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BigData & Analytics Team

Transforming massive datasets into actionable business insights

Expertise

  • •Data Pipeline Development
  • •Big Data Processing & ETL
  • •Business Intelligence & Reporting
  • •Data Warehousing
  • •Real-time Analytics
  • •Data Visualization & Dashboards

Technologies

Apache SparkHadoopKafkaTableauPower BIApache Airflow

Our Process

1

Requirements & Data Source Identification

Understanding analytics needs and data sources

  • →Gather business intelligence requirements
  • →Identify data sources and APIs
  • →Assess data volume, variety, and velocity
  • →Define key performance indicators (KPIs)
  • →Establish data governance policies
2

Data Pipeline Design

Architecting scalable data processing pipelines

  • →Design ETL/ELT pipeline architecture
  • →Choose appropriate big data technologies
  • →Plan data storage and warehouse structure
  • →Define data quality checks and validation rules
  • →Establish data refresh schedules
3

Data Ingestion & Processing

Building data pipelines for collection and transformation

  • →Implement data connectors and ingestion scripts
  • →Build data transformation logic with Spark or SQL
  • →Set up Apache Airflow for workflow orchestration
  • →Implement real-time streaming with Kafka if needed
  • →Handle data partitioning and optimization
4

Data Warehousing

Creating structured data warehouse for analytics

  • →Design star or snowflake schema
  • →Implement fact and dimension tables
  • →Load transformed data into data warehouse
  • →Create aggregated tables for performance
  • →Set up incremental data loading
5

Analytics & Visualization

Creating insights and interactive dashboards

  • →Develop SQL queries for business metrics
  • →Create interactive dashboards in Tableau or Power BI
  • →Build custom reports and visualizations
  • →Implement drill-down and filtering capabilities
  • →Share dashboards with stakeholders
6

Monitoring & Optimization

Ensuring pipeline reliability and performance

  • →Monitor pipeline execution and data quality
  • →Set up alerts for pipeline failures
  • →Optimize query performance and data models
  • →Scale infrastructure based on data growth
  • →Document data lineage and metadata
Checklist Progress
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Code Quality

At least two team members have reviewed and approved the code changes

Code follows team coding standards, style guide, and best practices

ESLint/Prettier passes with zero errors and warnings

Complex logic is well-documented with clear comments and JSDoc

All console.log statements and debug code removed from production

Testing

Minimum 80% code coverage with meaningful unit tests

All integration tests pass successfully in CI/CD pipeline

Feature tested manually across different scenarios and edge cases

Verified functionality in Chrome, Firefox, Safari, and Edge

Tested on mobile devices (iOS/Android) and tablets

Existing features still work correctly after changes

Security

All user inputs are validated and sanitized to prevent injection attacks

Proper authentication and authorization checks implemented

No API keys, passwords, or sensitive data exposed in code

All API calls use HTTPS and secure communication protocols

No critical or high-severity vulnerabilities in dependencies

Proper CORS and Content Security Policy configured

Performance

Page load time, API response time meet performance targets

Images optimized and compressed, using appropriate formats (WebP, AVIF)

Large components and routes are code-split and lazy-loaded

Database queries optimized with proper indexes and efficient joins

Appropriate caching (Redis, CDN) for static and dynamic content

JavaScript bundle size within acceptable limits (< 200KB gzipped)

Accessibility

Meets WCAG 2.1 Level AA accessibility standards

All interactive elements accessible via keyboard navigation

Tested with screen readers (NVDA, JAWS, VoiceOver)

Text and interactive elements meet minimum contrast ratios (4.5:1)

Proper ARIA labels and semantic HTML elements used

Clear focus indicators for all interactive elements

Documentation

README.md includes setup instructions, dependencies, and usage

API endpoints documented with request/response examples

CHANGELOG.md updated with new features, fixes, and breaking changes

All required environment variables documented in .env.example

Deployment procedures documented for production release

Database & Data

Database migration scripts created and tested

Database backup completed before deployment

Rollback procedure documented and tested

Data validation and integrity checks implemented

Deployment

All automated tests passing in CI/CD pipeline

Feature deployed and tested in staging environment

All production environment variables configured correctly

Error tracking and performance monitoring set up

Release notes prepared for stakeholder communication

Plan for verifying production deployment is successful