Microservices Consulting
Since 2016, ScienceSoft offers all-around microservices consulting services – from pure advisory to implementation and support – for mid-sized and large enterprises, startups and mature ISVs.
Microservices consulting helps introduce reliable and cost-effective microservices that are easy to maintain and scale.
Microservices-based software design deliverables
- A detailed software requirements specification.
- Non-functional software requirements.
- Microservices-based solution architecture design.
- App infrastructure design and configuration guides.
Project planning documentation for microservices implementation
- A feasibility study and a business case for microservices implementation.
- An implementation project plan with schedules.
- A RACI matrix for a microservices implementation project.
- A risk management plan for the implementation project.
- Business KPIs and technical KPIs to measure value from microservices implementation.
- A strategy for implementation costs optimization.
Deliverables for the microservices development process
- Documented microservices code with unit tests.
- Microservices API descriptions.
- Cloud infrastructure configuration, integration with the on-premises infrastructure (if any).
- CI/CD configuration in the cloud.
- (for modernization projects) Data migration scripts.
Microservices QA documentation
- A QA strategy, a test plan with the testing scope, types and levels of tests, reporting procedures, etc.
- Test cases and checklists.
- Test automation architecture design, configuration of optimal testing tools and frameworks, automated tests scripts (API and UI tests).
- Test reports.
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Owing to ScienceSoft’s experience in distributed modular systems, we cooperated with them on the evolution of our Azure-based product for accounts payable management. ScienceSoft was to cover end-to-end development of an intelligent paperless invoice processing module for the product.
ScienceSoft’s developers used the microservices approach to ensure the system’s scalability, which is critical for us as our user base is growing. They selected and implemented techs that ensured the system’s availability and fault-tolerance in the future. We were particularly pleased with neat alignment of the developed module with our requirements.
The new software module performs stably even under heavy load, which helps provide high-quality user experience for our customers.
Wadih Pazos, Chief Operating Officer, Paramount WorkPlace
Our Tech Stack for Microservices Projects
Frameworks and libraries for building microservices
Containerization and orchestration
Function-as-a-service
Messaging
We use Kafka for handling big data streams. In our IoT pet tracking solution, Kafka processes 30,000+ events per second from 1 million devices.
Databases / data storages
SQL
Our Microsoft SQL Server-based projects include a BI solution for 200 healthcare centers, the world’s largest PLM software, and an automated underwriting system for the global commercial insurance carrier.
We’ve implemented MySQL for Viber, an instant messenger with 1B+ users, and an award-winning remote patient monitoring software.
Azure SQL Database is great for handling large volumes of data and varying database traffic: it easily scales up and down without any downtime or disruption to the applications. It also offers automatic backups and point-in-time recoveries to protect databases from accidental corruption or deletion.
ScienceSoft's team has implemented Oracle for software products used by GSK and AstraZeneca. We’ve also delivered Oracle-based SCM platform for Auchan, a retail chain with 1,700 stores.
ScienceSoft has used PostgreSQL in an IoT fleet management solution that supports 2,000+ customers with 26,500+ IoT devices. We’ve also helped a fintech startup promptly launch a top-flight BNPL product based on PostgreSQL.
NoSQL
Our Apache Cassandra consultants helped a leading Internet of Vehicles company enhance their big data solution that analyzes IoT data from 600,000 vehicles.
ScienceSoft has helped one of the top market research companies migrate its big data solution for advertising channel analysis to Apache Hive. Together with other improvements, this led tо 100x faster data processing.
We use HBase if your database should scale to billions of rows and millions of columns while maintaining constant write and read performance.
With ScienceSoft’s managed IT support for Apache NiFi, an American biotechnology corporation got 10x faster big data processing, and its software stability increased from 50% to 99%.
ScienceSoft used MongoDB-based warehouse for an IoT solution that processed 30K+ events/per second from 1M devices. We’ve also delivered MongoDB-based operations management software for a pharma manufacturer.
Clouds
Logging, monitoring and alerting tools
CI/CD
Configuration management
Infrastructure as code
Highlights of Microservices Projects by ScienceSoft
Development of an Azure-Based Software Product for Intelligent Invoice Data Capture
- A cross-functional team of a BA, web developers, QA engineers from ScienceSoft.
- Microservices to enable easy scaling and targeted improvements of the software product.
- Streamlined service communication, discovery, monitoring, provision, and update and facilitated local testing with Azure Service Fabric.
Development of an Azure-Based Software Product for vCIO Services Management
- Microservices-based SaaS.
- Azure services for a service mesh layer, load balancing, and automated container deployment and management.
- A monitoring component to ensure stable work of large distributed software.
- Automated functional regression, integration and cross-browser testing.
Java Back-end Development for an Innovative Hotel Self-service App
- Microservices to ensure quick ongoing Agile development and simplify app scaling processes.
- Spring 5 WebFlux stack of reactive technologies for communication between microservices.
- Reliable, secure, and reusable APIs for microservices to reduce time and efforts for future integrations.
L2, L3 Support of a Marketing Campaign Management Solution
- Ongoing configuration of solution infrastructure elements to allow the seamless introduction of new microservices and modules.
- Troubleshooting, bug fixing, introduction of changes upon request.
- Introduction of a monitoring system to immediately detect and prevent the expansion of dangerous issues. The system consists of queue message count, online database logs analysis, data consistency monitoring, etc.
Team Augmentation for Marketplace Development
- Gradual re-architecting of a monolithic application into microservices for easier scaling.
- Back-end development on Golang.
Microservices advisory
- Analyze the high-level business needs and microservices implementation drivers.
- Assess microservices feasibility.
- Create a business case for microservices implementation, TCO, expected ROI, NPV.
- Prepare a microservices implementation project plan.
- Identify risks of microservices implementation and prepare their mitigation plan.
- Design a microservices app architecture.
Microservices development
Advisory +
- Iterative development of microservices.
- Configuration of the microservices infrastructure.
- Integration of microservices.
- Microservices testing.
- Microservices-based app deployment to production.
- (on demand) Continuous KPI-based maintenance and improvement of the microservices ecosystem.
Microservices review and optimization
- Review functional and non-functional microservices app requirements.
- Review the granularity, architecture, communication and APIs of microservices and make recommendations for areas that need improvement.
- Design a new microservices architecture.
- Identify potential improvements in development and deployment processes, e.g., including continuous deployment pipeline, automated testing.
Rebuilding legacy apps into microservices
- Review the as-is monolithic application architecture.
- App modernization feasibility study.
- (optional) Review your software development and delivery practices and suggest improvements.
- Plan a new microservices-based architecture.
- Create a detailed roadmap for refactoring the monolith to microservices.
- Refactor the legacy app/help the in-house team to refactor the legacy app.
- Test and deploy the modernized app.
Enterprise-wide microservices adoption
- Analyze high-level business needs and microservices implementation drivers.
- Review microservices adoption readiness.
- (if needed) Introduce/improve infrastructure automation, cloud services usage, containerization, CI/CD, monitoring and management practices, etc.
- Provide a microservices cost optimization plan (optional).
- Define KPIs and tools to measure business value from microservices implementation.
- Select and prioritize applications to migrate to the microservices architecture.
- Develop microservices.
- Develop an effective API program.
- QA strategy, microservices test plan, test automation architecture design.
- Configuration of optimal testing tools and frameworks.
- Test environment setup and automated test data generation.
- Test automation script development and maintenance.
Microservices test artifacts are compliant with ISO/IEC/IEEE 29119-3:2013, including regular detailed reports on defects and test closure.
How Enterprises Benefit From the Right Microservices
Source: IBM’s research ‘Microservices in the enterprise, 2021: Real benefits, worth the challenges’.
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