End-to-End Big Data Applications
Use Cases, Architecture, Gains
In big data services since 2013, ScienceSoft helps organizations across 30+ industries build tailored end-to-end big data applications that support smooth business operations and enable accurate analytics results.
The Global Big Data and Analytics Market Is to Reach $662 Billion by 2028
According to the 2023 report by Research and Markets, the global big data and analytics market is expected to reach $662 billion by 2028, compared to $337 billion in 2022, growing at a CAGR of 14.48%. The market growth drivers are the increasing volume and variety of data, the wide adoption of cloud computing, and the rising need for data-driven decisioning.
The major applications of big data include customer analytics (accounts for the biggest market share), supply chain analytics, marketing analytics, pricing analytics, workforce analytics, and more.
In the 2023 NewVantage Survey that features over 100 leading organizations from diverse industries (incl. retail, finance, manufacturing, healthcare, IT, telecoms, media, and education), 92% of respondents report delivering measurable business value from their data-driven investments.
End-to-End Big Data Applications: The Essence
End-to-end big data applications enable fault-tolerant, low-latency processing of massive data volumes coming from multiple sources, in various formats, and in both scheduled and unpredictable patterns.
End-to-end big data applications can be operational or analytical and can also present a combination of both types.
Operational end-to-end big data apps enable the processing of large, real-time, interactive workloads while preserving stable performance to ensure a smooth user experience. Examples of operational big data apps include social media, ridesharing platforms, e-marketplaces, large-scale IoT systems.
Analytical end-to-end big data apps use batch or real-time analysis of voluminous, multi-source data to enable predictive and prescriptive analytics, generate real-time alerts, and more.
High-Level Architecture of an End-to-End Big Data Application
The architecture of an end-to-end big data application can include all or several components present in the picture above.
- The data sources include internal sources like mobile and web apps gathering user-generated data, IoT devices (sensors, smart devices, wearables), and external sources (e.g., market and weather data, social media content).
- Raw data storage is a data lake that stores data for further batch processing. At this stage, any data is stored in its initial format and can be structured, unstructured, or semi-structured.
- The real-time (stream) message ingestion engine captures real-time messages (e.g., IoT readings, user app requests) and directs them for stream processing to enable immediate system response. The ingestion engine also directs real-time messages to the raw data storage to preserve it in the initial form (incl. metadata) for future analytics purposes.
- The stream processing module processes new messages or events in near real-time. In contrast, the batch module does it according to the established computation schedule (e.g., every hour, 24 hours, month), enabling cost-effective processing of voluminous historical data. If you want to know more about their differences and advantages, check out our dedicated guide.
- Analytics data storage is a data warehouse storing highly structured pre-processed data as well as analytical results ready for querying in big data analytics applications. The analytics output then goes either to the reporting tools or back to the data sources (e.g., sending action triggers to IoT actuators or returning ecommerce customers’ search requests).
- The AI/ML engine is an optional module that analyzes the structured data from the analytics data storage, produces intelligent insights (e.g., detecting pre-failure equipment conditions, predicting customer behavior), and feeds these insights into the corresponding processing modules. An accompanying ML training module is needed to update the engine, continuously improving its accuracy.
- Data orchestration and governance system automates repeating data processing operations (e.g., transforming source data and moving it between the architecture modules) and ensures complete data quality, protection, and compliance throughout the analytics lifecycle.
Defining ‘Big’ in Big Data Applications
With big data being quite a buzzword recently, many businesses wonder if their data volume is large enough to be considered ‘big.’ Often, the focus is on the numbers, like 100TB or 1PB. However, this approach is misleading.
Your data is big as soon as you see that traditional technologies and out-of-the-box solutions can’t handle it anymore. If conventional techs can’t enable the smooth operation of your data-rich apps or provide analytics results on time, your data is already voluminous enough to warrant big data implementation.
Big Data Application for IoT Pet Tracking
The solution: A GPS tracking app simultaneously analyzes data from millions of pet wearables. In case of a critical event (e.g., a pet leaving its geo-fenced zone or the device battery running low), the app sends a push notification to the owner.
- Stream processing of 30,000 events per second from 1 million devices.
- Easily scalable infrastructure that can handle any increase in the number of users and data volume.
Key techs: Apache Kafka, Apache Spark, MongoDB
Big Data App for Advertising Channel Analysis in 10+ Countries
The solution: A desktop analytics app for a leading market research company that enables cross-analysis of almost 30,000 attributes across different markets with easy-to-navigate reports.
- Up to 100x faster query processing compared to the legacy analytics solution.
- Processing ~1,000 different types of raw data for comprehensive advertising analytics.
Key techs: Apache Hadoop, Apache Hive, Apache Spark, Python (ETL), Scala (Spark, ETL).
Data Analytics Implementation for a Multibusiness Corporation
The solution: A centralized data analytics solution for mobile and desktop BI users that allowed the Customer to get a 360-degree customer view, optimize stock management, and access the employees’ performance.
- Consolidation of data from 15 disparate sources, including CRM, Magento, Google Analytics, and dedicated hotel, restaurant, and wellness systems.
- 100 ETL processes, 5 OLAP cubes, and 60 dimensions.
Key techs: Microsoft SQL Server, Microsoft SQL Analysis and Integration Services, Python, Microsoft Power BI.
Why Entrust Your Big Data Project to ScienceSoft
- Since 1989 in custom software development, data analytics, and data science.
- Since 2013 in big data solution development.
- Practical experience in 30+ industries, including manufacturing, healthcare, retail, BFSI, logistics, energy, and telecoms.
- A team of business analysts, solution architects, data engineers, and project managers with 5–20 years of experience.
- A Microsoft Solutions Partner and AWS Select Tier Services Partner.
- ISO 9001 and ISO 27001-certified to guarantee top service quality and complete security of our customers’ data.
Our happy clients
Big Data: You Envision It, We Make It Work
Over the past decade, we have successfully implemented big data to serve the unique needs of dozens of our clients — and we are ready to apply our skills and expertise to make it work for you.
How data accessibility fuels financial growth
“For a typical Fortune 1000 company, just a 10% increase in data accessibility will result in more than $65M additional net income.” – Richard Joyce, Senior Analyst at Forrester.
May Your Big Data App Meet and Exceed Your Expectations
And if you need a skilled team to deliver it, ScienceSoft is here to lend a helping hand.