Big Data Solution Implementation: The Essence

Big data implementation gains strategic importance across all major industry sectors, helping mid-sized and large organizations successfully handle the ever-growing amount of data for operational and analytical needs. When properly developed and maintained, big data solutions efficiently accommodate and process petabytes of XaaS users' data, enable IoT-driven automation, facilitate advanced analytics to boost enterprise decision making, and more.

Key steps of big data solution development: feasibility study, conceptualization and planning, architecture design, development and QA, deployment, support and maintenance.
Team: Project manager, business analyst, big data architect, big data developer, data engineer, data scientist, data analyst, DataOps engineer, DevOps engineer, QA engineer, test engineers.
Costs: from $200K to $3M for a mid-sized organization, depending on the project scope.

For 9 years, ScienceSoft designs and builds efficient and resilient big data solutions with scalable architectures able to withstand extreme concurrency, request rates, and traffic spikes.

Key Components of a Big Data Solution

Below, ScienceSoft’s big data experts provide an example of a high-level big data architecture and describe its key components.

Data sources are the initial point of the big data pipeline. It can be real-time data from social media, payment processing systems, IoT sensors, etc., as well as historical data from relational databases, web server log files, etc.
Data storage, also referred to as a data lake, holds voluminous data of different formats for further processing. Its main difference from a data warehouse (DWH) is that a data lake stores structured, unstructured, and semi-structured, while a DWH stores structured data only.

Note: If you want to learn more about the purpose and differences of data lakes and DWHs, check out the article on the topic by Alex Bekker, ScienceSoft’s Head of Data Analytics Department.

A stream ingestion engine receives real-time messages from the data sources and immediately directs them to real-time (stream) processing. The key advantage of this component is the high data ingestion speed that is required to quickly analyze and react to messages such as readings from industrial IoT sensors or consumer activity on an ecommerce website. Apart from undergoing stream processing, real-time messages get accumulated in the data lake to be used for batch processing according to the computation schedule.
Batch processing deals with huge volumes of historical data using parallel jobs. Stream data processing deals with real-time data, which means that it is processed in smaller volumes as soon as it is captured. Depending on your big data needs, a specific solution might enable only batch or only stream data processing, or combine both types as shown in the sample architecture above.

Batch processing of data at rest

Best for: processing large datasets and running repetitive non-time-sensitive jobs that facilitate analytics tasks (for billing, revenue reports, daily price optimization, demand forecasting, etc.).

Enables processing of large volumes of data.
May require less computing power to run simple batch jobs.
The results aren’t immediately available because of high latency. The time from when a message is received to when it is processed ranges from minutes to days.

Stream processing of real-time events

Best for: tasks that require immediate data processing, such as payment processing, traffic control, personalized recommendations on ecommerce websites, or burglary protection systems.

Suitable for processing data of lower volume.
More computing power is required for the stream processing solution to be active at all times (for on-premises solutions).
The processed data is always up-to-date and ready for immediate use due to low latency (milliseconds to seconds).

Once processed, data can go to a data warehouse for further analytical querying or directly to the analytics modules.
Lastly, the analytics and reporting module helps reveal patterns and trends in the processed data, then use these findings to enhance decision-making or automate certain complex processes (e.g., management of smart cities).
With orchestration that acts as a centralized control to data management processes, repeated data processing operations get automated.

Big Data Implementation Roadmap

Real-life big data implementation steps may vary greatly depending on the business goals a solution is to meet, data processing specifics (e.g., real-time, batch processing, both), etc. However, from ScienceSoft’s experience, there are six universal steps that are likely to be present in most projects.

Step 1.

Feasibility study

Analyzing business specifics and needs, validating the feasibility of a big data solution, calculating the estimated cost and ROI for the implementation project, assessing the operating costs.

Big data implementation is a long-term process that may entail unnecessary expenses if its feasibility is not properly investigated from the start. ScienceSoft’s big data consultants prepare a comprehensive feasibility study report with tangible gains and possible risks, and communicate the findings to all project stakeholders. This way, our customers can be sure that each dollar spent will bring value.

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Step 2.

Requirements engineering and big data solution planning

Defining the type of data (e.g., SaaS data, SCM records, operational data, images and video) to be collected and stored, estimated data volume and the required data quality metrics (for data consistency, accuracy, completeness, auditability, etc.).
Forming a high-level vision of the future big data solution, outlining:
- Data processing specifics (batch, real-time, or both).
- Required storage capabilities (data availability, data retention period, etc.).
- Integrations with the existing IT infrastructure components (if applicable).
- The number of potential users (e.g., from 100+ for an enterprise solution to 1M+ for a customer-oriented app).
- Security and compliance (e.g., HIPAA, PCI DSS, GDPR) requirements.
- Analytics processes (e.g., data mining, predictive analytics, machine learning) that need to be introduced to the solution, and more.
Choosing a deployment model: on-premises vs. cloud (public, private) vs. hybrid.
Selecting an optimal technology stack.
Preparing a comprehensive project plan with timeframes, required talents, and budget outlined.

Step 3.

Architecture design

Creating the data models that represent all data objects to be stored in databases, as well as associations between them, to get a clear picture of data flows, the ways data of certain formats will be collected, stored, and processed in the solution-to-be.
Mapping out data quality management strategy and data security mechanisms (data encryption, user access control, redundancy, etc.).
Designing the optimal big data architecture that enables data ingestion, processing, storage, and analytics.

As your business grows, the number of big data sources and the overall volume of data produced is likely to grow as well. For instance, Uber’s big data platform stored tens of terabytes of data in 2015, but by 2017, its volume exceeded 100 petabytes. This makes scalable architecture the cornerstone of efficient big data implementation that can save you from costly redevelopments down the road.

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Step 4.

Big data solution development and testing

Setting up the environments for development and delivery automation (СI/CD pipelines, container orchestration, etc.).
Building the required big data components (e.g., ETL pipelines, a data lake, a DWH) or the entire solution using the selected techs.
Implementing data security measures.
Performing quality assurance in parallel with development. Conducting comprehensive testing of the big data solution, including functional, performance, security and compliance testing. If you’re interested in the specifics of big data testing process, see expert guide by ScienceSoft.

Step 5.

Big data solution deployment

Preparing the target computing environment and moving the big data solution to production.
Setting up the required security controls (audit logs, intrusion prevention system, etc.).
Launching data ingestion from the data sources, verifying the data quality (consistency, accuracy, completeness, etc.) within the deployed solution.
Running system testing to validate that the entire big data solution works as expected in the target IT infrastructure.
Selecting and configuring big data solution monitoring tools, setting alerts for the issues that require immediate attention (e.g., server failures, data inconsistencies, overloaded message queue).
Delivering user training materials (FAQs, user manuals, a knowledge base) and conducting Q&A sessions and trainings, if needed.

Step 6.

Support and evolution (continuous)

Establishing support and maintenance procedures to ensure trouble-free operation of the big data solution: resolving user issues, refining the software and network settings, optimizing computing and storage resources utilization, etc.
Evolution may include developing new software modules and integrations, adding new data sources, expanding the big data analytics capabilities, introducing new security measures, etc.

Implement a Big Data Solution with Professionals

With 33 years in IT and 9 years in big data services, ScienceSoft can design, build, and support a state-of-the-art big data solution or provide assistance at any stage of big data implementation.

Implementation consulting

Business case delivery and feasibility study.
Creating a detailed project roadmap, time and budget estimations.
Selecting a deployment model (on-premises, cloud, hybrid) and optimal technology stack, designing the architecture of the solution-to-be.
PoC delivery (for complex projects).
Recommendations on big data quality and security management, regulatory compliance measures.
Actionable insights on optimization of computing resources and cloud storage (if applicable).

Plan my big data solution

Development

In-depth analysis of your big data needs.
Holistic conceptualization of a big data solution: architecture design, tech stack selection, a comprehensive project plan with tangible KPIs.
End-to-end big data solution development and testing.
Deploying the big data solution into the existing IT infrastructure, developing the necessary integrations and establishing required security controls.
User training.
Support, maintenance, and continuous evolution (if required).

Build my big data solution

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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Why Choose ScienceSoft for Big Data Implementation

10 years in big data solutions development.
34 years in data analytics and data science.
Experience in 30+ industries, including manufacturing, retail, healthcare, education, logistics, banking, energy, telecoms, and more.
700+ experts on board, including big data solution architects, DataOps engineers, and ISTQB-certified QA engineers.
A Microsoft partner since 2008.
An AWS Select Tier Services Partner.
Strong Agile and DevOps culture.
ISO 9001 and ISO 27001-certified to ensure robust quality management system and the security of the customers' data.
For the second straight year, ScienceSoft USA Corporation is listed among The Americas’ Fastest-Growing Companies by the Financial Times.

Selected Big Data Projects by ScienceSoft

Development of a Big Data Solution for IoT Pet Trackers

Design and development of an easily scalable big data solution that processes 30,000+ events per second from 1 million devices.
Enabling real-time pet location tracking, as well as sending and receiving photos, videos, and voice messages via an app.
Setting automatic hourly, weekly, or monthly reports with the option to tune the reporting period.

project details

Big Data Implementation for Advertising Channel Analysis

Development of a new analytical system that handles the continuously growing amount of data and enables advertising channel analysis in 10+ countries.
Processing more than 1,000 different types of raw data (archives, XLS, TXT, etc.).
Enabling cross analysis of almost 30,000 attributes and facilitating multi-angled data analytics for different markets.

project details

Big Data Consulting for a Leading Internet of Vehicles Company

In-depth audit of the existing big data solution: its architecture, documentation, available data sources, etc.
Designing the requirements for the solution-to-be and outlining their impact on the business.
High-level design of key architecture components.

project details

Big Data Consulting and Training for a Satellite Agency

Preparing comprehensive educational materials to introduce the client to the big data landscape with a focus on the space industry.
Training sessions to the top management and technical team in the form of workshops with Q&A sessions.
In-depth analysis of strong and weak points of the planned big data solution’s architecture.

project details

Big Data Implementation for a Multibusiness Corporation

Development of a big data solution that offered a 360-degree customer view as well as functionality for retail analytics, stock management optimization, and employee performance assessment.
Setting up a data warehouse and around 100 ETL processes.
An analytical server with 5 OLAP-cubes and about 60 dimensions in total.

project details

Hadoop Lab Deployment and Support

Deployment of an on-premises Hadoop lab for one of the largest US colleges.
Complex solution consisting of HDFS, Hive, YARN, Oozie, Spark, and more.
Creating comprehensive user guides for the solution, including step-by-step self-service instructions.

project details

Typical Roles on ScienceSoft’s Big Data Teams

Project manager

Plans and oversees a big data implementation project; ensures compliance with the timeframes and budget; reports to the stakeholders.

Business analyst

Analyzes the business needs or app vision; elicits functional and non-functional requirements; verifies the project’s feasibility.

Big data architect

Works out several architectural concepts to discuss them with the project stakeholders; creates data models; designs the chosen big data architecture and its integration points (if needed); selects the tech stack.

Big data developer

Assists in selecting techs; develops big data solution components; integrates the components with the required systems; fixes code issues and other defects on a QA team’s notices.

Data engineer

Assists in creating data models; designs, builds, and manages data pipelines; develops and implements a data quality management strategy.

Data scientist

Designs the processes of data mining; designs ML models; introduces ML capabilities into the big data solution; establishes predictive and prescriptive analytics.

Data analyst

Assists a data engineer in working out a data quality management strategy; selects analytics and reporting tools.

DataOps engineer

Helps streamline big data solution implementation by applying DevOps practices to the big data pipelines and workflows.

DevOps engineer

Sets up the big data solution development infrastructure; introduces CI/CD pipelines to automate development and release; deploys the solution into the production environment; monitors solution performance, security, etc.

QA engineer

Designs and implements a quality assurance strategy for a big data solution and high-level testing plans for its components.

Test engineer

Designs and develops manual and automated test cases to comprehensively test the operational and analytical parts of the big data solution; reports on the discovered issues found and validates the fixed defects.

Depending on a big data project’s scope and specifics, ScienceSoft can also involve talents like front-end developers, UX and UI designers, BI engineers, etc.

Sourcing Models for Big Data Solution Implementation

In-house big data solution development

Full control over the project.

Possible lack of talents with required skills, insufficient team scalability.
All managerial efforts are on your side.

Turn to ScienceSoft if you need expert help with planning your big data implementation project.

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Team augmentation

On-demand availability of talents with the required skills.
Sufficient control over the project.

Extra efforts to achieve smooth cooperation between the teams.

Turn to ScienceSoft if you need big data experts to assist your in-house team.

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Outsourced big data solution development

A fully managed & experienced big data team.
Established best practices for big data implementation.
A quick project start.

Risks of choosing an inept vendor.

Turn to ScienceSoft if you need a reliable vendor to build a robust and secure big data solution.

Turn to us

Technologies ScienceSoft Uses to Develop Big Data Solutions

Distributed storage

Database management

Data management

Data streaming and stream processing

Batch processing

Data warehouse, ad hoc exploration and reporting

Machine learning

Programming languages

Big Data Solution Implementation Costs

The total cost of a big data project depends on multiple factors, and is estimated after in-depth analysis of project specifics. Among key cost considerations are:

The type and complexity of business objectives the solution needs to meet (e.g., providing fault-tolerant streaming services, handling extreme customer demand, fraud prevention, price optimization).
The solution’s performance, availability, scalability, security, and compliance requirements.
The number and diversity of data sources, the complexity of data flows.
The volume and nature (structured, semi-structured, unstructured) of data to be ingested, stored, and processed by the solution.
The type of data processing (real-time, batch, both), the data quality thresholds (consistency, accuracy, completeness, etc.) that need to be achieved.
The number and complexity of required big data solution components.
The testing efforts required, the ratio of automated and manual testing, etc.
The team members’ seniority level, the chosen sourcing model.

The cost of end-to-end development of a big data solution may vary from $200K to $3M for a mid-sized organization. However, if one or several modules of a big data solution are needed, the costs will be much lower.

Want to find out the cost of your big data project?

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About ScienceSoft

ScienceSoft is a global IT consulting and software development company headquartered in McKinney, TX. Since 2013, we have been delivering end-to-end big data services to businesses in 30+ industries. Being ISO 9001 and ISO 27001-certified, we ensure robust quality management system and full security of our customers’ data.

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