Interactive analytics | Amazon Athena
Big data processing | Amazon EMR
Data warehousing | Amazon Redshift
Interactive analytics | Amazon Kinesis
Operational analytics | Amazon OpenSearch
Dashboards and visualizations | Amazon Quicksight
Visual data preparation | AWS Glue DataBrew
Real-time data movement | AWS Glue, Amazon Managed Streaming for Apache Kafka (MSK), Amazon Kinesis Data Streams, Amazon Kinesis Data Firehose, Amazon Kinesis Video Streams, AWS Database Migration Service
Object storage | Amazon S3, AWS Lake Formation
Backup and archive | Amazon S3 Glacier, AWS Backup
Data catalog | AWS Glue, AWS Lake Formation
Third-party data | AWS Data Exchange
Frameworks and interfaces | AWS Deep Learning AMIs
Platform services | Amazon SagеMaker
Interactive analytics | Amazon Athena
Big data processing | Amazon EMR
Data warehousing | Amazon Redshift
Interactive analytics | Amazon Kinesis
Operational analytics | Amazon OpenSearch
Dashboards and visualizations | Amazon Quicksight
Visual data preparation | AWS Glue DataBrew
Real-time data movement | AWS Glue, Amazon Managed Streaming for Apache Kafka (MSK), Amazon Kinesis Data Streams, Amazon Kinesis Data Firehose, Amazon Kinesis Video Streams, AWS Database Migration Service
Object storage | Amazon S3, AWS Lake Formation
Backup and archive | Amazon S3 Glacier, AWS Backup
Data catalog | AWS Glue, AWS Lake Formation
Third-party data | AWS Data Exchange
Frameworks and interfaces | AWS Deep Learning AMIs
Platform services | Amazon SagеMaker
Industry | Problem solved | Input Data | Outcome | Tech stack | Project duration |
---|---|---|---|---|---|
Financial Industry | Managing rewards points for the clients transactions | Raw transactions data with PCI information being removed | Amount of rewards points for every client in structured data | EC2, Docker, S3, Glue, Redshift, QuickSight | 1,5 years |
Financial Industry | Gift cards processing to keep track of statistics on money spent, amount of activated cards and major spending directions | Raw transactions data with PCI information being removed | Processed data with analysis and statistics for each type of cards | Lambda, s3, Glue, Redshift, PowerBI | 2 years |
Financial Industry | Solution for data ingestion and reporting (Financial reporting for banking) | SharePoint lists, CSV data | Processed data ready for analysis | Azure: Data Factory, Spark / DataBricks, Scala, Azure DevOps | 6 months |
Aircraft Industry | Processing the data from aircraft's engines to make predictions and analysis on their conditions and trends | Raw full flight data | Processed data with predictions and errors that can trigger the alarms | SQS, S3, Step Functions, Glue, Redshift, DynamoDB, SageMaker, PowerBI | 1,5 years |
Automotive | Smart factory Industry 4.0 solution for IoT and master data processing and reporting | IoT streaming data (SCV, OPC), SAP Idocs, relational databases | Lambda arch with hot notifications and cold analysis | Azure: IoT Hub, SAP, AKS, CosmosDb, Data Explorer, PowerBI, Angular, Azure DevOps | 1 year |
Automotive | Migration of huge monolith app to Azure (lift and shift / Containerization) | IoT streaming data (MQTT), master data | Application migration POC (no migration due to cost reasons (ORacle blocker) | Azure: IoT Hub, AKS, VMs, Pricing calc, Oracle, PostgreSQL, Oracle Cloud | - |
Retail | Implementation of cross-region reporting system for retail analysis | Microsoft Dynamics | Processed data, Microstragegy reports | Azure: AKS, SQL, .NET, Synapse | 3 years |
Oil and Gas | Digitalization and corporate data warehouse solution for corporation and compliance needs | IoT, SharePoint, manual, SCV, Oracle | Single point of truth for corporate data assets with the corresponding analytics apps | SQL Server, Oracle, Kalido DW, PowerBI | 5 years |