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Big Data/ML infrastructure management
Landing Zone
Infrastructure Management
Infrastructure Management
- Multi-Account Environment
- Security and Compliance
- Networking Architecture
- Identity and Access Management (IAM)
- Resource Organization
- Automation and Orchestration
- Operational Best Practices
- Cost Management
- Tagging / Resource Naming Standards
Infrastructure Management
Infrastructure Management
Infrastructure Management
- Building Data Lakes in major Cloud Providers
- Deploying highly available Data Warehousing databases
- Migrating data from on-prem to Cloud
- Modernizing older data services/resources to cutting edge technology resources in Cloud
- Deploying and managing Data Stream services in AWS
Focus Areas
Focus Areas
Focus Areas
- Volume - automating the process of creating hundreds of S3 buckets for Data Lake
- Velocity - working with services such as Kinesis Streams & Kinesis Data Analytics to deal with real-time or near-real-time data processing
- Veracity - setting up Glue service to help with cleaning and enriching data to improve its quality before storing it in a
- Volume - automating the process of creating hundreds of S3 buckets for Data Lake
- Velocity - working with services such as Kinesis Streams & Kinesis Data Analytics to deal with real-time or near-real-time data processing
- Veracity - setting up Glue service to help with cleaning and enriching data to improve its quality before storing it in a data warehouse or data lake.
- Value - deploying Athena, Elasticsearch & Quicksight to to extract valuable insights and make informed decisions
- Variability - using Kinesis Streams OR Glue with pre-processing done via Lambda to fix the issue of inconsistency that can exist in the data flow
- Complexity - using Athena, Quicksight, Elasticsearch to analyze such complex data structures that require advanced analytical techniques
- Distributed Computing - automating the deployment of EMR clusters and Glue service
- Machine Learning and Analytics - deploying services like Sagemaker and Athena for advanced ML workloads
- Privacy and Security - using AWS Macie (ML) to automatically discover, classify, and protect sensitive data such as personally identifiable information (PII) and intellectual property.
- Scalability - as simple as ensuring Redshift has Concurrency Scaling feature
Our Mission
Focus Areas
Focus Areas
Our mission is to empower organizations in unlocking the full potential of their data by delivering cutting-edge cloud-based solutions for the efficient and secure management of Big Data infrastructure resources. We are committed to providing scalable, reliable, and innovative solutions that enable businesses to harness the power of data
Our mission is to empower organizations in unlocking the full potential of their data by delivering cutting-edge cloud-based solutions for the efficient and secure management of Big Data infrastructure resources. We are committed to providing scalable, reliable, and innovative solutions that enable businesses to harness the power of data analytics, drive informed decision-making, and stay at the forefront of technological advancements. Our passion lies in simplifying the complexities of Big Data infrastructure, ensuring seamless integration, and delivering exceptional value to our clients as they navigate the dynamic landscape of data-driven insights in the cloud.