Big Data Implementation
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What We Do
Evolve Your Business While Evolving Your Data Landscape as you Hire Big Data Consultants
The increasing volume of data & its complexity can be simplified with Tableau – a BI and visual data analytics suite, to help Businesses & people make informed decisions. Big Data with us for data management, insightful analysis, & effective visual presentation that are fast, convenient, & result-oriented to establish a Data Culture & spur change. With our Tableau developers, turn your data insights into value-based actions.
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Big Data Implementation Steps
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. Go through them in the guide below or download the roadmap in .pdf to consult later.
Feasibility study
Requirements engineering and big data solution planning
Architecture design
- Creating the data models that represent all data objects to be stored in big data 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.
Big data solution development and testing
- Setting up the environments for development and delivery automation (CI/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 devstudio360.
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.
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.
Typical Roles on ScienceSoft’s Big Data Teams
With 750+ professionals on board, ScienceSoft has experts to cover any project scope. Below, we describe key roles in our big data projects teams that can also be augmented with our front-end developers, UX and UI designers, BI engineers, and other professionals.
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.
Implement a Big Data Solution with Professionals
With 35 years in IT and 11 years in big data services, devstudio360 can design, build, and support a state-of-the-art big data solution or provide assistance at any stage of big data implementation. With established practices for scoping, cost estimation, risk mitigation, and other project management aspects, we drive projects success regardless of time and budget constraints, as well as changing requirements.
Consulting on big data implementation
Get expert guidance at any step of your big data initiative. ScienceSoft is ready to provide you with a wide scope of deliverables, including a business case, a detailed project roadmap, an architecture design, and a PoC to help you eliminate doubt and avoid unnecessary expenses.
Big data solution development
We’re ready to deliver big data software of any complexity to let you benefit from a solution that is tailored to your specific needs and runs smoothly at any data and user loads. If needed, you can also count on us to stay with you for continuous maintenance and support.
