Data Engineering Services
When finance, sales, and operations define the same customer or transaction differently, another dashboard adds another argument. RaftLabs develops data pipelines, warehouses, shared models, and quality checks that turn disconnected operational systems into a dependable reporting and AI foundation.
Bring the problem, the current workflow, or the existing code. We reply with a practical next step within one business day.
The brief
Good software decisions begin with the constraint, not a list of features or a preferred technology.
Does the same revenue, customer, or inventory question produce a different answer in every team?
Are analysts rebuilding joins and cleaning exports before they can answer the actual question?
Plain answer
Data engineering services connect source systems, standardize business definitions, and deliver trusted data for reporting or AI. RaftLabs develops batch and real-time pipelines, warehouses, and quality checks. A focused warehouse for 3 to 5 sources starts at $30,000 and takes 8 to 12 weeks.
Finance starts with the ERP. Sales starts with the CRM. Operations trusts the warehouse system. Someone joins the files, changes a filter, and arrives with a fourth version of revenue by customer.
Data engineering removes the repeated reconciliation. It gives the business one documented definition, one refresh path, and a visible failure when a source does not arrive as expected.
Proof
A configured connector and reporting tool are better when the sources are clean and the definitions already agree.
Several operational systems hold overlapping customers, products, transactions, or events.
Teams repeatedly clean, join, and reconcile exports before analysis can begin.
A named owner can approve shared definitions, source precedence, access, and the first subject area.
One clean source and a standard reporting connector answer the current questions.
The business cannot yet agree which decisions or metrics the data product should support.
The request is for a dashboard while the underlying definitions remain disputed.
Scope
| Configured platform | Custom data engineering | |
|---|---|---|
| Best when | Common sources and standard reporting needs | Proprietary sources, identity rules, or operating logic matter |
| Connectors | Use the vendor's supported source and destination catalog | Develop around the exact interface and failure behaviour |
| Business logic | Model standard entities inside platform conventions | Encode approved definitions and source precedence |
| Operations | Monitor the vendor service and configured jobs | Own pipeline tests, alerts, recovery, and documentation |
| First step | Trial representative sources and reports | Prove one subject area from source to accepted output |
We recommend configuration when it solves the problem cleanly. Custom work fits when identity resolution, source quirks, business logic, or reliability gaps remain after the standard connectors are exhausted.
How it works
Start with one subject area and make each definition and reconciliation test visible.
Choose the reports, workflows, or AI uses the first data product must support. Inventory the source systems, owners, refresh needs, access limits, and manual reconciliation used today.
Map identifiers and record grain. Decide which source wins when values conflict, how history behaves, and who approves the shared definitions for core entities and metrics.
Load representative history, apply the approved logic, and test freshness, completeness, schema, relationships, and duplicates. A failed check stops or labels the affected output before a report or model consumes it.
Reconcile warehouse totals and sample records with each source. Document ownership, alerts, repair steps, and known limits. Add another domain only after analysts and business owners accept the first one.
RaftLabs built a gas-station management platform with AI OCR that connected 40+ locations during beta. A lightweight local utility synchronized data from existing point-of-sale systems every 10 minutes and kept records locally when a station lost connectivity.
The platform processed more than 20,000 transactions in one test day. Those figures describe that system and rollout; they are not a universal capacity promise for another data stack.
Scope and price
A focused first release covers three to five sources, core entities, quality checks, and a useful analytical layer.
Add more domains or real-time movement only after the first output reconciles with the source systems and users accept its definitions.
Starting investment
Starts at $30,000
A focused warehouse commonly takes 8 to 12 weeks. Source access and reconciliation complexity move the estimate most.
Data audit before the build
We inspect representative source data and interfaces before fixing the phase scope and price. Unknown access or quality problems stay visible rather than becoming quiet assumptions.
Fixed-price phase
Once the sources, definitions, and acceptance checks are agreed, the first phase price is locked in writing. Changes require approval before work begins.
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Read moreData engineering services design and develop the pipelines, storage, models, and quality controls that move operational data into a dependable form for reporting, analytics, or AI. A project may include ETL or ELT, a warehouse or lakehouse, shared business definitions, orchestration, monitoring, access, and documentation.
ETL transforms data before loading it into the destination. ELT loads source data first and transforms it inside the target platform. The right choice depends on data sensitivity, volume, source constraints, audit needs, available compute, and how often business logic changes. We choose after mapping those boundaries.
A warehouse is useful when several systems must support repeatable analysis through shared entities and metric definitions. It may be unnecessary when one reporting tool over one clean source answers the questions. The first assessment should name the decisions and reconciliation failures before choosing a platform.
A focused warehouse covering three to five source systems starts around $30,000 and commonly takes 8 to 12 weeks. Cost grows with source complexity, history, data quality, custom connectors, real-time requirements, access controls, retention, and the number of subject areas.
AI systems need governed access to current, well-defined data. Data engineering can prepare training data, features, retrieval sources, event streams, and evaluation records. It does not make an AI use case valuable by itself; the model or product still needs its own success criteria and operating controls.
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We will trace it to the source systems, definitions, and smallest data product that can settle the number.