All company data available in one place
Integrating data from various sources improves its quality, eliminates manual work, and enhances the accuracy of analytics.
Let’s talkWe drive the success of leaders:
Consistent data enables better collaboration and accurate business decisions.
With data integration platforms, all data is combined into a centralized system, enhancing a company’s flexibility and responsiveness to business needs.
No data silos
Source integration breaks down barriers between departments, enabling consistent and integrated data access for users across the organization. By using advanced data integration software, teams can access, process, and analyze datasets, reducing errors and improving decision-making.
Quick access to data
Data integration solutions enable the extraction and processing of datasets without manual intervention. This eliminates inefficiencies, reduces costs, and minimizes human error. Centralized databases allow faster analytics, reporting, and easier access for users.
Better system integration
Consolidating data in one place ensures better information quality not only for people but also for company systems, whose performance largely depends on this data.
Higher quality data
Consistent data formats, no duplicates, or false data lead to greater precision in work. It also enables using data to streamline business processes, such as personalizing customer experiences.
Faster and more accurate insights
Integrating data sources and formats accelerates analysis and increases the precision of insights. It supports the identification of threats and opportunities, and highlights areas for improvement, such as customer service.
Shorter time-to-market
Using data integration platforms and automated processes, businesses can speed up access to insights and accelerate their create, transform, load (ETL) pipelines. This allows companies to deliver products and services before the competition does.
Gain momentum for your company’s growth
Our data source integration philosophy
Data integration is not a one-time action, but a process that evolves as the business grows.
With our data integration platform, we educate clients about the importance of data integration tools, best practices, and measurable benefits, such as:
- methods tailored to specific sources (e.g., databases, APIs, corporate systems): batch, stream, full load, increment, etc.
- choosing the ELT or ETL strategy for data integration
- leveraging data integration software, whether ready-made or customized for client-specific needs, and selecting optimal tools
Data integration is the first step towards building trust in data and making accurate decisions that support short-term successes and long-term growth.
Better data leads to better business decisions
Four challenges in a company that data source integration can easily solve:
Incorrect data and lack of integration
Without unified data systems, poor quality and integration errors lead to faulty analysis. A robust data integration solution ensures accurate, actionable data.
Manual work reduces efficiency
Manually combining datasets wastes time and increases errors. Data integration software automates workflows, boosting efficiency.
Difficult access to information
Lack of data centralization creates conflicting sources of truth. A single integration platform ensures reliable, accessible databases for all users.
Unnecessary expenses
Without integration, you can’t eliminate certain systems that are completely unnecessary when optimizing.
Integration of sources with Alterdata: expertise, experience, and a business-driven approach.
End-to-end
We plan, design, and implement data integration tailored to your needs and business goals.
We provide advice on the best solutions based on our experience.
A wide tech-stack
We use the latest and most efficient technologies from three leading cloud solution providers.
This allows us to offer an integration perfectly tailored to your needs.
Certified specialists
Our experts stay current with the latest trends and have hands-on experience across various industries and business models.
These skills enable them to select solutions that ensure success.
Solutions for success
We combine an understanding of business goals with data integration to deliver solutions that drive results.
Our priority is efficiency and delivering maximum value to our clients.
Tailored services
We don't sell off-the-shelf products. We create tailored solutions that achieve your business goals.
We prioritize stakeholder feedback, ensuring all integrations are effective, scalable, and aligned with your business processes.
Convenient collaboration
We assemble a dedicated team to support your data integration needs, offering flexible availability and cost-efficient service.
You only pay for the time used, making our integration services reliable and adaptable to your evolving requirements.
Integrate your data and boost company’s efficiency
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.
Methods of data source integration
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