15

5

the speed of management decision-making has increased

from 1-2 days to

times

reduced labor costs
for collecting reports

We integrated the holding's disparate IT systems to improve reporting accuracy and speed up management decision-making

Development of an analytical platform for data collection and reporting

РАЗРАБОТКА КОРПОРАТИВНЫХ ХРАНИЛИЩ И ОЗЕР ДАННЫХ

min

2-4

30

the integration time of one source has been reduced

from 3 days to

data sources integrated

We developed a scalable, regulatory reporting and analytics system to support tourism management decisions

Building a data warehouse and reporting system from scratch

РАЗРАБОТКА КОРПОРАТИВНЫХ ХРАНИЛИЩ И ОЗЕР ДАННЫХ

h

15%

20%

the cost of data storage has decreased

by

by

hardware costs have been reduced

We created a high-speed data warehouse for executive authorities, which provides rapid access to information and data analysis

Developing a Fast Data Warehouse

РАЗРАБОТКА КОРПОРАТИВНЫХ ХРАНИЛИЩ И ОЗЕР ДАННЫХ

2

5

the speed of decision-making has increased

times

labor costs for preparing reports have been reduced

Installation, maintenance, and development of a data platform based on solutions from Arenadata — ADB, ADH, ADPS, ADS, ADC, as well as platform migration to another OS

РАЗРАБОТКА КОРПОРАТИВНЫХ ХРАНИЛИЩ И ОЗЕР ДАННЫХ

times

1.5

30%

the accuracy of criteria calculation has been increased

by

the speed of criteria processing and flight display window generation has been increased

How a flexible solution allows for recalculating flight validity for any arbitrary date without changing the code

Development of an analytical system for recording and verifying waste removal trips for compliance with criteria

РАЗРАБОТКА КОРПОРАТИВНЫХ ХРАНИЛИЩ И ОЗЕР ДАННЫХ

times

Industrial Digital Ecosystems: Predictive Models and Architecture Development Challenges

The concept of a digital ecosystem (DES) based on predictive models and the development of complex, stable, interactive, self-organizing and reliable control systems for various industries in an article by Alexander Suleikin, PhD, CEO and Big Data systems architect at DUC Technologies

Получите рекомендации
от экспертов «ДЮК Технологии»

about the service

Data warehouse and data lake
development framework

Design

Development

Support

Design

01/03

1. Analysis of all data sources: source systems and entities, description of attributes, key and non-key fields

2. Creating Integration Contracts, defining integration keys and the type of data loading into the storage

3. Creation of data mart prototypes, creation of s2t documents for the marts

4. Designing the data model of the detail layer and the showcase layer

Development

02/03

1. Development of integrations

2. Development of a detailed layer

3. Development of data marts

4. Orchestration of data pipelines from the source to the formation of data marts, including data quality management processes and interaction with BI

Support

03/03

Troubleshooting system crashes and problems

improving the quality of management decisions based on data

increasing process efficiency

increasing business transparency and identifying bottlenecks

increasing sales by optimizing pricing policies and understanding competitors' prices

Business values

We calculate the price individually

We have implementation options for a wide range of needs.
Tell us about your company, and we'll find the perfect solution for you. Our specialists will contact you within 1 hour.

Получите рекомендации
от экспертов «ДЮК Технологии»

Data storage and processing

PostgreSQL

Greenplum

Hadoop

Clickhouse

MSSQL/Oracle/Vertica

Integrations and ETL

Apache NiFi 

Apache AirFlow 

PySpark

dbt

Data Quality
and Catalog

Great Expectations

Open Metadata

Visualization

Apache Superset

Redash

Qlik Sense

Airbyte

PXF, gpfdist

Flink

PowerBI

Yandex Data Lens

Grafana Loki

Teradata

Trino

Impala

Semantic layer

Cube

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