Data Warehouse
Modernization
Get your data warehouse ready for AI. We’re modernizing your data architecture to ensure the scalability, speed, and quality needed to implement advanced analytics and Generative AI.
Let’s talk
We empower leaders:
Why does a traditional data warehouse
hinder the implementation of AI?
Many organizations are unable to move to the AI production phase because their current data architecture relies on outdated legacy systems. Traditional data warehouses were not designed with the speed and scale required by modern LLM models, which creates a bottleneck in your development.
Siloed data
When data is locked away in siloed systems, AI models are unable to access the full business context needed to make accurate inferences.
High maintenance costs
Spending on outdated on-premises infrastructure wastes budget that should be supporting innovation and the development of your AI projects.
Data is refreshed too infrequently
The performance of older systems hinders more frequent updates. Even if a given business process requires faster access to information, outdated infrastructure creates a bottleneck and artificially slows down the operation of AI algorithms.
Low reliability of information
The lack of automated data cleaning processes directly leads to AI agents producing erroneous results and flawed analytical outcomes.
Lack of business context (Semantic Layer)
Older systems do not allow for the creation of a single, centralized map of business concepts. As a result, AI algorithms and agents “get lost” in the table structures, failing to understand the true meaning of the data the company possesses.
Your data warehouse shouldn’t be a barrier anymore
Contact usThe Foundations of Modern AI-Driven Architecture
Moving to the Cloud (Cloud-Native)
Moving to the Cloud (Cloud-Native)
- Migrating to platforms such as BigQuery, Snowflake, or Databricks ensures native optimization for advanced ML and AI algorithms. The serverless architecture guarantees instant resource scaling and significant cost savings for storing and analyzing big data sets.
Building a Data Lakehouse
Building a Data Lakehouse
- This is a modern combination of the flexibility of the Data Lake (for data used in LLM models) with the rigor, structure, and security characteristic of traditional systems. This architecture creates a Single Source of Truth, eliminating costly data duplication and streamlining the work of BI and Data Science teams.
Pipeline Automation (ELT/ETL)
Pipeline Automation (ELT/ETL)
- We implement processes that automatically clean, standardize, and prepare data for the effective training of artificial intelligence models. By leveraging the Data Build Tool (dbt) and DataOps standards, we reduce the time it takes to deliver finished analyses from weeks to a matter of days.
Real-time Streaming
Real-time Streaming
- We enable data processing as soon as it is generated, which is essential for recommendation systems and real-time customer behavior predictions. Replacing batch reports with data streaming allows companies to make decisions based on live data, responding to market changes immediately.
AI-specific features, or
what sets us apart
Go beyond standard analytics with unique engineering solutions designed to scale Generative AI systems.
Semantic Layer: We help you build a clear layer of business definitions within your data architecture. The more precise and well-designed this structure is, the better your AI agents will perform.
Vector Databases: We prepare your infrastructure for vector databases—essential for the operation of RAG systems (integration with company documents).
Data Governance & Quality: We implement automated data quality monitoring so you can be sure that AI doesn’t draw incorrect conclusions based on inaccurate data.
How are we handling the modernization?
Turn data chaos into powerful AI tools with our proven modernization methodology.
AI Readiness Assessment
We audit your current technology stack to identify bottlenecks and develop a detailed roadmap for implementations with realistic ROI potential.
Migration Strategy
We design a secure path to modern architecture that minimizes the risk of data loss and ensures full control over cloud costs.
“Downtime-free” modernization
We implement new solutions in parallel with your existing operations, ensuring the continuity of business reporting while laying a modern foundation for AI.
Launch of the AI Pilot Model
We conclude the process by implementing a proof-of-concept (PoC) that demonstrates the value of the new structure in practice and delivers tangible business benefits from day one.
Let’s build a foundation that actually generates income
Contact FormDiscover our clients’ success stories
We helped Celsium build a data warehouse that reduced costs by PLN 180,000 per year
We integrated data from meters, SCADA, billing, and weather systems into a single data warehouse on Google Cloud Platform. We created advanced ETL processes, data quality control mechanisms, and dashboards in Tableau to support daily analysis of heat production and consumption.
The result? Meter failures detected in one day (previously one month), operational data updated three times a day, and significant savings thanks to heat source optimization and better demand balancing.
We built a modern data warehouse in GCP for PŚO
We helped Polski Światłowód Otwarty design and implement a scalable Data Lake architecture on Google Cloud Platform. We integrated 13 data sources, created automated ELT processes, access security, and a data model that serves as a single source of truth within the organization.
The result? Independence in reporting, rapid integration of new systems, readiness for future needs, and cost savings by eliminating on-premise infrastructure.
We helped AMS leverage data from DOOH media and maintain its position as a leader in outdoor advertising
We built a modern data ecosystem for AMS, a leader in OOH and DOOH advertising. We combined data from media, internal systems, Proxi.cloud, and CitiesAI to create a unified data warehouse in BigQuery with near real-time analysis.
The result? Data-driven targeting, campaign automation, better results for customers, and a stronger market position thanks to programmatic buying based on actual reach.
We helped Tutlo automate data integration and build a modern real-time ETL
In collaboration with the Tutlo team, we designed and implemented a data integration architecture based on serverless Google Cloud components. The system enables data synchronization from dozens of sources—including CRM—with full monitoring, CI/CD automation, and readiness for further scalability.
The result? A stable and flexible data ecosystem, ready for process automation, ML projects, and dynamic development of the educational platform.
We helped FunCraft forecast ROI and optimize UA budgets in the mobile gaming industry
We implemented a comprehensive BI solution for an American game studio, integrating data from Adjust, stores, and advertising platforms into the BigQuery warehouse. We built advanced dashboards in Looker Studio and predictive ROI models that enable accurate budget decisions—even with a long return on investment cycle.
The result? The FunCraft marketing team works faster, more efficiently, and with full control over their data.
Your data holds great potential. Ask us how to make the most of it
Why should choose Alterdata?
We combine expert experience, extensive technical knowledge, and a flexible approach to collaboration to create data solutions that are truly tailored to your organization’s needs.
Comprehensive End-to-End Implementation
We manage the entire process: from consulting and technology selection, through data warehouse construction, to the development, maintenance, and optimization of solutions. This ensures that our clients receive consistent support at every stage of their data-related work, without having to coordinate multiple independent vendors.
Data Expert Team
We bring together the expertise of data engineers, analysts, data scientists, IT architects, and business consultants to address both technological and business needs. Our team helps translate an organization’s goals into concrete solutions that effectively support decision-making and business growth.
Technology Neutrality
We choose tools based on the goal, not the other way around. We work with popular cloud and analytics technologies, including Google Cloud, Azure, AWS, Snowflake, Databricks, Power BI, Tableau, and Looker. Thanks to our extensive knowledge of these tools, we recommend the solutions best suited to the client’s situation, rather than pushing a single technology.
Flexible Model of Collaboration
We offer support exactly when you need it, ranging from individual specialists to a Data Team as a Service model, without the need to build a full in-house team. This allows you to quickly expand your organization’s capabilities and leverage expert knowledge in a way that aligns with your current needs.
Business-Specific Solutions
We design services and architecture tailored to specific requirements, budgets, industries, company sizes, and business objectives. We treat each implementation as a unique case to ensure that the technology supports the processes, workflows, and priorities of the organization in question.
Secure Architecture
We create scalable, secure solutions designed to support organizational growth, handle increasing data volumes, and facilitate migration to modern cloud environments. We ensure access control, stability, and scalability so that the data platform can grow alongside your business.
Tech stack: the foundation of
our work
Discover the tools and technologies that power the solutions created by Alterdata.
Google Cloud Storage enables data storage in the cloud and provides high performance, offering flexible management of large datasets. It ensures easy data access and supports advanced analytics.
Azure Data Lake Storage is a service for storing and analyzing structured and unstructured data in the cloud, created by Microsoft. Data Lake Storage is scalable and supports various data formats.
Amazon S3 is a cloud service for securely storing data with virtually unlimited scalability. It is efficient, ensures consistency, and provides easy access to data.
Databricks is a cloud-based analytics platform that combines data engineering, data analysis, machine learning, and predictive models. It processes large datasets with high efficiency.
Microsoft Fabric is an integrated analytics environment that combines various tools such as Power BI, Data Factory, and Synapse. The platform supports the entire data lifecycle, including integration, processing, analysis, and visualization of results.
Google BigLake is a service that combines the features of both data warehouses and data lakes, making it easier to manage data in various formats and locations. It also allows processing large datasets without the need to move them between systems.
Google Cloud Dataflow is a data processing service based on Apache Beam. It supports distributed data processing in real-time and advanced analytics.
Azure Data Factory is a cloud-based data integration service that automates data flows and orchestrates processing tasks. It enables seamless integration of data from both cloud and on-premises sources for processing within a single environment.
Apache Kafka processes real-time data streams and supports the management of large volumes of data from various sources. It enables the analysis of events immediately after they occur.
Pub/Sub is used for messaging between applications, real-time data stream processing, analysis, and message queue creation. It integrates well with microservices and event-driven architectures (EDA).
Google Cloud Run supports containerized applications in a scalable and automated way, optimizing costs and resources. It allows flexible and efficient management of cloud applications, reducing the workload.
Azure Functions is another serverless solution that runs code in response to events, eliminating the need for server management. Its other advantages include the ability to automate processes and integrate various services.
AWS Lambda is an event-driven, serverless Function as a Service (FaaS) that enables automatic execution of code in response to events. It allows running applications without server infrastructure.
Azure App Service is a cloud platform used for running web and mobile applications. It offers automatic resource scaling and integration with DevOps tools (e.g., GitHub, Azure DevOps).
Snowflake is a platform that enables the storage, processing, and analysis of large datasets in the cloud. It is easily scalable, efficient, and ensures consistency as well as easy access to data.
Amazon Redshift is a cloud data warehouse that enables fast processing and analysis of large datasets. Redshift also offers the creation of complex analyses and real-time data reporting.
BigQuery is a scalable data analysis platform from Google Cloud. It enables fast processing of large datasets, analytics, and advanced reporting. It simplifies data access through integration with various data sources.
Azure Synapse Analytics is a platform that combines data warehousing, big data processing, and real-time analytics. It enables complex analyses on large volumes of data.
Data Build Tool simplifies data transformation and modeling directly in databases. It allows creating complex structures, automating processes, and managing data models in SQL.
Dataform is part of the Google Cloud Platform, automating data transformation in BigQuery using SQL query language. It supports serverless data stream orchestration and enables collaborative work with data.
Pandas is a data structure and analytical tool library in Python. It is useful for data manipulation and analysis. Pandas is used particularly in statistics and machine learning.
PySpark is an API for Apache Spark that allows processing large amounts of data in a distributed environment, in real-time. This tool is easy to use and versatile in its functionality.
Looker Studio is a tool used for exploring and advanced data visualization from various sources, in the form of clear reports, charts, and interactive dashboards. It facilitates data sharing and supports simultaneous collaboration among multiple users, without the need for coding.
Tableau, an application from Salesforce, is a versatile tool for data analysis and visualization, ideal for those seeking intuitive solutions. It is valued for its visualizations of spatial and geographical data, quick trend identification, and data analysis accuracy.
Power BI, Microsoft’s Business Intelligence platform, efficiently transforms large volumes of data into clear, interactive dashboards and accessible reports. It easily integrates with various data sources and monitors KPIs in real-time.
Looker is a cloud-based Business Intelligence and data analytics platform that enables data exploration, sharing, and visualization while supporting decision-making processes. Looker also leverages machine learning to automate processes and generate predictions.
Terraform is an open-source tool that allows for infrastructure management as code, as well as the automatic creation and updating of cloud resources. It supports efficient infrastructure control, minimizes the risk of errors, and ensures transparency and repeatability of processes.
GCP Workflows automates workflows in the cloud and simplifies the management of processes connecting Google Cloud services. This tool saves time by avoiding the duplication of tasks, improves work quality by eliminating errors, and enables efficient resource management.
Apache Airflow manages workflows, enabling scheduling, monitoring, and automation of ETL processes and other analytical tasks. It also provides access to the status of completed and ongoing tasks, as well as insights into their execution logs.
Rundeck is an open-source automation tool that enables scheduling, managing, and executing tasks on servers. It allows for quick response to events and supports the optimization of administrative tasks.
Python is a programming language, also used for machine learning, with libraries dedicated to machine learning (e.g., TensorFlow and scikit-learn). It is used for creating and testing machine learning models.
BigQuery ML allows the creation of machine learning models directly within Google’s data warehouse using only SQL. It provides a fast time-to-market, is cost-effective, and enables rapid iterative work.
R is a programming language primarily used for statistical calculations, data analysis, and visualization, but it also has modules for training and testing machine learning models. It enables rapid prototyping and deployment of machine learning.
Vertex AI is used for deploying, testing, and managing machine learning models. It also includes pre-built models prepared and trained by Google, such as Gemini. Vertex AI also supports custom models from TensorFlow, PyTorch, and other popular frameworks.
FAQ
Is modernizing the data warehouse essential for implementing AI?
Yes, because traditional on-premises systems do not offer the computing power scalability required to train ML models. Modernization enables the creation of a data foundation that eliminates silos and delivers high-quality information in real time.
How much does it cost to migrate a data warehouse to the cloud (e.g., BigQuery)?
The cost depends on data volume and process complexity, but the pay-as-you-go cloud model allows for significant IT cost optimization. Thanks to its modern architecture, you pay only for the resources you actually use, avoiding the costly maintenance of your own servers.
What is a Data Lakehouse, and why is it better for AI?
A Data Lakehouse combines the advantages of a data lake (flexibility for PDF files and video for LLMs) with the structure of a data warehouse (tabular structures). It is an ideal solution for AI, as it provides a single source of truth for BI analysts and machine learning engineers.
How can you ensure the security of company data when implementing RAG models?
We use private vector databases and secure connections within your cloud, ensuring that data never leaves your protected environment. We implement rigorous data governance to ensure that AI models have access only to authorized resources.
How long does the data architecture modernization process take?
We typically deliver the initial results in the form of a Proof of Concept (PoC) within 4–6 weeks. The full migration and automation of ELT pipelines depends on the scale of the project, but our zero-downtime approach allows for the gradual implementation of changes without disrupting business operations.
Is my old data suitable for training artificial intelligence?
Most data requires “processing” – which is why data quality automation is a critical step. We implement data cleaning and denormalization processes that transform raw data into valuable fuel for AI algorithms.
What are vector databases, and do I need them?
Vector databases (such as Pinecone, Weaviate, or BigQuery’s native solutions) are essential if you plan to implement document-based conversational systems (RAG). They enable AI to instantly perform contextual searches across thousands of company files.
What are the business benefits (ROI) of modernizing a data warehouse?
The main benefits include shorter time-to-market, lower operating costs, and the ability to implement predictive analytics. Companies with a modern data stack make decisions 30–50% faster than their competitors.
Will the upgrade affect how my current reports work in Power BI / Tableau?
No. Our methodology is designed for seamless upgrades, which means that new data sources are integrated in parallel. Your current business dashboards remain active, with the added benefit of faster and more accurate data.
How does Alterdata’s approach differ from that of standard IT companies?
We’re not just a migration company – we’re an AI-first partner. We build data architectures designed to be usable by production models, implementing solutions such as a Feature Store and advanced MLOps right from the start.