Hadoop Implementation
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What We Do
Hadoop Implementation In a Nutshell
Hadoop is an open-source framework that enables distributed big data storage, processing, and analytics across multiple cluster nodes. That is why Hadoop implementation is a crucial first step to building powerful big data solutions capable of processing massive datasets and driving advanced analytics. Adopted by such global market giants as Facebook, eBay, Uber, Netflix, and LinkedIn, Hadoop-based apps help handle petabytes of data from various sources and derive strategically vital insights from it.
How to implement Hadoop in 7 steps
- Analyze data handling issues to be solved with Hadoop.
- Define data processing requirements and data quality thresholds.
- Estimate the size and structure of Hadoop clusters.
- Design an architecture to enable distributed data storage, resource management, data processing and presentation.
- Implement the app in parallel with QA processes.
- Launch the app and start user training.
- Ensure continuous solution evolution in line with the changing business needs.
7 Steps to Hadoop Implementation
Hadoop may be used as a base for a large variety of components (e.g., Hive, HBase, Spark, etc.) to meet different purposes. So, its implementation roadmap naturally alters depending on the solution requirements. Still, based on ScienceSoft’s experience, there are six high-level steps that are common for most Hadoop projects:
Feasibility study
- Analyzing your business needs and goals, outlining the current data handling issues (e.g., low system performance due to increased volume of heterogeneous data, data quality management challenges).
- Evaluating the viability of implementing a Hadoop-based app, calculating the approximate ROI and future operational costs for the solution-to-be.
Requirements engineering
- Eliciting functional and non-functional requirements for the Hadoop solution, including the relevant compliance requirements (e.g., HIPAA, PCI DSS, GDPR).
- Identifying the required data sources with regard to the data type, volume, structure, etc. Deciding on target data quality thresholds (e.g., data consistency, completeness, accuracy, auditability).
- Deciding on the data processing approach (batch, real-time, both).
- Defining the needed integrations with the existing apps and IT infrastructure components.
Solution conceptualization and planning
- Defining the key logical components of the future app (e.g., a data lake, batch and/or real-time processing, a data warehouse, analytics and reporting modules).
- Estimating the required size and structure of Hadoop clusters, taking into account:
- The volume of data to be ingested by Hadoop.
- The expected data flow growth.
- Replication factor (e.g., for an HDFS cluster it’s 3 by default).
- Compression rate (if applicable).
- The space reserved for OS activities.
- Choosing the deployment model (on-premises, cloud, hybrid).
- Selecting the best suited technology stack.
- Preparing a detailed project plan, including the project schedule, required skills, budget, etc.
Architecture design
- Creating a high-level scheme of the future solution with the key data objects, their connections, and major data flows.
- Working out the data quality management strategy.
- Planning the data security measures (encryption of data at rest and in motion, data masking, user authentication, fine-grained user access control).
- Designing a scalable solution architecture that contains at least four major layers:
- Distributed data storage layer represented by HDFS (Hadoop Distributed File System). As the name suggests, HDFS divides large incoming files into manageable data blocks and replicates each dataset at least 3 times to store them across several nodes, or computers. This way, data is protected against loss in case of a node failure. Among HDFS’s alternatives offered by cloud providers are Amazon S3 and Azure Blob Storage.
- Resource management layer consisting of YARN that serves as an OS to a Hadoop-based solution. YARN ensures balanced resource loading by scheduling the data processing jobs. If supplemented with Apache Spark or Storm, YARN can help enable stream data processing.
- Data processing layer with MapReduce at its core that splits input data to be processed in parallel as individual units. The processed datasets are then sorted out and aggregated as a final output ready for querying. Nowadays, data processing is often conducted with the help of additional tools, such as Apache Hive, Pig, and other tools depending on the specific solution’s needs.
- Data presentation layer (usually represented by Hive and/or HBase) that provides quick access to the data stored in Hadoop, enabling data querying and further analysis.
Hadoop implementation and testing
- Setting up the environments for development and delivery automation (CI/CD pipelines, container orchestration, etc.).
- Building the Hadoop-based app using the selected techs and implementing the planned data security measures.
- Establishing QA processes in parallel with the development. Conducting comprehensive testing, including functional testing (validating the app’s business logic, continuous data availability, report generation, etc.), performance, security, and compliance testing.
Hadoop-based app deployment
- Running pre-launch user acceptance tests to confirm that the solution performs well in real-world scenarios.
- Launching the application in the production environment, establishing the required security controls (access permissions, logging mechanisms, encryption key management, patching automation, etc.).
- Choosing and configuring the monitoring tools to track the computing resources capacity and usage, performance, connectivity, DataNode health, etc.
- Starting data ingestion from real-life data sources, ensuring that the target data quality thresholds are achieved.
- Conducting user training.
After-launch support and evolution (continuous)
- Setting the support and maintenance procedures to ensure the smooth operation of the solution: addressing user and system issues, optimizing the usage of computing and storage resources, etc.
- Adjusting the solution to the evolving business needs: adding new functional modules and integrations, implementing new security measures, etc.
Software Outsourcing
By leveraging our extensive experience and diverse talent pool, we will help you fast-track your time-to-market and reach your business objectives better. Whether you are looking to scale your development capacity, access niche skill sets or address complex technical issues, our outsourcing software development services offer a cost-effective and flexible answer to your needs.
We guarantee high-quality, transparent products that meet or exceed expectations, ensuring customer satisfaction. We emphasize open communication and collaboration through all stages of outsourcing. With devstudio360 as your IT outsourcing partner, we manage the technical aspects of it which allows you to stay competitive in today’s dynamic market environment.
We use our software development expertise to cut down on costs and fasten time-to-market for your product or service.
Our Solutions
- Skilled development teams
- Flexible engagement models
- Adept QA team
- Project management
- Ongoing maintenance
On-demand Software Teams
Our on-demand software teams are ideal for businesses, facing constantly changing project requirements or resource demands because they can adapt quickly and efficiently. In case you need more developers, designers or project managers; we have a large number of professionals who are highly talented and ready for seamless integration into your existing team. Using agile methodologies when building teams guarantees sufficient personnel as expected by the project schedule within budget parameters.
Our company creates an atmosphere where everyone works together transparently while freely communicating thoughts. Utilization of our on-demand software teams opens up chances to hire different talents whose level of experience is so high. devstudio360 gives you access to such a talent pool hence increasing efficiency by shortening project timelines, reducing risk levels.
At devstudio360, we provide highly skilled professionals to cater for your project’s immediate requirements and swiftly scale as the need arises.
Our Solutions
- Experienced professionals
- Flexible team sizes/numbers
- Collaborative rapid deployment
- Cost-effective solutions are delivered
Legacy Software Modernization
We understand how complex it can be to work with legacy systems which include scalability limits, performance bottlenecks, and antiquated user interfaces. Therefore, we offer a detailed process of modernizing your applications starting from their platforms, frameworks. However, it will seamlessly integrate with your current infrastructure, causing minimal disruption to your operations.
Being guided by experienced developers, designers and consultants who collaborate with you throughout the process will help us gain more insight into your business goals. This results in being highly productive and engaging because they produce intuitive interfaces and streamlined workflows to improve productivity among users. devstudio360 guarantees you tremendous improvements in terms of agility and scalability of use for those outdated software platforms.
We at devstudio360 improve our outdated systems by upgrading them to modern technologies which results in better scalability, performance, and UX/UI.
Our Solutions
- Application Modernization
- Legacy system assessment/Migration planning
- Implementation of modern architecture/Data migration
- Continuous support
- Maintenance and Support
Software Audit
Our services for software audit are meant to help you make an extensive analysis of your software infrastructure. In checking the quality of codes, architecture data integrity as well as system dependencies, we employ a thorough approach to ensure that your software meets industry standards and best practices. Moreover, our experienced auditors will use modern tools and approaches to inspect each component of your program supplying you with practical information leading to enhancing its general quality and efficiency.
Additionally, we do not just identify problems but also deliver custom-made solutions and guidelines on how addressing them can lead to improvement in your software environment. Our audit service empowers implementing security fixes, code refactoring and adjusting system configurations so that risks can be mitigated. With eSparkBiz being the most reliable partner for software audits ever, compliance and security of the product will perform at its peak.
We follow a comprehensive software audit, compliance issues and performance optimization for every software audit service.
Our Solutions
- Code review/analysis
- Security assessment
- Performance evaluation monitoring
- Compliance checks
- Detailed reporting
Consider Professional Hadoop Implementation Services
Relying on 35 years of experience in IT and 11 years in big data services, devstudio360 can design, develop, and support a state-of-the-art Hadoop-based solution or assist at any stage of Hadoop implementation. With established practices for scoping, cost estimation, risk mitigation, and other project management aspects, we drive projects to their goals regardless of time and budget constraints.
Hadoop implementation consulting
Rely on devstudio360’s expert guidance to ensure that your Hadoop implementation is plain sailing. We will assess the feasibility and ROI of your Hadoop-based app, help you choose the best suited architecture and tech stack, draw up a detailed project roadmap, and deliver a PoC for complex solutions.
Hadoop implementation outsourcing
devstudio360’s big data professionals are ready to take charge of the entire Hadoop implementation project for you. We will take a deep dive into your business needs, design a highly efficient Hadoop architecture, develop and deploy the app, and ensure state-of-the-art data security. If you need long-term support and evolution of your Hadoop-based app, we are always here to lend a hand.
Typical Roles in ScienceSoft’s Hadoop Implementation Projects
Project manager
- Outlines the timeframes, budget, key milestones, and KPIs of a Hadoop implementation project.
- Tracks project progress, reports to the stakeholders.
Business analyst
- Investigates the business needs or product vision (for SaaS apps).
- Conducts an in-depth feasibility study of the Hadoop implementation project.
- Elicits the functional and non-functional requirements for the solution to-be.
Big data architect
- Develops several architectural concepts and presents them to the project stakeholders.
- Creates data models and designs the chosen solution architecture.
- Selects the best suited tech stack.
Hadoop developer
- Assists in choosing optimal techs.
- Develops Hadoop modules in line with the solution design, integrates the components with the target systems.
- Fixes the found code defects according to QA team’s notices.
Data engineer
- Participates in creating data models.
- Builds and manages the data pipelines.
- Works out and implements a data quality management strategy.
Data scientist
- Designs and implements ML models (if needed).
- Sets up predictive and prescriptive analytics.
Hadoop Implementation Costs
The cost of Hadoop implementation can vary from $50,000 to $2,000,000. Based on devstudio360’s experience, the following factors are major cost considerations for Hadoop-based apps:
- The type and complexity of business purposes a Hadoop-based app needs to serve (e.g., data storage and warehousing, customer analytics, fraud detection).
- The architecture complexity, the number of app modules.
- The requirements for software availability, performance, scalability, security, and compliance.
- The software deployment model (on-premises, cloud, hybrid).
- The number and variety of data sources, the complexity of data flows.
- The type of data processing (batch, real-time/near real-time, both), the required processing speed.
- The data volume to be collected, stored, and processed by the system.
- The data cleansing specifics, the target data quality thresholds (completeness, consistency, accuracy, etc.).
