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Big data engineering, DWH data warehouses and applied Data Science analytics

Collect fragmented databases, CRM, advertising cabinets and analytics into a single high-speed analytical warehouse (DWH) without duplicates and losses. We design reliable data processing pipelines (ETL/ELT) on the stack of ClickHouse, PostgreSQL, Apache Airflow and dbt: deep customer segmentation (RFM), accurate LTV calculation, cohort analysis and mathematical modeling of consumer behavior.

✓ Single Source of Truth (Single Source of Truth): integration of all transactions, marketing and warehouses in one database
✓ Acceleration of analytical queries by 50+ times: ClickHouse processes billions of rows in fractions of a second
✓ Full automation of reports: data is updated automatically every night or in real-time through queues
✓ Deep client analytics: RFM segmentation, cohort retention matrices and client churn prediction
ClickHouse & PostgreSQL DWH ETL / ELT Pipelines (Airflow & dbt) Customer RFM Segmentation Cohort Retention Matrices Data Quality Testing & Governance
Get DWH Concept
Formats and pricing
ALL IT • SERVICE SPEC
SLA 99.9%
Innovative technologies and artificial intelligence WARRANTY UNDER AGREEMENT
Analytical data warehouse DWH

Rows processed per day: 42.6M • ETL execution time: 14 min • Connected sources: 12 • Data errors: 0%

⏱ Start: 1–3 days 🛡️ NDA: 100% SLA Warranty
🗄️
ClickHouse DWH
Speed 0.05s
🔄
Airflow ETL
Automatic Data Collection
🎯
LTV Models
Cohort Analysis
🧹
Data Quality
Clean Data without Duplicates
✓ Official contract with fixed cost of work
✓ 100% transfer of rights to code and configuration to the customer
✓ Assigned leading engineer and support 90 days
FULL TRANSFER OF RIGHTS CONTRACT + WARRANTY
[ DATA POWER ]

Why a successful business invests in data engineering and Data Science

Solutions based on different Excel tables cost companies up to 20% margin through blind spots

[ 01 ]

Real-time analytics for any periods

Instead of waiting for 1C to load every half hour, analytical queries are executed in fractions of a second.

[ 02 ]

Accurate unit economics calculation

Calculation of CAC (acquisition cost), LTV (lifetime value) and margin per each client and channel.

[ 03 ]

Data purity and zero duplication

Automatic deduplication algorithms merge transactions from different channels into a single customer profile (Customer 360).

[ 04 ]

Readiness for AI and ML implementation

High-quality prepared DWH — is a foundation, without which it is impossible to train predictive models and neural networks.

[ WORK STAGES ]

Full complex of building big data infrastructure

From connecting API advertising and accounting systems to Data Science analytical models

[ STEP 01 ] Architecture

Audit of Data Sources and Design of DWH Architecture

We compose a data flow map, analyze 1C, CRM, GA4, advertising schemes, and design a star/snowflake schema.

[ STEP 02 ] ETL Pipelines

Development of reliable ETL/ELT pipelines (Airflow)

We configure regular automatic data download according to the schedule with error processing, queues and retries.

[ STEP 03 ] dbt Tuning

Data Transformation and Modeling (dbt)

We are writing versioned transformation models on dbt (data build tool): normalization, aggregation, and cleaning of raw tables.

[ STEP 04 ] Customer 360

Construction of a customer profile (Customer 360)

Combining client data from the website, CRM, messengers, and cash register into a single identifier for cross-analysis.

[ STEP 05 ] Data Science

Mathematical models of Data Science (RFM, Churn, LTV)

Client base clustering into segments (VIP, loyal, dormant, lost) and prediction of probability of repeat purchases.

[ STEP 06 ] Data Quality

Testing data quality (Data Quality & CI/CD)

Automated integrity tests (Great Expectations): warning about abnormal zeros, deviations or delays in receipt.

[ Tariff Plans ]

Packages for implementation of data warehouses and Data Science

Select the project scale according to the number of sources and the monthly volume of transactions

Quick start for companies with 2–4 sources

Data Pipeline & Mart Starter

Integration of CRM, website, and accounting database into a high-speed PostgreSQL/ClickHouse database to eliminate manual reporting.

Development time: from 3–4 weeks
Work Scope:
✓ Connection to 4 sources (CRM, 1C/BAS, Google Analytics, Ads)
✓ Deployment of PostgreSQL / ClickHouse warehouse in the cloud
✓ Configuration of daily ETL scripts for data extraction
✓ Basic normalization of data and consolidation of financial tables
✓ Ready data warehouses for Power BI integration
✓ 30 days of technical warranty
Choose the tariff 'Starter DWH'
Most popular for e-Commerce, distribution, and SaaS ★ Popular

Modern Data Stack & Science Core

Industrial stack (Airflow + dbt + ClickHouse), cross-profile Customer 360 and RFM and LTV segmentation algorithms.

Development time: from 6–8 weeks
Work Scope:
✓ All that is included in the package «Data Pipeline Starter»
✓ Connection to an unlimited number of sources and databases
✓ Orchestration on Apache Airflow with schedule and alerting
✓ Transformation on dbt with automatic documentation and quality tests
✓ RFM segmentation and cohort retention matrices for clients
✓ 60 days of engineering support and training for analysts
Choose 'Modern Data Stack'
For large retail networks, telecom and fintech

Enterprise Big Data & ML Infrastructure

Clustered distributed big data storage, processing of streaming events in real time (Kafka) and predictive scoring.

Development time: from 10–14 weeks
Work Scope:
✓ Everything that is included in the package «Modern Data Stack & Science Core»
✓ Distributed cluster ClickHouse / BigQuery / Snowflake
✓ Stream data processing in real time (Real-Time Kafka / Flink)
✓ Predictive models for churn and recommendation algorithms
✓ Full GDPR compliance and corporate security standards
✓ Dedicated team of Data Engineers & Data Scientists (SLA 99.9%)
Choose 'Enterprise Big Data'
← Swipe options →
[ COST CALCULATOR ]

Calculate the cost of the Data Science / DWH project

Select Data Sources and Analytics Tasks

1. Number of sources that need to be combined: Select Variant

2. Monthly volume of new records: Select Variant

3. Main project goal: Select Variant

CHOOSEN PARAMETERS OF THE SOLUTION:
⏱ Estimated Timeline: Calculation... 👥 Team: 2–3 engineers
1. Number of sources that need to be combined: 2–4 sources (CRM, 1C/BAS, Advertising Offices)
2. Monthly volume of new records: Up to 1 million rows per month
3. Main project goal: Aggregation of all data into one place and connection of BI dashboards
✓ Parameters will be fixed in the application form
Get an exact quote
[ STAGES OF COOPERATION ]

How the project is delivered

Transparent process from first contact to final release on your domain.

01
1 Week

Data audit and schema design

We are studying the structure of data sources - databases, fixing business rules for metric calculation and drawing the DWH architecture.

02
2 weeks

Deployment of a vault and ETL pipelines

We are setting up ClickHouse / PostgreSQL, writing Airflow connectors for reliable regular data collection.

03
2–3 weeks

Data Modeling and Calculation of Data Warehouses (dbt)

We write SQL transformations, configure summary tables for orders, payments, and marketing expenses.

04
1–2 weeks

Data Science Modules and Quality Testing

We calculate client segments, cohorts retention and connect automatic tests against occurrence of duplicates.

05
3–5 days

System Handover and BI Tool Integration

Provides the team with access to ready-made storefronts, connects Power BI / Metabase, and instructs analysts.

Our Clients

Companies that have entrusted ALL IT Agency with the development and scaling of their digital solutions

Partner Case 6
Valikhnovski Surgery Institute
DIM Service
Meregi Group
Partner Case 11
Wizeclub
Partner Case 5
TOV Zagin Alpha
Partner Enterprise
Partner Case 6
Valikhnovski Surgery Institute
DIM Service
Meregi Group
Partner Case 11
Wizeclub
Partner Case 5
TOV Zagin Alpha
Partner Enterprise
[ FREQUENT QUESTIONS ]

Answers to common questions from customers

Key details regarding terms, independent website editing, and rights transfer.

Battlefield operational databases (OLTP) are optimized for fast individual row insertion, not for analyzing millions of records per year. When an analyst runs a heavy annual report in a battlefield database, it freezes, blocking the work of managers at cash registers or warehouses. Analytical warehouse DWH (OLAP) isolates analytics and performs complex calculations in hundredths of a second without loading operational processes.

ClickHouse — this is a columnar database management system. Unlike regular row-based databases, it reads only the columns that are involved in the query (for example, amount and date) from the disk, automatically compressing data by 5–10 times. This allows scanning tens of millions of rows in milliseconds even on moderate server hardware.

This is the technology of merging all digital traces of one customer. If a client orders a product on the website from one number, then calls from another, and pays with a card at the counter — the system merges these events into one profile. You see the full history, real-life customer lifecycle (LTV) and accurate ad return.

Depending on the business task, we configure nightly batch updates (Batch ETL, for example at 03:00 every night), updates every 15–30 minutes, or real-time streaming (Real-time Streaming) using Kafka or Debezium queues.

Yes, we provide full post-project support: monitor the stability of data collection conveyor belts 24/7, connect new sources, add new data warehouses and expand mathematical models according to business growth.

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Do you want to build a reliable data warehouse and set up analytics?

Describe the data sources of your company and the main questions to analytics. Our Big Data engineers will prepare an architectural diagram and cost calculation within 24 hours.

Confidentiality

Official NDA contract for system audit

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We will contact you within 15 minutes during business hours.

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