chief Data Officer / Head of Data - needs one governed platform for all data integration and management, measurable data quality, and a roadmap that survives reorganizations.
CTO / VP Engineering - wants to consolidate a sprawling stack of ETL and iPaaS tools, reduce maintenance load on engineers, and enable product teams with self-service data.
Head of Data Platform / Data Engineering - needs a reference architecture, CI/CD, and observability so the team can onboard new sources in days, not quarters, without sacrificing reliability.
Chief Analytics / AI Officer - needs clean, timely, well-documented data to make analytics and AI models work in production, not just in notebooks.
CIO / CISO - needs secure, auditable, compliant data movement across cloud and on-prem, with clear ownership and no shadow integrations.

Measurable impact of unified data management

50 to 70%
Reduction in time to onboard a new data source, from weeks to days.
Source: DS Stream, Data Integration solution page
40 to 60%
Lower total cost of ownership versus maintaining multiple ETL and iPaaS tools.
Source: DS Stream, Data Integration solution page
30 to 50%
Faster delivery of analytics and AI use cases on the new platform.
Source: DS Stream, Data Integration solution page
90%+
Pipeline reliability with automated monitoring and data contracts.
Source: DS Stream, Data Integration solution page

Unified data platform vs. a patchwork of point tools

How the two approaches differ on the criteria that decide cost and reliability.
Criterion	Unified data management platform	Separate ETL and iPaaS point tools
Scope	One integrated platform for ingestion, transformation, governance, quality, and delivery	Separate tools, each focused on moving data
Contracts and lineage	Data contracts, automated lineage, master data, and observability included	Not covered by ETL alone
Latency	Sub-second for streaming, under 15-minute freshness for operational analytics	Overnight batch jobs while the business needs real-time data
Ownership	Clear data owners; failures alert the owner, not a central team	Point-to-point integrations nobody owns
Scaling	Scales across domains without breaking; new sources onboarded in days	Every new analytics or AI project rebuilds the same plumbing
Total cost	40 to 60% lower total cost of ownership	Multiple ETL and iPaaS licenses and maintenance to carry

Frequently asked questions

Do you replace our existing cloud data integration tools or work with them?

We work with them first. Most clients already have Fivetran, Airflow, dbt, Informatica, or similar tools in place. We assess what works, consolidate what overlaps, and replace only what is clearly underperforming or redundant. The goal is a coherent platform, not a vendor rip-and-replace.

How long until we see value from a data integration services engagement?

Typically 6 to 8 weeks to the first production use case. Our 90-day delivery model gets an MVP integration platform live with two to three high-value use cases, full governance, and observability. Scaling to additional domains runs in parallel over the following quarters.

Can you handle both cloud and on-prem sources?

Yes. Our reference architecture is hybrid by default. We use secure agents, private connectivity (VPN, PrivateLink, ExpressRoute), and CDC tooling to integrate on-prem systems such as SAP, Oracle, and mainframes with cloud warehouses and lakehouses, without forcing a full migration first.

How do you ensure data quality across integration pipelines?

Through data contracts, automated tests, and observability. Every critical dataset has a schema contract enforced in CI/CD, freshness and volume SLAs monitored in production, and anomaly detection on key metrics. Failures alert the data owner, not a central team, so quality stays close to the source.

What does cloud and data integration cost at enterprise scale?

It depends on data volume, source complexity, and latency requirements. Most clients reduce total integration cost by 40 to 60% compared with maintaining multiple legacy ETL and iPaaS tools. We provide a cost model during discovery, so you see platform, license, and run-cost projections before committing.

Who owns the platform after go-live, your team or ours?

Your choice. We offer three models: fully managed, where we run it; co-managed, with shared SRE; or enablement, where we build and your team runs it. Most clients start co-managed and move to enablement within 12 months, once their team is trained on the reference architecture.