Hadoop Implementation
Plan, Costs, Tools, and Best Practices
In big data services since 2013, ScienceSoft designs, develops, and supports secure and scalable Hadoop-based apps that drive high ROI and successfully handle quickly growing volumes of data.
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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.
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Key big data implementation steps: feasibility study, requirements engineering, software conceptualization and planning, architecture design, Hadoop implementation and testing, deployment, support and maintenance. Team: project manager, business analyst, big data architect, Hadoop developers, data engineer, data scientist, data analyst, DataOps engineer, DevOps engineer, QA engineer, test engineers. Costs: from $50K to $2+M, depending on the project scope. |
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With extensive hands-on experience in big data, ScienceSoft designs and implements secure and scalable Hadoop-based solutions for 30+ industries, including BFSI, healthcare, retail, manufacturing, education, telecoms, and more.
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:
Step 1.
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.
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Step 2.
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.
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Step 3.
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.
To help our customers choose the most cost-efficient deployment model, ScienceSoft focuses on the key priorities of the project.
We usually recommend deploying Hadoop in the cloud if application elasticity is needed and the requirements for the computing resources are likely to change in the future (e.g., you may need more storage or processing power). That’s the case for the majority of Hadoop-based apps, so cloud deployment is our go-to option.
Going for on-premises deployment is feasible if harsh security requirements are to be met, the project scope is unlikely to change, and the customer is ready to invest in hardware, office space, DevOps team ramp-up, etc.
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Step 4.
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.
In real-life Hadoop-based apps, Hadoop techs are most often combined with other big data frameworks (e.g., Apache Spark, Storm, Kafka, Flink) to achieve the desired functionality.
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Step 5.
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.
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Step 6.
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.
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Step 7.
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.
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Consider Professional Hadoop Implementation Services
Relying on 34 years of experience in IT and 10 years in big data services, ScienceSoft can design, develop, and support a state-of-the-art Hadoop-based solution or assist at any stage of Hadoop implementation.
Hadoop implementation consulting
Rely on ScienceSoft’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
ScienceSoft’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.
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
Why Choose ScienceSoft for Hadoop Implementation
- 34 years in data analytics and data science.
- 10 years in end-to-end Hadoop implementation.
- Working experience with 30+ industries, including BFSI, healthcare, retail, manufacturing, education, telecoms, and more.
- 700+ experts on board, including big data architects, Hadoop developers, DataOps engineers, and more.
- A Microsoft partner since 2008.
- An AWS Select Tier Services Partner.
- Established Agile and DevOps practices.
- ISO 9001 and ISO 27001-certified to ensure mature 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.
Hadoop Implementation by ScienceSoft: Success Stories
Hadoop Lab Deployment and Support
- Deployment of an on-premises Hadoop lab for one of the largest US colleges.
- A large-scale solution composed of HDFS, YARN, Hive, Spark, Oozie, and more.
- Detailed user guides for the solution, including step-by-step self-service instructions, and a number of remote assistance sessions.
Big Data Implementation for Advertising Channel Analysis
- Development of a new analytical system that manages the ever-growing amount of data and enables advertising channel analytics in 10+ countries.
- Implementation of Apache Spark for up to 100 times faster processing of queries.
- Processing over 1,000 different types of raw data (archives, TXT, XLS, etc.).
- Enabling cross analysis of nearly 30,000 attributes and multi-angled data analytics for different markets.
Collaboration Software MVP Development for a Construction Company
- Delivery of a Delta Lake-based MVP ready to enable ML capabilities.
- Design of a highly scalable architecture for the solution to manage the ever-growing amount of big data.
- Implementation of a secure multi-layered data storage mechanism that enables tracking the record of data added, modified, and deleted in all file versions.
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.
Data analyst
- Closely collaborates with a data engineer on the data quality management strategy.
- Configures the analytics and reporting tools.
DataOps engineer
- Implements DevOps practices to the big data pipelines and workflows to provide faster access to data processing results and boost the quality of data analytics.
DevOps engineer
- Configures the development infrastructure.
- Introduces CI/CD pipelines to automate the development and release.
- Moves the solution into the production environment, sets up security controls.
- Monitors Hadoop-based app performance, security, availability etc.
QA engineer
- Works out and implements a QA strategy for Hadoop implementation and high-level testing plans for the solution components.
Test engineer
- Runs manual and automated tests to comprehensively test the Hadoop-based app.
- Reports on the detected issues and validates the remediated defects.
ScienceSoft is always ready to involve additional talents, such as front-end developers, UI and UX designers, penetration testing engineers, etc., to meet your specific project needs.
Sourcing Models for Hadoop Implementation
Technologies ScienceSoft Uses to Develop Big Data Solutions
Hadoop Implementation Costs
Core cost factors
The cost of Hadoop implementation varies greatly from case to case. Based on ScienceSoft’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.).
- The big data analytics tools (machine learning, OLAP cubes, self-service BI) to implement in the solution.
- The scope of automated and manual testing.
- The team composition and its members’ seniority level, the chosen sourcing model.
Sample cost ranges
$50,000–$100,000
For a solution with simple data ingestion and analytics functionality.
$100,000–$500,000
For a solution that enables data ingestion from multiple sources, data cleansing, and data analysis for various purposes.
$500,000–$2,000,000
For a high-end solution that allows for fast and efficient processing and analysis of massive datasets of different nature.
About ScienceSoft
ScienceSoft is a global IT consulting and software development company headquartered in McKinney, TX, US. Since 2013, we have been designing, developing, and testing highly efficient and scalable Hadoop-based apps. In our big data projects, we employ robust quality management system and guarantee the security of our customers’ data, as proven by ISO 9001 and ISO 27001 certificates.