Data Architecture Modernization on Microsoft Azure: 4-Month Implementation

Being the preferred digital transformation partner for 30+ F500 companies, Sigmoid is a boutique Data & AI consulting firm specialized in building Big Data & Advanced Analytics solutions on Azure. Our capabilities in modernizing data architectures span across four major areas: Migration, ELT/ETL Modernization, Data Warehouse Modernization and DevOps.

1. Migration - Sigmoid has experince in helping customers migrate from old legacy systems, databases, complex workloads to Azure. Performing various migration methods such as lift-and-shift, re-platforming and re-architecturing, Sigmoid is particularly experienced in migrating SAP workloads to Azure. We have also built migration accelerators, which help in creation of pre-built data models
2. ETL/ELT Modernization - Sigmoid has built a low-code no code framework for ingesting data from multiple sources. Performing orchestration using Azure Data Factory and storing the data in Azure Data Lake Storage, Sigmoid uses Bicep for provisioning IaC
3. Building and Modernizing Data warehouses - Using Azure Synapse Analytics or Azure Databricks for data processing, transformation and analytics, ADF for orchestrating pipelines and ADLS for data storage, Sigmoid has helped modernize warehouses for improving scalability, performance and cost efficiency
4. DevOps - Utlizing Azure DevOps services, Sigmoid ensures seamless development, deployment, and operations, enabling faster delivery and improved efficiency in software lifecycles

Adhering to Azure's Well Architected Framework, our offer broadly covers the following pointers:

1. Understanding the goals and objectives, gathering requirements from stakeholders, defining the scope of the project and identifying the key metrics and success criterias
2. Assessing the current data sources, formats, quality of data. Impelementing ETL/ELT pipelines for automated data ingestion and deployment of necessary resources via IaC
3. Defining data models and schemas
4. Formulating data governance frameworks and implementing data management processes to ensure data quality with cataloguing and lineaging
5. Conducting battle testing of data pipelines, ETL/ELT processes, and data integrity. Validating data accuracy, completeness, and consistency
6. Deploying the solution into production environment
7. Monitoring system performance, cloud cost, and site reliability
8. Documenting the data engineering processes, workflows, and system architecture
9. Scaling and adoption - Helping stakeholders, data domain owners, consumers and business users with adoption of the new platform

Specializing in data strategy consulting, Sigmoid helps organizations build a robust data foundation to allow for scalability for performing analytics on high speed data, that consumes optimum compute resources( lower Total Cost of Ownership) without hampering performance.