CONNECT AND INGEST

Connect and ingest data without

delays or data loss

Your data lives across SaaS tools, databases, files, and legacy systems. LakeStack simplifies data integration by bringing everything together in real time, with pipelines that stay reliable, governed, and ready for use.

Start moving your data reliably

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The problem

Data integration breaks before it even reaches the warehouse

When pipelines are fragile and data is scattered, every team pays the price in delays, broken trust, and missed decisions.

75%

Data is scattered

Your data sits across dozens of tools and systems, making it hard to unify and trust what you see. Without a single integration layer, nothing connects the way it should.

80%

Pipelines keep breaking

Manual integrations and custom scripts create constant maintenance overhead and silent failures. Your team spends more time fixing pipelines than building on top of them.

40%

Data arrives too late

When data is not available in real time, your dashboards, models, and workflows lose relevance. Stale data means stale decisions.

How it works

Reliable ingestion that works across every data source

LakeStack connects your entire ecosystem and ensures data flows consistently from source to destination, even as systems change. Whether you are ingesting from SaaS apps, databases, files, or enterprise systems, you get governed, real-time pipelines that scale without constant fixes.

1. Connect sources

Link your SaaS apps, databases, and files using pre-built connectors. LakeStack bridges your entire ecosystem instantly.

Result: A clean starting point for all organizational data.

2. Ingest & sync

Execute real-time or batch ingestion based on your needs. Ensure data flows consistently even as your systems change.

Result: Reliable, automated pipelines that eliminate manual work.

3. Govern & monitor

Apply enterprise-grade oversight to every flow. Automatically monitor health and maintain strict data governance.

Result: Secure, audit-ready data that scales without constant fixes.

4. Deliver & scale

Handoff structured data to your pipeline-ready destination. Empower downstream teams with immediate access.

Result: Accelerated time-to-insight with zero deployment friction.

Why LakeStack

What makes LakeStack different

A unified platform designed to solve the complexity of enterprise data ingestion at the source.

Ingestion aligned with governance

Governance starts at ingestion. Access policies, metadata, ownership, and lineage are applied as data enters the platform, ensuring every dataset is controlled, traceable, and compliant.

Built for hybrid data environments

Ingest data seamlessly across on-prem systems, private infrastructure, cloud applications, and external partners, unifying legacy and modern sources within a single architecture.

Continuous data, always current

Support both batch and streaming ingestion in one platform. Keep datasets continuously updated to power real-time analytics, operational workflows, and AI use cases.

Standardized pipelines, lower complexity

Eliminate fragmented, one-off pipelines. LakeStack standardizes ingestion patterns so teams onboard new sources faster, reduce maintenance overhead, and minimize operational risk.

Business ROI

Built for teams that rely on data every day

When pipelines run reliably, and data arrives on time, every team downstream moves faster, from reporting to operations to customer experience.

80%

Reduction in manual data preparation and processing

70%

Faster access to operational insights across teams

80%

faster time-to-insights across teams

Customer impact

Proven business impact

Browse all customer stories

Discover how leading organizations use LakeStack to transform fragmented data sources into governed, high-impact business assets.

About Client

AFG.tech operates a multi-location dealership platform, with core data spread across CRM, workshop, and invoicing systems.

As the platform scaled, data remained siloed and inconsistently structured. Reporting depended on manual effort and custom pipelines, making it difficult to get a reliable, real-time view of operations across dealerships.

9-12

months of engineering effort avoided.

80%

reduction in ingestion and reporting workload.

AFG.tech replaced fragmented, pipeline-heavy data workflows with a unified, governed lakehouse, enabling real-time access to consistent, query-ready data across all dealerships.

View case study

About Client

Kior Healthcare operates across multiple clinical systems, with data spread across lab systems, ERP, bookings, imaging, and unstructured sources like PDFs and clinician notes.

As data volume and formats grew, teams relied on manual data preparation and file handling, making it difficult to access timely, reliable information for both clinical and operational decisions.

80%

reduction in manual data prep and file processing

70%

faster clinician and operational visibility

Kior Healthcare replaced fragmented, file-heavy data workflows with a unified, governed lakehouse, bringing structured and unstructured clinical data into a single, query-ready foundation.

View case study

What comes next

Explore what you can do after ingestion

Transformations

Turn raw data into structured, analytics-ready datasets. Build reliable transformation pipelines with full lineage.

Transform data

Activations

Push data back into business tools to drive real-time actions. Trigger workflows on live events, not stale snapshots.

Activate data

Frequently asked questions

How long does it take to set up a new data source?

Most data sources can be connected quickly using pre-built connectors, without writing custom code. The actual setup time depends on the complexity of your source system and access permissions, but in most cases, teams can start ingesting data within hours instead of days. This removes the typical delays caused by engineering dependencies.

Can LakeStack handle real-time data ingestion?

Yes, LakeStack supports both real-time and batch ingestion, so you can choose what fits your use case. For operational use cases like dashboards or customer workflows, real-time ingestion ensures your data stays fresh and actionable. For reporting or historical analysis, batch pipelines help optimize cost and performance without compromising reliability.

What happens when source schemas change?

Schema changes are one of the most common reasons pipelines fail. LakeStack is designed to handle schema evolution automatically, so your pipelines continue running even when source data structures change. This reduces manual fixes, prevents data loss, and ensures your downstream systems always receive consistent data.

How do you ensure data reliability?

LakeStack includes built-in monitoring, alerting, and fault tolerance mechanisms that continuously track pipeline health. If an issue occurs, your team is notified immediately so it can be resolved before it impacts business users. This means fewer silent failures, more predictable data flows, and higher trust in your data.

Do we need to manage infrastructure?

No, LakeStack handles the underlying infrastructure, so your team does not have to manage pipelines, scaling, or maintenance manually. This allows your engineering and data teams to focus on building use cases and driving outcomes, instead of spending time on operational overhead.