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AWS Retail Kazakhstan

Technodom

Data Lake / Data Warehouse Modernization · Marketing Personalization

By migrating infrastructure and analytics to the cloud, businesses around the world remove challenges such as limited scalability and efficiency in analyzing data. Technodom wanted to modernize its data infrastructure to overcome challenges such as data silos, which hindered its ability to make data-driven decisions quickly.

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Increasing Development Productivity by 40 Percent Using AWS Working alongside AWS, Technodom began a gradual migration in 2022.
Making use of 47 AWS services, the company chose to design a new data architecture on AWS to overcome these challenges.
Technodom increased data quality by 70 percent and development productivity by 40 percent using AWS.
The company developed a digital transformation strategy to improve business growth in 2022.

Reported impact

Business outcome

By migrating infrastructure and analytics to the cloud, businesses around the world remove challenges such as limited scalability and efficiency in analyzing data. Technodom is a leading national retail network of electric and computer equipment in Kazakhstan. Key reported metrics include The company developed a digital transformation strategy to improve business growth in 2022.; Making use of 47 AWS services, the company chose to design a new data architecture on AWS to overcome these challenges.; Increasing Development Productivity by 40 Percent Using AWS Working alongside AWS, Technodom began a gradual migration in 2022..

  • Productivity Improvement
  • Revenue Growth
  • Scalability

Solution

Technodom Migrates to AWS to Modernize Its Data Architecture and Grow Its Business By migrating infrastructure and analytics to the cloud, businesses around the world remove challenges such as limited scalability and efficiency in analyzing data. Technodom is a leading national retail network of electric and computer equipment in Kazakhstan. It was one of the first in the region to migrate to Amazon Web Services (AWS) for a big data and machine learning (ML) project. Technodom wanted to modernize its data infrastructure to overcome challenges such as data silos, which hindered its ability to make data-driven decisions quickly. Using AWS, Technodom increased development productivity, enhanced customer experience, and improved personalization with new ML models, and modernized its data architecture Migrating to AWS for a Modernized Data Architecture Founded in 2002, Technodom has 79 stores and 92 pick-up points across 28 cities in Kazakhstan.

Data flow

Source data enters the Technodom workflow through its application services, processed with Amazon SageMaker.

Solution architecture

1 components · 1 layer
  1. Compute
    • Amazon SageMaker Supports model training, fine-tuning, evaluation, or managed inference for the customer workload.

Architecture clues

  • Technodom uses AWS services to manage and secure its data lake, to orchestrate complex workflows, and to handle near real-time data processing.
  • “By using ML models built on AWS SageMaker and automating the ML pipeline, we provide personalized recommendations for each store’s product assortment, leading to a sales increase of up to 15 percent in certain categorie

Evidence from the source

By migrating infrastructure and analytics to the cloud, businesses around the world remove challenges such as limited scalability and efficiency in analyzing data. Technodom wanted to modernize its data infrastructure to overcome challenges
Increasing Development Productivity by 40 Percent Using AWS Working alongside AWS, Technodom began a gradual migration in 2022.
Making use of 47 AWS services, the company chose to design a new data architecture on AWS to overcome these challenges.
Technodom increased data quality by 70 percent and development productivity by 40 percent using AWS.
The company developed a digital transformation strategy to improve business growth in 2022.
Transparency Evidence & methodology 90/100 reference value · 87% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value90/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    4 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance8/10
    The publication date comes from publisher-controlled metadata.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. AWS

    Technodom customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/technodom
    Source type
    Vendor-published customer story
    Published
    Sep 9, 2024
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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