Login AI
Data engineering for AI and analytics
We design the data pipeline: pull from APIs, databases, and files, land it in a layered warehouse with marts, and add quality checks. In 6-8 weeks we launch a pilot on three sources.
We build ingestion, storage, and marts so reports reconcile and quality checks run before publication.
Pricing
from ₽1,200,000
per project
This is the lower bound of a project. We scope the exact quote after a brief: it depends on volume and the number of integrations.
Capabilities
Ingestion from sources
We pull data from APIs, databases, and files with a connector, cursor, and retry on a dropped connection. You set the schedule, and a repeat run closes a missed interval.
Warehouse and marts
We build warehouse layers: raw, cleaned, and analytics. Marts are shaped for reports, so an analyst gets a table with clear fields and freshness within the last hour.
Quality checks
Every run checks empty required fields, duplicate keys, and amount mismatches between source and mart. A mismatch stops publication and sends an alert within 15 minutes.
Lineage and monitoring
The lineage line links a mart column to its source table and transformation step. A dashboard shows freshness, volume, and pipeline status, so a failure shows before a complaint.
From request to result
1
Source audit
We review systems, owners, and access methods: API, database, file, or an export from an accounting system. We record refresh cadence and required fields.
2
Warehouse model
We design layers and marts for your reports: which tables you need, how keys connect, and where a metric is calculated. We agree the schema with your analyst.
3
Pipelines and checks
We build ingestion and refresh, then add quality checks and lineage. Every pipeline gets a schedule, a retry, and an alert, and the result lands on the dashboard.
4
Pilot and handover
We launch a pilot on three sources and up to 5 TB, then reconcile the numbers with your reports. We hand over documentation, tests, and instructions to your team.
Check if it fits you
Good fit
Three sources or more
Data sits in a CRM, billing, and files while an analyst assembles the summary by hand. The pipeline brings the sources into one warehouse and refreshes marts.
An analytics owner
An analyst owns the metrics and confirms the calculation rules. They set source priority and accept the pilot.
Not a fit
No access to sources
A vendor closed the API and withholds an extract, while the database sits with a contractor. Access or an official export comes first.
Ad-hoc extracts only
Reports are assembled by hand once a month, and one person holds the repeatable process. A pipeline pays off when a report is needed more often.
What you get
3 sources
Sources in the pipeline
The base pipeline collects 3 sources: an API, a database, and files. Each source is listed on the map and added to the schedule.
5 TB
Warehouse volume
The base warehouse holds 5 TB: a raw layer, a cleaned layer, and marts. Volume grows with the sources, and the retention policy sets a lifetime.
2 pipelines
Pipelines at work
The base setup runs 2 pipelines: loading and mart refresh. Schedule, retries, and alerts are set in the first stage, so runs stay automatic.
What the delivery includes
A source map
The source map lists systems, owners, refresh cadence, and access method. It shows which APIs and tables feed the warehouse.
Pipelines
Pipelines pull data on a schedule and land it in warehouse layers. Every run records status, volume, and duration, and a repeat run closes an interval.
A quality dashboard
The quality dashboard shows the share of empty fields, duplicate keys, and mismatches between source and mart. It tracks data freshness and check results.
How the system is built
Ingestion
Ingestion pulls from APIs, databases, and files on a schedule or an event. Each source gets a connector, cursor, and retry rule, so a load resumes from the point where it stopped. Raw records stay in the warehouse.
Transformation
Transformation brings raw records into one schema: types, dictionaries, and keys settle into a single layer. The model splits raw, cleaned, and analytics layers, and logic lives in SQL and versions with the code.
Quality and lineage
Quality and lineage checks run at every step: checks count empty fields, duplicates, and amount mismatches. The lineage line links a mart column to its source table, so a dispute about a number opens the path.
Where the solution pushes back
- 01
Source quality
Source quality sets the ceiling for mart accuracy. Duplicates, mixed date formats, and empty fields surface on the first run, so the audit comes before model design. An ingestion check adds 10–15 minutes per source to the pipeline run, and a mismatch surfaces before the mart is published.
- 02
Data ownership
Data ownership sits with your analyst, who confirms metric rules and source priority. We hand over documentation, tests, and instructions, so your team runs the pipelines.
- 03
Latency and cost
Latency and cost move together: streaming refresh updates marts in minutes and uses more compute, while a nightly batch costs less at volume. We size both modes on your data.
Technologies
The technologies we use in our work and their role in a project.
Ingestion, storage, and analytics
Ingestion runs through Airflow, streaming events land in Kafka, transactional data sits in PostgreSQL, analytics queries hit ClickHouse, and files and archives live in MinIO.
Cost calculator
This is an indicative range, not a quote. We scope the exact estimate after a brief: it depends on volume and the number of integrations.
Data sources
Volume, TB
Pipelines
Warehouse
Add-ons
Support
Year one: updates and monitoring, a reply within 2 business days.
Service
Data engineering for AI and analytics
The estimate covers our team's work. Hardware, third-party service licences and travel are added on request.
Estimated cost
₽1.95M
Timeline
8 wk
Frequently asked questions
Which stack do you use?
The stack: Airflow for scheduling, Kafka for events, PostgreSQL for transactions, ClickHouse for marts, MinIO for files. We pick it for your sources and volume.
How do you choose a warehouse?
We look at query type and volume. Transactions go to PostgreSQL, aggregates over millions of rows to ClickHouse, files to MinIO. Up to 5 TB, PostgreSQL and ClickHouse cover the job.
How long does the project take?
A pilot on three sources and two pipelines takes 6-8 weeks. The first two weeks cover the audit and model, then come pipelines and checks. We size it after the source list.
How do you ensure data quality?
Checks count empty fields, duplicate keys, and amount mismatches with the mart. You set the threshold, for example an empty-field share below 1%. A mismatch stops publication, and an alert arrives within 15 minutes.
What teams add to this service
Related Services
Part of Solutions
Implementation Examples
−90%
Discrepancies in reports
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