Move your data warehouse
to the modern cloud
Flexible infrastructure that grows with your company and lets you pay only for the resources you actually use.
Let’s talkWe drive the success of leaders:
Migrating your data warehouse to the cloud is an investment in your company’s future.
With on-premise solutions, you pay for maximum load, which you typically don’t use. Cloud optimizes costs, adjusting performance to demand.
A solution that grows with your business
A cloud data warehouse lets you select components tailored to your current business needs. As your company grows, you can scale easily while maintaining efficient data processing and controlling costs.
Charge only for usage
A cloud data warehouse has almost zero initial cost. As your company grows, the fees scale with actual usage, so you only pay for increased performance when you need it.
Quick setup with ready-made components
Cloud solutions are built quickly using ready-made components, just like building blocks. You choose the right ones from different categories (PaaS, SaaS, IaaS, etc.), knowing that each is efficient and secure.
Easy scalability
Do you have more data and need greater performance right away? The cloud easily adapts to increased data or performance needs without downtime, a challenge with on-premise systems.
Better protection against data loss
Cloud-based data warehouses offer superior backup and recovery solutions across multiple servers, minimizing downtime and ensuring your datasets remain accessible after unexpected failures.
A future-ready solution
Cloud data warehouse software integrates seamlessly with technologies like AI, machine learning, and Big Data analytics, enabling real-time insights without costly updates or hardware upgrades.
See what else you can gain by migrating your data warehouse
Migration to the cloud with Alterdata: efficiently and with support every step of the way
Analyzing requirements and creating a plan
- Understanding expectations and defining goals
- Choosing the best cloud platform
Designing the architecture
- Developing a cloud architecture
- Planning migration
- Implementing security standards
Populating the cloud with data
- Extracting data from existing systems
- Integrating and transforming data
- Uploading historical data to the cloud
Implementing and configuration
- Creating solutions tailored to each cloud environment
- Building a data warehouse
- Configuring analytical tools
Testing and validating the ready solution
- Providing support to the client during the migration process
- Conducting tests of results and readiness level
- Continuously monitoring performance and security
Deploying, training, and monitoring
- Deploying the ready solution
- Training users
- Monitoring and optimizing
- Supporting the development of the cloud with new technologies, e.g., AI
- Implementing tools for managing and transforming data in the warehouse
Switch from on-premise to a better solution
6 signs that you should consider moving to the cloud
Silos and lack of data democratization
Separate reporting in transactional systems means limiting access to IT departments, and reducing usability for other users.
Slow reporting
Due to low performance in data processing in on-premise data warehouses, you have to wait for key reports for hours.
High infrastructure costs
On-premise data warehouses require significant upfront investments in hardware, ongoing maintenance, and electricity, driving up costs.
Limited disk space
Growing datasets overwhelm server storage, making expansion difficult and expensive.
Lack of scalability
Increasing data and new sources slow down on-premise systems, reducing overall performance.
Low performance under load
Transactional and cyclical queries run slower when processing multiple reports and analytics simultaneously.
Clouds at your fingertips
When choosing cloud solutions tailored to your goals and processes, we focus solely on objective criteria such as scalability, performance, cost, and task alignment.
We use the most efficient technologies from the three largest providers to ensure maximum productivity and flexibility both now and in the future.
Cloud migration with Alterdata: quick results and zero unnecessary stress.
Cloud tailored to your needs
We take into account the data sources and use, the technologies in the client's company, and their preferences.
Solutions are built around your team’s competencies, with training provided for smooth operation.
End-to-end implimentation
We support the client throughout the entire cloud migration process, explaining what we do and the benefits they will gain.
We ensure service continuity and avoid transferring problems to the cloud.
Solutions for success
We combine an understanding of business goals with the ability to create solutions that help achieve them effectively.
We don’t just implement the cloud; we also help you use it efficiently.
A broad tech-stack
We create solutions based on efficient technologies from Azure, AWS, and Google Cloud.
With these, we build platforms perfectly tailored to the clients' needs.
Certified specialists
Our experts possess cutting-edge knowledge and extensive experience across diverse industries and business models.
This expertise allows them to select solutions that ensure long-term success.
Continuous improvements
We build the foundation of an efficient system and continuously improve it by constantly updating.
We add functionalities when the company faces new challenges.
Find out how much you can gain with the cloud
Discover our clients’ success stories
How data-driven advertising management helped an AMS agency maintain its leading position.
For the AMS team, we created a reliable and user-friendly ecosystem by integrating key data from external providers, including traffic measurements from mobile devices.
Thanks to the solutions offered by Alterdata, AMS was able to provide clients with access to key metrics, giving them greater control over campaigns and optimization of advertising spend.
Implementation of Business Intelligence and integration of distributed databases in PŚO
For Polish Open FIber, we built an advanced Data Hub architecture based on an efficient and scalable Google Cloud ecosystem. We implemented Power BI as a Business Analytics tool and also trained its users.
This improved data availability and accelerated the creation of interactive reports and dashboards.
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 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 visualizations 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.
Bartosz Szymański
Data Strategy and Customer Relations Director