Big Data Services
Big data services encompass a comprehensive range of activities, including strategy consulting, architecture design, software development, analytics, AI/ML implementation, support, and more. We at ScienceSoft help companies maximize data value and achieve business goals with big data analysis.
- During 34 years in data analytics and data science, we have been satisfying companies’ diverse analytical needs (including the need for advanced analytics), which makes us fully understand the transformation you’re undergoing.
- We hold partnerships with Microsoft, Amazon, Oracle and other tech leaders to keep pace with the technological advancements and the evolution of the data analytics landscape.
- An expert team of architects, developers, DataOps engineers, ISTQB-certified QA engineers, data scientists, project managers, and business analysts with 5–20 years of experience.
Quality-first approach based on a mature ISO 9001-certified quality management system.
ISO 27001-certified security management based on comprehensive policies and processes, advanced security technology, and skilled professionals.
Transparent and flexible pricing.
We collaborate with companies from 70+ countries. Some of our prominent clients include:
Big data consulting
- Big data implementation/evolution strategies and detailed roadmaps.
- Recommendations on data quality management.
- Big data solution architecture + an outline of an optimal technology stack.
- User adoption strategies.
- A proof of concept (for complex projects).
Big data development
- Big data needs analysis.
- Big data solution architecture and design.
- Big data solution development (a data lake, DWH, ETL/ELT setup, data analysis (SQL and NoSQL), big data reporting and dashboarding).
- Setup of big data governance procedures (big data quality, security, etc.)
- ML models development.
Big data support
- Big data solution administration.
- Big data software updating.
- Adding new users and handling permissions.
- Big data management.
- Big data cleaning.
- Big data backup and recovery.
- Big data solution health checks.
- Big data solution performance monitoring and troubleshooting.
Big data managed analytics services
- Big data solution infrastructure setup and support.
- Big data extraction and management.
- ML model development and tuning.
- Predefined and ad hoc reports (within several weeks after our cooperation starts).
- Big data solution evolution.
Technical Components of a Big Data Solution We Cover
Data quality management
The Financial Times Includes ScienceSoft USA Corporation in the List of the Americas’ Fastest-Growing Companies 2023
For the second year in a row, ScienceSoft USA Corporation ranks among 500 American companies with the highest revenue growth. This achievement is the result of our unfailing commitment to provide high-quality IT services and create best-value solutions that meet and even exceed our clients’ expectations.
Big data warehousing
- Storing data about business processes, finances, resources, customers, etc. for analytical querying and reporting.
- Corporate performance analytics.
- Revenue, cost and investment analytics.
- Predicting, forecasting, planning (performance, revenue, capacity, etc.) with all interdependencies.
- Collecting, processing and storing large volumes of operational data (transactional data, production process data, asset data, employee data, plans, etc.)
- Detecting deviations and undesirable patterns in a company’s operations (production processes, product distribution, etc.)
- Recognizing bottlenecks (equipment failure, resource unavailability, etc.), conducting cause-effect analysis.
- Forecasting (demand, capacity, inventory, etc.)
- What-if scenario modeling and operational risk management.
Industry-specific big data analytics use cases
- Analyzing manufacturing data (equipment year, model, sensor data, error messages, engine temperature, etc.) to predict equipment failures and the remaining useful time in real time.
- Real-time monitoring of production processes, production equipment data, materials usage, etc., to identify factors leading to production time increase and delays for production optimization.
- Capturing, storing, and analyzing patient-related data (doctor notes, medical images, EHR/EMR data, R&D results, etc.).
- Real-time patient monitoring and alerting on trends and patterns requiring the doctor’s attention.
- Personalized care plans recommendations.
- Mining claims data to identify fraudulent activity.
- Forecasting the supply demand, supplier risks, etc., to enable healthcare supply chain optimization and planning.
- Analyzing integrated transactional data, interaction events, customer behavior in real time, identifying complex AML transactions, creating advanced risk models, etc., to identify potential fraud and fraud patterns.
- Consolidating and analyzing data on assets and liabilities and conducting credit risk assessment, liquidity risk assessment, counterparty risk analysis, etc., to mitigate financial risks.
Transportation and logistics
- Tracking and analyzing real-time sensor data (cargo state, location, etc.) to make the delivery process fully transparent and ensure high-quality delivery of sensitive goods.
- Analyzing driver behavior, maintenance needs, weather data, traffic data, fuel consumption data, etc., in real time to enable dynamic route optimization.
Retail and ecommerce
- Analyzing customer demographic data, data from mobile apps, in-store purchases, etc. to identify customer paths and behavior to optimize merchandizing, provide personalized product recommendations, discounts, etc.
- Forecasting customer demand, analyzing the key attributes of past and current products/services and commercial success of their offerings, and using ML-driven recommendations to create new products/services.
- Consolidating and analyzing data from social media, web visits, call logs, and more to personalize customer support, launch tailored customer retention campaigns, etc.
- Analyzing customer transactions, spend patterns, predicting future customer actions with ML models to assess customer lifetime value, target marketing and sales offers to your best customers, etc.
Oil and gas
- Analyzing log and sensor data from different types of equipment in real time and putting analytics results into operations to facilitate predictive equipment maintenance.
- Analyzing drilling and production process data, data generated from seismic monitors, etc., to identify new oil deposits.
- Analyzing sensor and historical production data and building ML-based predictive models to measure well production and understand the usage rate.
- Analyzing the network usage trends and patterns and using sophisticated models to forecast areas with excess capacity and optimize the network capacity.
- Analyzing overall customer satisfaction, identifying customer churn patterns, and recommending the most relevant products/services to increase customer retention.
Big Data Technologies We Use
Here’s the list of technologies most frequently used in our big data projects:
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.
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 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 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.
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.
Big data processing
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.
A large US-based jewelry manufacturer and retailer relies on ETL pipelines built by ScienceSoft’s Spark developers.
ScienceSoft's Python developers and data scientists excel at building general-purpose Python apps, big data and IoT platforms, AI and ML-based apps, and BI solutions.
ScienceSoft's Java developers build secure, resilient and efficient cloud-native and cloud-only software of any complexity and successfully modernize legacy software solutions.
ScienceSoft's C++ developers created the desktop version of Viber and an award-winning imaging application for a global leader in image processing.
Our Customers Say
We needed a proficient big data consultancy to deploy a Hadoop lab for us and to support us on the way to its successful and fast adoption. ScienceSoft's team proved their mastery in a vast range of big data technologies we required: Hadoop Distributed File System, Hadoop MapReduce, Apache Hive, Apache Ambari, Apache Oozie, Apache Spark, Apache ZooKeeper are just a couple of names. ScienceSoft's team also showed themselves great consultants. Whenever a question arose, we got it answered almost instantly.
Kaiyang Liang Ph.D., Professor, Miami Dade College
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