Explore itemized architecture packages ranging from basic server deployments to advanced Kafka data streaming and enterprise Snowflake warehousing.
Basic Linux server setup, SSH access, users and essential configuration.
Deploy an existing application from GitHub or Git to a Linux server.
Connect domain, configure reverse proxy and enable HTTPS.
Launch and configure an AWS EC2 environment for application deployment.
Containerize and deploy an application using Docker.
Configure a production-ready SQL or NoSQL database environment.
Configure Redis for caching, sessions and high-performance applications.
Automate application build, testing and deployment workflows.
Configure production cloud infrastructure for applications and APIs.
Create reusable and automated cloud infrastructure using Terraform.
Set up monitoring, logs, alerts and basic observability.
Improve server security, IAM, network controls and cloud configuration.
Deploy and configure containerized applications on Kubernetes.
Set up managed Kubernetes infrastructure on major cloud platforms.
Migrate applications, databases and infrastructure to AWS, Azure or GCP.
Identify application, database and infrastructure bottlenecks.
Design resilient and highly available cloud infrastructure.
Design secure, scalable and production-ready enterprise cloud architecture.
Technical consultation for data platforms, pipelines and architecture.
Move data from APIs, databases and files into your data platform.
Build automated pipelines to extract, transform and load data.
Configure Snowflake warehouse, databases, schemas and access.
Automate and orchestrate scheduled data workflows.
Build event-driven and real-time data streaming pipelines.
Build modular, tested and maintainable SQL transformation workflows.
Build scheduled pipelines for processing large datasets.
Stream database changes into modern data platforms.
Process and transform large-scale datasets using distributed computing.
Develop, optimize and improve Snowflake data warehouse workloads.
Build scalable Lakehouse and data processing workloads.
Build low-latency streaming pipelines for real-time analytics.
Design scalable cloud data lake storage and processing architecture.
Design modern Lakehouse architecture for analytics and AI workloads.
Migrate databases, pipelines and analytics workloads to modern cloud platforms.
Improve data validation, testing, reliability and governance.
Build production data pipelines for AI and machine learning workloads.
Design scalable enterprise data platforms, pipelines and analytics architecture.