Avaliação dos dados
    
      We start by analyzing your entire ETL ecosystem, encompassing transformations, tasks, dependencies, and runtime behavior, to uncover weak points and blockers. You’ll get a clear, migration-ready blueprint with no unexpected surprises.
       
    
   
  
    ETL workflow design
    
      Our engineers build new workflows from scratch or re-architect existing ones using modular design, batch control, and real-time triggers. We design everything for reusability, clarity, and easy maintenance post-migration.
       
    
   
  
    Planeamento da migração
    
      Innowise scopes each migration phase in detail, from source inventory and data cutover to testing checkpoints and rollback procedures. You’ll get a practical, risk-mitigated plan that can be easily followed with zero guesswork.
       
    
   
  
    Extração de dados
    
      To extract structured, semi-structured, and unstructured data from legacy sources, Innowise develops custom connectors and scripts. We handle outdated formats, missing metadata, and inconsistent encodings with special care.
       
    
   
  
    Transformação de dados
    
      Our team rewrites old transformation logic using modern tools and scalable 
      arquitetura de dados, 
      removing deadweight and improving performance. For better traceability, Innowise documents everything to the letter.
       
    
   
  
    Mapeamento de dados
    
      Innowise creates detailed mapping specifications that track every field, type, and relationship from source to destination. Business rules, format conversions, and exception handling are embedded directly into the mapping layer.
       
    
   
  
    ETL performance optimization
    
      We identify long-running processes, high-memory usage, and bottlenecks, and then restructure ETL flows for faster execution required for advanced analytics and 
      grandes dados. That includes load balancing and parallelization for peak performance.
       
    
   
  
    Dataflow monitoring
    
      Innowise integrates monitoring with real-time logs, metrics, and automated alerts. We continuously review usage patterns and refine workflows to eliminate failures and optimize processing logic.
       
    
   
  
    Data quality management implementation
    
      To ensure quality, we run full quality assessments across datasets and ETL steps to catch duplicates, mismatches, and logic errors. Then, Innowise implements automated validations, cleansing routines, and correction workflows.
       
    
   
  
    Apoio pós-migração
    
      After ETL migration, we stay involved to fix logic edge cases, optimize schedules, and validate performance under real workloads. We also share documentation and guides for your internal teams, so they can perform maintenance tasks.
      