Python Consulting Services
Benefit from Experienced Consultants and Transparent Collaboration with ScienceSoft
In Python development since 2013 and in data analytics and data science since 1989, ScienceSoft offers comprehensive Python consulting services that stretch beyond pure advisory and may include practical help with implementation and support.
Python consulting services are a way to solve technical challenges of Python development at any stage of the app development project life cycle.
Scope of Python Consulting Services by ScienceSoft
For a new Python project
Development of a general-purpose Python app
- Designing the app architecture.
- Designing UX and UI.
- Configuring the required application infrastructure.
- Developing, integrating, testing and deploying the new Python-based app.
Development of a Python-based AI app
- Creating a data strategy.
- Data architecture design.
- Data warehousing with embedded data analytics capabilities.
- Training of machine learning models.
Development of a Python-based big data app
- Building a data lake.
- Building an operational data store and a data warehouse.
- Designing ETL/ELT processes.
- Big data quality management.
- Big data security management.
- Building OLAP cubes.
- Big data visualization.
Development of a Python-based BI solution
- Designing a BI software architecture.
- Setting up data governance (data quality management, data security, master data and metadata management, etc.)
- Developing BI solution components (a data lake, DWH, OLAP cubes, reports and dashboards).
- Adding data science capabilities, if necessary.
- BI solution quality assurance.
- Data migration, if necessary.
For existing Python code
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- Python development since 2013.
- Data analytics and data science since 1989.
- Consultants, solution architects and data scientists with 5-20 years of practical experience.
- Expertise with large Python-based projects: 25+ FTE, ~ 30-40 microservices and over 80 CI/CD pipelines.
- Competencies in advanced techs (big data, IoT, AI/ML, AR/VR, AVI, blockchain, image analysis, etc.).
- 700+ experts onboard, including PMs, data scientists, QA, security, DevOps, and support engineers, to form all-around consulting and development teams.
Our Python-Related Tech Expertise
Python development frameworks
ML libraries
IoT
Open-source enterprise solutions
Content management systems (CMS)
Clouds
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.
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 to 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.
Cloud services
AWS
We use Amazon Redshift to build cost-effective data warehouses that easily handle complex queries and large amounts of data.
We use Amazon DynamoDB as a NoSQL database service for solutions that require low latency, high scalability and always available data.
Azure
We leverage Azure Cosmos DB to implement a multi-model, globally distributed, elastic NoSQL database on the cloud. Our team used Cosmos DB in a connected car solution for one of the world’s technology leaders.
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.
Google Cloud Platform
We use Google Cloud Datastore to set up a highly scalable and cost-effective solution for storing and managing NoSQL data structures. This database can be easily integrated with other Google Cloud services (BigQuery, Kubernetes, and many more).
Big data
By request of a leading market research company, we have built a Hadoop-based big data solution for monitoring and analyzing advertising channels in 10+ countries.
A large US-based jewelry manufacturer and retailer relies on ETL pipelines built by ScienceSoft’s Spark developers.
Our Apache Cassandra consultants helped a leading Internet of Vehicles company enhance their big data solution that analyzes IoT data from 600,000 vehicles.
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.
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 to 100x faster data processing.
We leverage Apache ZooKeeper to coordinate services in large-scale distributed systems and avoid server crashes, performance and partitioning issues.
We use HBase if your database should scale to billions of rows and millions of columns while maintaining constant write and read performance.
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.
We use Amazon Redshift to build cost-effective data warehouses that easily handle complex queries and large amounts of data.
We use Amazon DynamoDB as a NoSQL database service for solutions that require low latency, high scalability and always available data.
We leverage Azure Cosmos DB to implement a multi-model, globally distributed, elastic NoSQL database on the cloud. Our team used Cosmos DB in a connected car solution for one of the world’s technology leaders.
We use Google Cloud Datastore to set up a highly scalable and cost-effective solution for storing and managing NoSQL data structures. This database can be easily integrated with other Google Cloud services (BigQuery, Kubernetes, and many more).
How We Deliver Python Consulting
Expert consultants
ScienceSoft’s consultants are senior Python developers and data scientists who can join the project any time and quickly grasp its specifics. All our consultants are reaching-out, action-taking individuals that are eager to bring fresh ideas and value.
Quick cooperation start and focus on long-term collaboration
Typically, our Python consulting starts with code review, architecture redesign, solving painful issues with app performance, scalability, etc. We like to start quickly and bring quick benefits:
Then, the engagement usually evolves into a long-term collaboration. ScienceSoft's Python consultants blend into a client’s team to guide them on the challenging aspects of Python application development and support. When it's needed, Python consultants can join the team temporally to execute tasks that require advanced skills.
Trackable cooperation success
ScienceSoft employs mature KPI metrics (e.g., Lead Time, Cycle Time, Deployment Frequency, Customer Satisfaction) for tracking project progress. We give you access to our log tools and send you regular progress reports to ensure full transparency of cooperation. We also welcome any custom KPIs from clients that they want to monitor.
Selected Python Projects by ScienceSoft
Implementation of a Data Analytics Platform for a Telecom Company
- Gathering raw data from 10+ sources.
- Building ROLAP cubes with 30+ dimensions and 10+ facts to enable regular and ad-hoc reporting.
- Designing multi-tenant analytics.
- Using Amazon Spot Instances to reduce the costs of AWS computing resources by 80%.
Technologies: Python, Apache Kafka, Amazon Simple Storage Service, Amazon Redshift, MQTT.
SaaS Application Re-Architecting and Modernization for a Pharma-Focused Advertiser
- Legacy code audit.
- Cleaning up revealed code and design issues to improve software maintainability and evolution.
- Migrating to the multi-tenant application architecture.
Technologies: Python, Flask, Redis, PostgreSQL, Elasticsearch, AWS Elastic Beanstalk.
Data Science Implementation for Sales Analysis and Forecasting
- Cleaning the historical data.
- Building statistical models and algorithms for accurate sales forecast per product category/brand/store.
- Providing recommendations on how to increase sales by up to 15%.
Technologies: Python, Microsoft SQL Server, Microsoft SQL Server Integration Services, Microsoft SQL Server Analysis Services.
Development of a Pet Tracking Solution
- Developing pet tracker software, iOS and Android mobile apps from scratch in 4 months.
- Enabling voice communication and nationwide GPS tracking and snapshotting of the surroundings with a 2-megapixel camera for a wearable pet tracker.
- Building the back end and two client apps for iOS and Android to manage the animal tracking devices.
Technologies: Python, Android SDK, iOS 9 SDK.
Modernization of Image Quality Assessment Software
- Delivering the Python script to automate several image quality assessment software modules and allow for testing of new cameras without human intervention.
- Optimizing the existing coding algorithms to streamline the processing of measurement data and reducing the overall assessment time for one camera.
Technologies: Python, C++, C#.
Code rules and guidelines
- Following the Python style guide.
- Creating descriptive names for code variables.
- Leaving comments on what particular code functions do.
- Providing full code documentation with dependencies in a README file.
Code review practices
- Ad hoc review.
- Walkthrough.
- Pull request.
- Inspection.
Code quality metrics
- Maintainability index (MI).
- Cyclomatic Complexity (CC).
- Depth of Inheritance.
- Class Coupling.
- Lines of Code.
- Halstead Volume.
Long-term Python skills augmentation
We offer Python consultants and data scientists to help with challenging tasks in your Python-based projects.
- On-demand availability.
- Easy scaling up and down.
Python app development and evolution
ScienceSoft’s team takes over the responsibility for the design and development of a complete Python app or its part and offers the following cooperation options.
- Independent work (our PMs handle all the necessary communication and reporting according to the schedule).
- Work in close daily collaboration with your other teams. There may be mixed teams of ScienceSoft's and your employees.
Fixed price
Best for: Python tasks/projects with specific, measurable clear-cut deliverables.
You pay the price established by a contract.
Time & Material, Time & Material with a monthly/quarterly cap
Best for: Advisory activities (e.g., Python app architecture design/re-design), agile Python app development, Python app evolution (introducing substantial code changes or adding new functionality).
You receive the end-of-the-month invoice based on the hours or efforts reported per month (under the stated upper limit in case of T&M with a cap).
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