Big Data Solution Implementation | XpertsApp

Big Data Solution Implementation

Big data solutions are designed and implemented by XpertsApp for businesses across 30+ industries using big data technologies.

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Big Data Solution Implementation: The Essence

Across all major industries, big data implementation is gaining strategic significance, helping organizations handle the ever-growing amount of data for operational and analytical needs. In addition to accommodating and processing petabytes of XaaS users' data, big data solutions enable IoT-enabled automation, enhance enterprise decision-making, and more when properly developed and maintained.

  • The development of big data solutions involves the following steps: feasibility study, conceptualization, and planning, architecture design, development and quality assurance, deployment, support, and maintenance.
  • We have a team of project managers, business analysts, big data architects, big data developers, data engineers, data scientists, data analysts, DevOps engineers, QA engineers, and test engineers on our team.
  • It can cost anywhere between $500K and $5M, depending on the scope of the project.

Our scalable architectures are capable of handling extreme concurrency, requests, and traffic spikes that come with big data solutions.

Key Components of a Big Data Solution

XpertsApp's big data experts describe the key components of a high-level big data architecture below.

  • Big data pipelines begin with data sources. A combination of real-time and historical data is possible, such as data from social media, payment processing systems, IoT sensors, etc.
  • It holds voluminous data in different formats for later processing, and it is also called a data lake. Unlike data warehouses (DWHs), data lakes store structured, unstructured, and semi-structured data, while DWHs only store structured data.
  • Data sources deliver real-time messages to a stream ingestion engine for immediate processing. A key benefit of this component is its ability to ingest large amounts of data quickly, which makes it ideal for analyzing and reacting to messages such as those from industrial IoT sensors or consumer activity from eCommerce websites. As well as getting stream-processed, real-time messages accumulate in a data lake and are processed in batches in accordance with schedules.
  • Parallel jobs are used to process huge volumes of historical data in batch processing. Real-time data processing is concerned with capturing and processing small amounts of data in real time. In the sample architecture above, a solution might only allow batch or only stream data processing depending on your big data needs.

Batch processing of data at rest

The software is best suited for processing large datasets, as well as routine and non-time-sensitive tasks facilitating analytics (billing, revenue reports, price optimization, demand forecasts, etc.).

  • Processes large amounts of data efficiently.
  • Run simple batch jobs with less computing power.
  • High latency prevents results from being available immediately. Messages are processed within minutes to days after they are received.

Our scalable architectures are capable of handling extreme concurrency, requests, and traffic spikes that come with big data solutions.

Stream processing of real-time events

Suitable for: data processing tasks that require immediate response, such as payment processing, traffic control, or personalized recommendations on eCommerce websites.

  • It is suitable for processing smaller amounts of data.
  • In order for a stream processing solution to remain active at all times, more computing power is required (for on-premises solutions).
  • As a result of low latency, the processed data is always up-to-date and ready to use immediately (milliseconds to seconds).
  • Processed data can either be sent directly to analytics modules or to a data warehouse for further analysis.
  • As a final step, the analytics and reporting module helps reveal patterns and trends within the processed data, allowing users to enhance their decision-making or automate certain complex processes (e.g., managing smart cities).
  • Data management processes can be automated through orchestration, which serves as a centralized control.

Roadmap For Implementing Big Data

Implementing big data in the real world may involve a variety of steps, depending on the business objectives, data processing requirements (e.g., real-time or batch processing, or both), etc. XpertsApp has found that most projects follow six universal steps based on their experience.

Step 1. An evaluation of feasibility

Assessing business requirements and requirements, validating the feasibility of a big data solution, estimating costs and ROI, and analyzing operating costs.

Step 2. Planning big data solutions and requirements engineering

  • Identifying the types of data to be collected and stored (for example, SaaS data, SCM records, operational data, images, and videos) and the quality metrics to be used (for example, data consistency, accuracy, completeness, suitability, etc.).
  • Outlining a high-level vision for the future big data solution, including:
    • Specifics of data processing (batch, real-time, or both).
    • Data storage requirements (data availability, data retention period, etc.).
    • Integration of existing IT infrastructure components (if applicable).
    • A potential number of users (for example, 100+ for an enterprise solution to 1M+ for a customer-oriented app).
    • Compliance requirements (e.g., HIPAA, PCI DSS, GDPR).
    • Data mining, predictive analytics, machine learning, and more analytics processes need to be introduced to the solution.
    • Deployment models: on-premises vs. cloud (public, private), hybrid vs. on-premises.
    • Choosing the best technology stack.
    • Creating a comprehensive project plan with a timeline, talent requirements, and budget.

Step 3. Architecture design

  • Modeling the data objects to be stored in databases, as well as their associations, to gain a clear understanding of data flows, and how data of certain formats will be collected, stored, and processed.
  • Planning for data quality management and data security (encryption, access controls, redundancy, etc.).
  • Developing the best big data architecture for ingesting, processing, storing, and analyzing data.

Step 4. Development and testing of big data solutions

  • Building environments for continuous integration and continuous delivery (CI/CD pipelines, container orchestration, etc.).
  • Using the selected technologies, build the big data components (e.g., ETL pipelines, data lakes, DWHs).
  • Protecting data by implementing security measures.
  • Parallel to development, quality assurance is performed. Test big data solutions functionally, performance-wise, security-wise, and in compliance with regulatory requirements.

Step 5. Deploying a big data solution

  • 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, and 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 and 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 training, if needed.

Step 6. Evolution and support (continuous)

  • Supporting and maintaining the big data solution to ensure trouble-free operation: resolving user issues, optimizing network settings, and optimizing computing and storage resources.
  • As part of the evolution process, new software modules, integrations, data sources, big data analytics capabilities, and security measures may be developed.
  • Professionals can help you implement a Big Data solution.

Implement a Big Data Solution with Professionals

XpertsApp specializes in big data integration services, including designing, building, and supporting state-of-the-art solutions.

Implementation consulting

  • Developing a business case and conducting a feasibility study.
  • Time and budget estimations and a detailed roadmap for the project.
  • Designing the architecture of the solution-to-be, choosing a deployment model (on-premises, cloud, hybrid).
  • Delivering a proof of concept (for complex projects).
  • Regulation compliance measures, quality and security management recommendations for big data.
  • A list of actions that can be taken to optimize computing resources and cloud storage (if applicable).
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Development

  • An in-depth analysis of your big data requirements.
  • Design, select, and implement a complete big data solution: architecture, tech stack, and measurable KPIs.
  • Development and testing of end-to-end big data solutions.
  • Integration of the big data solution into the existing IT infrastructure, development of necessary integrations, and setting up required security controls.
  • Providing training to users.
  • The support, maintenance, and continuous evolution of the system (if required).
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Invest in Professionals at XpertsApp for Your Big Data Project

As a provider of Big Data Analytics Services, As one of the leading Data analytics services companies, XpertsApp is well-positioned to help your business increase efficiency and make better decisions using big data.

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