We deliver and migrate data warehouses on AWS technologies

We’ll help you with Amazon Redshift, Aurora, Kinesis, Glue, QuickSight and other tools

acterys, Data science a AI, microsoft fabric

Amazon Web Services

We develop data and analytics solutions in the cloud on the Amazon Web Services platform. We’ll help you with architecture design, methodologies, security, environment setup and development. We’d be happy to involve your own team in development and train them. We utilize components from Redshift, Aurora, Data Migration Services, Kinesis, QuickSight, Glue and other companies.

Dolphins in numbers

14

years on the market

3000+

number of participants in the BI Academy

140 mil.+

annual turnover

178+

number of certifications achieved

Data warehouse in AWS

We’ll set up regular uploading of data from your information systems to a data warehouse in Amazon Web Services, ensuring it is linked, cleaned, historicized, understandable and optimized for reporting.

Data warehouse in AWS

DWH development in AWS

We’ll design the optimal data architecture in AWS to ensure maximum efficiency and performance. We’ll set up the environment to minimize costs.

We’ll create a self-service data model where users can easily and swiftly create optimized reports on their own.

We’ll develop ETL procedures in AWS Glue using maximum automation and AI to minimize development costs.

We’ll migrate your existing data warehouse to AWS. We’ll ensure a smooth transition, optimize performance and minimize costs.

We’ll provide comprehensive data warehouse operations support. We’ll monitor performance, resolve incidents and regularly optimize the system to keep it running at its best.

Data lake and lake house in AWS

We’ll ensure regular uploading of structured and unstructured data from your information systems, sensors, IoT devices and other internal and external sources to a data lake so that it is available in one place at any time. We’ll make selected data available in a user-optimized lake house layer for easy analysis and reporting.

Data lake and lake house in AWS

Data lake and lake house development in AWS

We’ll design the optimal data lake house architecture in AWS, combining the benefits of a data lake and data warehouse. You’ll save on costs and get a flexible and powerful platform for data analysis.

We’ll enable agile development of reports and analyses by combining data from the data lake and warehouse layers so you can respond to business needs in hours, days at most.

By generating ETL procedures from metadata, we’ll automate development of the data lake layer so that adding new data sources takes days instead of months.

We’ll clean the data needed for regular reporting and analysis, integrate it into a dimensional layer, and link it to give you a comprehensive view of your business. 

During implementation, we’ll put data governance processes in place and describe the data to make it user-friendly and ready for your AI applications.

It's easy with us

We know how to do it.

1.

Non-binding consultation

We will identify your needs and evaluate how Amazon Web Services can help you work with data more efficiently. Free of charge and without obligation.

2.

Solution proposal

We will design the optimal solution tailored to your requirements, including architecture, integration and specific tools.

3.

Implementation

We will ensure fast and smooth deployment of Amazon Web Services into your environment, including configuration and team training.

4.

Support

We provide long-term technical support and regular optimization so that your solution always works at 100%.

How we’ve helped clients

We automated data retrieval and optimized it for reporting, developed clear reports and dashboards. We provide support for reporting and above-the-floor applications, as well as training in data analysis through the BI Academy.

Case study

We analyzed the university’s source systems and created a data warehouse for reporting. We defined user needs and developed the DWH architecture design and model, data integration, transformation in DWH, interfacing with Power BI, and report templates.

Case study

We analyzed data sources to create a data warehouse for reporting in Power BI. The project included analysis of the acquiring environment, data architecture design, source integration, data cleaning and transformation, report development and solution handover.

Case study

We analyzed the university’s source systems and created a data warehouse to process data for reporting. We prepared report templates, set up sharing and trained users to enable them to further develop the solution in-house.

Case study

We carried out a needs analysis, created a systems map and data architecture and implemented a report analysis. We also provided Power BI training for users and administrators.

Case study

Need help implementing AWS?

We’d be happy to advise you. Send us a message with your contact information and we’ll get back to you as soon as possible.

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