How to Build a Cloud Native Data Lake with Open Source Technologies

Overview

Discover how to deploy a Kubernetes-based data lake using open source tools, from initial setup to running a complete prototype platform on your local machine.

Syllabus

• Introduction to Cloud Native Data Lakes
Overview of cloud native architectures
Benefits of data lakes for data storage and analytics
• Fundamentals of Kubernetes
Understanding container orchestration
Setting up a local Kubernetes cluster (Minikube, kind, or K3s)
Kubernetes basic operations: Pods, Services, and Deployments
• Open Source Technologies for Data Lakes
Apache Hadoop and HDFS
Apache Spark for data processing
Apache Kafka for real-time data ingestion
• Storage Layer
Setting up distributed file systems
Configuring object storage solutions (e.g., MinIO, Ceph)
• Data Ingestion
Configuring data ingestion pipelines with Kafka
Exploring ETL tools like Apache NiFi and Apache Airflow
• Data Processing
Running Spark jobs on Kubernetes
Implementing batch and stream processing
• Data Access & Querying
Setting up SQL query engines (e.g., Presto, Trino)
Using Hive Metastore for schema management
• Security and Governance
Implementing basic security practices
Introduction to data governance tools (Apache Atlas)
• Monitoring and Logging
Configuring monitoring tools (Prometheus, Grafana)
Log aggregation and monitoring with ELK stack (Elasticsearch, Logstash, Kibana)
• Deployment and Testing
Building a data lake prototype on a local machine
Perform testing and data validation
• Case Study and Hands-on Projects
Real-world data lake architecture case studies
Capstone project: Deploy a cloud native data lake using open source tools on Kubernetes
• Conclusion and Future Trends
Emerging trends in cloud native data technologies
Examining the future of open source data lakes

Subjects

Business