Explore the landscape of Open Source Data Engineering

1. Storage Systems:

• From relational OLTP databases like PostgreSQL and MySQL to distributed SQL DBMS like CockroachDB and TiDB, find the right storage solution for your needs
• Includes NoSQL options like MongoDB and Redis for diverse data requirements.

2. Data Integration:

• Tools for CDC, log and event collection, and data integration platforms such as Kafka Connect, CloudQuery, and Airbyte ensure seamless data flow and event management.

3. Data Infrastructure & Monitoring:

• Manage and monitor your data infrastructure with tools for resource scheduling like Kubernetes and Docker, security solutions like Apache Knox, and observability frameworks like Prometheus and ELK.

4. Data Processing & Computation:

• Optimize your data processing with unified processing platforms like Apache Beam and Spark, batch processing with Hadoop, and stream processing with Flink and Samza.

5. ML/AI Platform:

• Empower your machine learning and AI initiatives with vector storage solutions like Milvus, MLOps platforms like MLflow and Kubeflow, and other AI tools for enhanced data insights.

6. Data Lake Platform:

• Efficiently manage large-scale data with distributed file systems like Hadoop HDFS, open table formats like Iceberg, and serialization frameworks like Parquet.

7. Workflow & Data Ops:

• Streamline your data operations with workflow orchestration tools like Apache Airflow, data quality solutions like Great Expectations, and data warehousing with LakeFS.

8. Metadata Management:

• Organize and manage your metadata with platforms like Amundsen and Apache Atlas, and ensure data security with tools like Hive and Schema-registry.

9. Analytics & Visualization:

• Enhance your data analysis and visualization with BI tools like Superset, query and collaboration tools like Hue, and semantic layers like Cube, AtScale, etc.