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Big Data Solution Implementation

Plan, Tools, and Costs

Working with big data technologies since 2013, ScienceSoft designs and implements secure and scalable big data solutions for businesses in 30+ industries.

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Big Data Solution Implementation - ScienceSoft
Big Data Solution Implementation - ScienceSoft

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 $500K to $5M, 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.

Key components of a big data solution - ScienceSoft

  • 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 the processing 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.

ScienceSoft

ScienceSoft

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.
ScienceSoft

ScienceSoft

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.

ScienceSoft

ScienceSoft

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.
ScienceSoft

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.
ScienceSoft

ScienceSoft

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.
ScienceSoft

ScienceSoft

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

Testimonial from Miami Dade College - ScienceSoft

Why Choose ScienceSoft for Big Data Implementation

  • 9 years in big data solutions development.
  • 33 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 Gold Partner with 9 Gold Competencies.
  • 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.
  • Listed in The Americas’ Fastest-Growing Companies 2022 by Financial Times.

Selected Big Data Projects by ScienceSoft

Development of a Big Data Solution for IoT Pet Trackers

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.
Big Data Implementation for Advertising Channel Analysis

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.
Big Data Consulting for a Leading Internet of Vehicles Company

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.
Big Data Consulting and Training for a Satellite Agency

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.
Big Data Implementation for a Multibusiness Corporation

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.
Hadoop Lab Deployment and Support

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.

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

Technologies ScienceSoft Uses to Develop Big Data Solutions

Distributed storage

Apache Hadoop
Amazon S3
Azure Blob Storage

Database management

Data management

Apache Airflow
Talend
Informatica
Zaloni
Apache ZooKeeper
Azkaban

Data streaming and stream processing

Apache Kafka
Apache NiFi
Apache Spark
Apache Storm
Azure IoT Hub
Azure Stream Analytics
Amazon Kinesis

Batch processing

MapReduce
Amazon EMR
Apache Hive
Apache Pig

Data warehouse, ad hoc exploration and reporting

Machine learning

MATLAB
GNU Octave
Mahout
Caffe
Maxnet
TensorFlow
Keras
Torch
OpenCV
Theano
Apache Spark MLlib
Scikit Learn
Gensim
SpaCy
Amazon Machine Learning
Amazon SageMaker
Azure Machine Learning
Google Cloud AI Platform
Einstein

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 $500K to $5M. However, if one or several modules of a big data solution are needed, the costs will be much lower.

Find Out the Cost of Your Big Data Project

ScienceSoft’s big data consultants are ready to analyze your needs and targets and provide an accurate estimation of your big data project cost.

About ScienceSoft

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.