Delta Lake vs Kylo: What are the differences?

1. Data ingestion and processing: Delta Lake is specially designed for managing big data while Kylo is focused on providing a data lake accelerator platform for data processing. Delta Lake provides ACID transactions, scalable metadata handling, and easy data versioning capabilities, making it suitable for large-scale data processing tasks. On the other hand, Kylo simplifies data ingestion processes by providing an intuitive user interface for users to rapidly ingest and process large amounts of data within a data lake environment.

2. Data transformation capabilities: Delta Lake enhances data transformation processes by integrating with Apache Spark and providing strong consistency through ACID transactions, making it ideal for real-time analytics and data warehousing applications. In contrast, Kylo simplifies data transformation tasks by offering a drag-and-drop interface for building data pipelines without the need for coding, targeting users who prefer a low-code or no-code solution for their data processing needs.

3. Governance and metadata management: Delta Lake includes built-in support for managing metadata and simplifies governance processes by ensuring data quality and reliability, making it easier for users to track and audit data lineage. On the other hand, Kylo focuses on providing data cataloging and data lineage capabilities, enabling users to understand the flow of data within their organization and maintain data governance standards effectively.

4. Workflow automation: Delta Lake lacks native workflow automation features but can be integrated with workflow management tools for orchestrating data pipelines and workflows. In contrast, Kylo offers a built-in workflow automation module that allows users to schedule and monitor data pipelines, providing a more streamlined approach to managing and executing data processing tasks.

5. Security and access control: Delta Lake provides advanced security features such as role-based access control, encryption at rest, and integration with Apache Ranger for fine-grained access control, ensuring data security and compliance. On the other hand, Kylo focuses on role-based access control and data lineage security, providing users with the necessary tools to secure and monitor data access within the platform.

6. Ecosystem integration: Delta Lake is tightly integrated with the Apache Spark ecosystem, allowing users to leverage the power of Spark for data processing and analytics tasks seamlessly. In contrast, Kylo integrates with various data lake technologies such as Apache NiFi and Apache Kafka, providing users with a flexible platform for ingesting, processing, and analyzing data from different sources within the data lake environment.

In Summary, Delta Lake and Kylo differ in their focus on data processing capabilities, governance features, workflow automation, security mechanisms, and ecosystem integrations, catering to different user requirements in the realm of big data management and analytics.