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 |
Big Data implies an enormous and steadily expanding set of data which cannot be processed and analyzed only by human intelligence. The significant advantages of Big data development include not only data processing, but also the possibility to visualize the obtained information.
Big Data is divided into Structured Data, Unstructured Data and Semi-Structured Data.
Any information that can be saved and processed in a fixed format is described as “structured” data. This type of data is the one that enables you to analyze information in the shortest time frame.
Unstructured data has no predefined structure and can be represented in text, video, audio or image form. Such information tends to be a bit more challenging to analyze, but it often provides the most relevant insights.
Semi-structured data does not have to be represented in any particular, clearly fixed form, but it should have tags or other markers to differentiate the presented items.
Big Data is extensively applied to process information in an effective manner and provide a superior competitive advantage in the marketplace. Big Data analysis helps to systematize information and identify invisible cause and effect relationships. Using Big Data, a web development company is able to optimize communication with its clients, improve operational efficiency, enhance service quality and minimize expenses.
Big data technologies refers to modern software tools that are used to operate and analyze various types of information.
Big data technology can be categorized into Operational Big Data Technologies, that interact with real-time activity and maintain the required data, and Analytical Big Data Technologies, which in turn analyze a larger amount of information and offer the most appropriate business solutions for the ongoing business operation.
A Big data developer is a specialist who designs and tracks data processing systems, creates the interface of the Big data model and upgrades it.
The main aim of such a professional is to implement data management strategies and principles in order to tackle a concrete task.
Big data analytics tools predict the results of your strategic decisions, thereby optimizing operational efficiency and minimizing your company’s risks. Ongoing market research enables you to explore the existing trends in detail, as well as foresee the preferences of potential clients.
Using Big Data in the working process of a web development company you can enhance customer service, boost the competitiveness of your business and not only increase your income, but also prevent any possible crucial errors.
Any modern technological solution required to simplify a number of business processes implies considerable expenses, particularly at the first stages of implementation. The final sum may vary, as it directly depends on the expertise of the chosen developer.