Data Source Audit & Pipeline Roadmap
We inventory your databases, SaaS tools and files, then plan a pipeline roadmap that ties data work to business priorities.
- Source inventory
- Pipeline roadmap
- Effort estimate
Data & Analytics
Data Engineering Services
We design and build ingestion pipelines, transformation layers and orchestrated workflows in Airflow and dbt, so analysts and models get clean, tested data on a predictable schedule.
- ETL/ELT pipelines
- Data modelling
- Automated data tests
- Pipeline monitoring
- Years of delivery
- 8+
- Years of delivery
- Projects shipped
- 350+
- Projects shipped
- Clients across US, UK, AU & SG
- 200+
- Clients across US, UK, AU & SG
What’s included
Everything You Need for Reliable, Scalable Data Pipelines
We design, build and run the pipelines that move clean, modelled data from every source to the teams who need it.
-
-
Data Platform Architecture Design
We choose the lakehouse or warehouse layout, storage and orchestration that fit your volumes, budget and growth plans.
- Architecture diagram
- Tool selection
- Cost model
-
ETL & ELT Pipeline Development
Your data is ingested in batch or streaming mode with dbt, Airflow or Spark, so reports stop depending on manual exports.
- Production pipelines
- Orchestration DAGs
- Source code ownership
-
Data Modelling & Transformation Layer
We build tested, documented models for finance, sales and operations so every team calculates metrics the same way.
- dbt models
- Data dictionary
- Lineage map
-
API & Application Data Integration
We connect your CRM, ERP, payment and product databases so data flows automatically without spreadsheets or double entry.
- Connectors
- API ingestion jobs
-
Data Quality Checks & Observability
Automated tests, freshness alerts and anomaly detection catch broken data before it reaches a dashboard or a decision.
- Quality test suite
- Alerting rules
- Observability dashboard
-
Cloud Data Platform Deployment
We deploy on AWS, Azure or GCP with infrastructure as code, CI/CD and access controls, ready for production loads.
- IaC templates
- CI/CD pipeline
- Runbook
-
Pipeline Monitoring & Cost Optimisation
We monitor jobs, fix failures and tune compute and storage, keeping pipelines dependable and your cloud bill predictable.
- SLA-based support
- Cost report
Need something specific?
Why Kramate
Why Business Owners Choose Kramate for Data Engineering
We keep your numbers flowing, and we explain every step in words your whole team can follow.
-
Numbers That Arrive on Their Own
Instead of
copying figures between spreadsheets by hand
With Kramate
We set your data to move by itself, then show you it arriving from a real source.
-
A Warning Before Bad Numbers Spread
Instead of: finding errors after the big meeting
With Kramate: If a source stops sending, we tell you straight away and explain the cause in plain words.
-
You Own and Can Read It All
Instead of: being stuck with one supplier
With Kramate: Everything comes with notes written for your next hire, and all of it belongs to you.
-
A Cloud Bill You Can Predict
Instead of: surprise charges every month
With Kramate: You get a cost estimate before we start, and we flag any change before it reaches your bill.
-
A Fixed Price Paid in Steps
Instead of: open-ended bills with no finish line
With Kramate: You pay as each part is finished, and every two weeks we show you real progress.
Our approach
How We Run Your Data Engineering Project, Step by Step
You see where your data lives, how it moves and who can trust it.
6 clear steps 6 deliverables you keep
-
Source Review
We list every system that holds your data and note what is messy, missing or slow to reach.
-
Data Flow Design
We sketch how data should travel from each source to one shared home, in a plain picture you can follow.
-
Build Automated Loads
We build the jobs that collect, clean and load your data on a schedule, without anyone pressing buttons.
-
Check Data Quality
We compare loaded figures with your source systems and set alerts for gaps, duplicates and late arrivals.
-
Go Live
We switch the loads on in your live setup and watch the first runs closely with your team.
-
Watch & Tune
We keep an eye on run times and running costs, then fix slow or failing jobs as they appear.
Tools & technologies
Technologies We Use for Data Engineering
We pick ingestion, modelling and orchestration tools that match your data volumes and your team.
- Airbyte: Open-source connectors that sync data from apps and databases.
- Fivetran: Managed connectors that copy data from business apps into your warehouse.
- Apache Kafka: Streams events between systems in real time.
- Apache Spark: Processes very large datasets quickly across many machines.
- dbt: Turns raw tables into tested, documented models the business can trust.
- Python: Readable language for business logic, data and automation.
- Apache Airflow: Schedules and monitors data and model pipelines.
- Snowflake: Cloud data warehouse that scales storage and queries separately.
- Google BigQuery: Serverless Google warehouse for fast queries on very large datasets.
- Databricks: Unified platform for big data processing, analytics and machine learning.
- PostgreSQL: Dependable database for complex business data.
- Great Expectations: Runs automated checks so bad data is caught before it reaches reports.
- Terraform: Defines cloud setups as code so they can be repeated.
- Docker: Packages software so it runs the same everywhere.
- GitHub Actions: Automates testing and releases on every code change.
Expert guide
Data Engineering Services: A Complete Guide for Businesses
Data engineering services build and run the plumbing that moves data from the systems where it is created to the places where people and models use it. That means ingesting records from applications, files and event streams, cleaning and shaping them, scheduling the work, and proving the output can be trusted.
Most analytics and AI problems that look like modeling problems are really pipeline problems: a late file, a renamed column, a duplicate order. Good engineering makes those failures visible and recoverable instead of silent.
This guide follows a record from source to report, explains the choices at each step, covers privacy rules in the USA, UK, Australia and Singapore, and shows how to budget and pick a partner.
What data engineering services cover, from source to table
Think of a single customer order. It is created in a shop system, copied into a payments tool, and mentioned in a support ticket. Data engineering decides how those three versions are collected, matched, cleaned and stored so that finance, operations and analysts all see one answer.
A typical engagement covers these layers:
- Ingestion: connectors and jobs that pull data from databases, SaaS tools, files and event streams.
- Storage: a landing area for raw data and curated tables for use, in a warehouse or lakehouse.
- Transformation: business rules that turn raw records into customers, orders and revenue.
- Orchestration: scheduling, dependencies, retries and alerts.
- Quality, lineage and access control: tests, a record of where each field came from, and rules on who may see what.
You likely need data engineering services when reports disagree, when a handful of people hold the knowledge of how a spreadsheet is built, or when a new analytics or AI initiative stalls on data access.
Choosing an ingestion pattern
How data enters the platform shapes freshness, cost and complexity. Pick the simplest pattern that meets the real business need, not the most impressive one.
| Pattern | How it works | Best when | Watch for |
|---|---|---|---|
| Scheduled batch | Full or incremental extracts on a timetable | Daily or hourly reporting is enough | Late source files and growing run times |
| Change data capture | Reads the source database log and sends only changes | You need near-current data without loading the source | Schema changes and deleted rows |
| Event streaming | Applications publish events to a broker such as Apache Kafka (opens in a new tab) | Many consumers need the same events quickly | Duplicate and out-of-order events |
| File and API pulls | Scheduled calls to partner files or vendor APIs | Third-party data with no database access | Rate limits and silent format changes |
If minutes of delay matter, see real-time analytics solutions. If hours are fine, batch is cheaper and easier to repair.
ETL, ELT and where transformation belongs
In ETL, data is transformed before it is loaded. In ELT, raw data is loaded first and transformed inside the warehouse using SQL. Modern cloud warehouses make ELT the common choice because the raw copy is kept, so logic can be corrected and replayed without re-extracting from the source. The usual destination is a cloud data warehouse.
Tools such as dbt (opens in a new tab) let engineers write transformations as version-controlled SQL models with built-in tests and generated documentation. That brings software habits to analytics: code review, a test run before each change goes live, and a clear history of who altered a definition.
Keep business definitions, such as what counts as an active customer, in one reviewed place instead of repeating them inside dashboards.
Orchestration and reliable operations
A pipeline is a chain of dependent tasks. An orchestrator such as Apache Airflow (opens in a new tab) defines that chain as code, runs tasks in order, retries failures and records what ran and when.
Reliability comes from design habits more than from tools:
- Idempotent jobs: running a task twice gives the same result, so a retry never doubles revenue.
- Backfills: the ability to reprocess a past date range after a bug fix.
- Alerts that route to a person, with a short runbook describing the first checks.
- Freshness checks that warn when a table has not updated, even if no job failed.
- Documented ownership: each pipeline has a named owner and a defined fallback.
Testing data and tracing lineage
Data tests check the data itself, not only the code: no duplicate order IDs, no missing customer keys, amounts within a sensible range, and every order linked to a valid customer. Frameworks such as Great Expectations (opens in a new tab) express these checks as readable rules that run inside the pipeline and can stop bad data from reaching reports.
Lineage answers the question every analyst eventually asks: where did this number come from? The OpenLineage (opens in a new tab) project defines an open standard for collecting that information from jobs and datasets, so impact analysis becomes a lookup instead of a meeting.
Privacy and security inside pipelines
Pipelines copy data, and every copy is a place personal information can leak. Good practice is to collect only the fields needed, mask or tokenize sensitive columns early, encrypt data in transit and at rest, grant least-privilege access to each job, and keep audit logs.
The NIST Privacy Framework (opens in a new tab) offers a vendor-neutral way to map data flows and privacy risks. For deeper encryption and key-management work, see data security and encryption services.
Plan deletion as well as collection. If a customer exercises a deletion right, you need to know every table that holds their records, which is where lineage pays off.
Industry use cases
- Retail: merging web, store and returns data into one order history so stock and revenue reports finally match.
- Healthcare: pipelines that de-identify appointment data before analysts see it, with access logged.
- Financial services: daily reconciliation feeds with tests that halt a load when totals do not balance.
- Logistics: event streams from vehicles and scanners joined to shipment records for live status.
- SaaS: product usage events combined with billing data to give customer success a single account view.
These are illustrative examples, not Kramate client results.
Regional considerations for data pipelines
Rules attach to the people the data describes and to where it is processed. Treat the following as a map and confirm specifics with legal counsel.
- USA: privacy law is a patchwork of state and sector rules, such as HIPAA for health data and the California Consumer Privacy Act (opens in a new tab), which gives residents rights to access and delete.
- UK: UK GDPR and the Data Protection Act 2018, enforced by the ICO. Its UK GDPR guidance (opens in a new tab) covers lawful basis and international transfers.
- Australia: the Privacy Act 1988 and the Australian Privacy Principles (opens in a new tab), including rules on sending personal information overseas.
- Singapore: the Personal Data Protection Act, with guidance from the Personal Data Protection Commission (opens in a new tab).
Practical consequence: choose warehouse and orchestration regions deliberately, and document which pipelines move personal data across borders. For pipeline support, agree on-call hours that match when your jobs actually run.
Cost factors and how to plan a budget
The price of data engineering services follows the number and messiness of sources far more than the volume of data. A well-documented database is quick to connect, while a legacy export with undocumented codes can take longer than everything else combined.
- Number and type of sources, and whether each has a stable API or export.
- Freshness requirements: nightly, hourly or streaming.
- Depth of transformation logic and the number of business definitions to agree.
- Test coverage, lineage and monitoring expected from day one.
- Compliance needs such as masking, residency and audit trails.
- Cloud usage after launch: storage, compute and scheduler hosting.
Moving history out of an old system is a related but separate task, covered by data migration services. Start with two or three sources that feed one high-value report, then widen. Fixed-price suits a defined first slice, a dedicated team suits a growing platform, and hourly suits clean-up or advice.
How to choose a provider of data engineering services
- Ask how they test data, not just code, and request an example of a failing check.
- Ask who is paged when a pipeline breaks at night and what the runbook says.
- Check for open tooling so you are not locked into a proprietary scheduler.
- Review documentation habits: diagrams, owners, definitions and lineage.
- Confirm you own the code, configuration and credentials from day one.
- Look for a staged plan that delivers one trusted dataset early.
How Kramate delivers data engineering
Work starts under an NDA, followed by a free consultation and a detailed estimate within 24 hours. A dedicated project manager shares daily updates on Slack or Teams, and each two-week round ends with a demo of working pipelines. All intellectual property transfers to you, and the team can lead delivery or join yours. See our delivery approach for the full process.
Frequently asked questions
What is the difference between ETL and ELT?
ETL transforms data before loading it into the destination. ELT loads raw data first and transforms it inside the warehouse. ELT is common with cloud warehouses because the raw copy is preserved, so logic can be fixed and replayed without going back to the source.
What does a data engineer do day to day?
A data engineer builds and maintains ingestion jobs, transformation code, schedulers and tests, then investigates failures and slow queries. They work with analysts and scientists to turn raw source data into dependable tables, and they document how each dataset is produced.
Should we use batch or streaming pipelines?
Use batch unless a decision truly depends on data from the last few minutes. Streaming adds moving parts: brokers, ordering and duplicate handling. Many organizations run mostly batch with one or two streaming feeds where freshness pays for itself.
How do you test a data pipeline?
Combine code tests with data tests: uniqueness, completeness, valid ranges, referential links and reconciliation against the source totals. Run them inside the pipeline, decide which failures stop a load, and alert a named owner when one trips.
What is data lineage and why does it matter?
Lineage records which sources and jobs produced each dataset and column. It speeds up debugging, shows what a source change will break, and helps you find every copy of personal data when handling a deletion or access request.
How much does a data pipeline project cost?
Cost depends on the number and quality of sources, freshness needs, transformation complexity, testing and compliance. Kramate offers fixed-price, dedicated-team and hourly options, with an estimate within 24 hours of a free consultation.
Sources and further reading
- Apache Kafka Documentation (opens in a new tab)Apache Software Foundation
- dbt Documentation (opens in a new tab)dbt Labs
- Apache Airflow Documentation (opens in a new tab)Apache Software Foundation
- Great Expectations Documentation (opens in a new tab)Great Expectations
- OpenLineage (opens in a new tab)LF AI & Data Foundation
- NIST Privacy Framework (opens in a new tab)NIST
- California Consumer Privacy Act (CCPA) (opens in a new tab)California Attorney General
- UK GDPR guidance and resources (opens in a new tab)UK ICO
- Australian Privacy Principles (opens in a new tab)OAIC
- Personal Data Protection Commission Singapore (opens in a new tab)PDPC
FAQ
Frequently asked questions
The number of data sources, data volumes, transformation complexity and freshness needs drive cost. Testing, monitoring and security requirements add effort. So does the target platform, such as a warehouse or lakehouse. Fewer, well-documented sources cost less than many messy ones. We give a no-obligation estimate within 24 hours.
We typically do source assessment, architecture design, pipeline build, data modelling, testing and handover with monitoring. Duration depends on source count, data quality and how quickly access is granted. Delivering pipelines source by source lets your analysts start using data before everything is finished.
You do. Kramate signs an NDA before detailed discussions, and the pipeline code, transformation models, orchestration setup and documentation all transfer to you. They live in your repositories and cloud accounts, so you can run them without us.
Yes, where they suit the job. We commonly work with existing warehouses, dbt-style transformation, Airflow-style schedulers and cloud services. We follow your repository and review practices, so changes look familiar to your team and pass your normal checks.
Data engineering builds the systems that collect, clean and deliver data. Data science uses that data to build models and find patterns. Without reliable pipelines, data scientists spend most of their time cleaning data. Many teams need engineering first, then modelling.
Check how often jobs fail, how long fixes take, whether data arrives on time, and whether users trust the numbers. If analysts regularly fix data by hand, reliability is low. We add tests, alerts and lineage so failures are caught early.
Common mistakes include skipping tests, hard-coding logic, ignoring failed-load handling and building without documentation. Another is loading everything before knowing which questions matter. We design for re-runs, clear ownership and small, testable steps, which keeps pipelines easier to change.
Taking new projects
Let’s talk about your Data Engineering Services needs
Tell us about your project and get a free consultation and estimate within 24 hours.
- We reply within 4 business hours A real consultant reads your brief — no bot, no queue.
- 30-minute discovery call We dig into goals, constraints and budget. NDA signed on request.
- Proposal in 24 hours Scope, timeline, team and price — clear enough to decide on.
NDA on request You own the IP No obligation Free estimate