Here is a detailed summary of the video transcription in markdown format, broken down into sections:
Data and Analytics Platforms
- Data has become a key commodity with various formats and personas (external users, internal users, data scientists, developers, etc.)
- Need to support real-time decision-making and processing of massive data volumes
- Platform engineering has emerged to address the needs of both developers (autonomy, open-source tools) and platform teams (security, scalability, cost, performance)
- New data-intensive workloads (notebooks, data lakes, data meshes, streaming, ML/AI) pose new challenges for platform engineering
Optimizing Analytics Platforms on Kubernetes
Layer 1: Building a Production-Ready Kubernetes Cluster
- Use non-routable IP ranges for network scaling
- Configure VPC-CNI for efficient IP management
- Optimize CoreDNS performance and resolution
- Leverage managed scaling for CoreDNS
- Monitor the Kubernetes control plane, API throttling, and network health
Layer 2: Installing Open-Source Tools
- Use the Spark Operator for running Apache Spark
- Integrate Apache Unicorn for priority-based job scheduling
- Leverage workflow engines like Apache Airflow or Argo Workflows
Layer 3: Onboarding Tenants
- Provide a self-service API for tenants to manage resources (IAM, S3, etc.)
- Use projects like AWS Controllers for Kubernetes (ACK) to extend the Kubernetes API
Customer Case Study: Appsflyer
Challenges
- Massive data volumes (100+ PB daily)
- Highly dynamic and distributed compute resources
- Strict SLAs for data processing
Solutions
- Migrated from EC2 to EKS with Carpenter for efficient scaling and cost optimization
- Leveraged Graviton instances and local storage for performance
- Enriched observability by combining metrics from Carpenter, Kubernetes, and Spark
- Empowered data engineers with self-service APIs and automation
Results
- 60% cost reduction
- 35% improvement in SLA
- Reduced operational overhead for platform engineers
Key Takeaways
- Optimize and monitor EKS for analytics workloads using best practices
- Align tools and practices to foster organizational growth
- Enable self-service APIs to empower data engineers and scientists