Overview
The article introduces OpenHouse, a control plane developed at LinkedIn for managing tables in open source data lakehouse deployments. It emphasizes the need for a unified system to simplify table management and enhance the developer experience by reducing complexity and operational overhead.
What You'll Learn
1
How to create and manage tables using OpenHouse's API
2
Why a unified control plane is essential for effective data management
3
When to implement data governance policies in table management
Prerequisites & Requirements
- Understanding of data lakehouse architectures and metadata management
- Familiarity with SQL and data management APIs(optional)
Key Questions Answered
What are the guiding principles for building OpenHouse?
OpenHouse was built on four guiding principles: using tables as the only API abstraction, storing tables in a controlled namespace, enforcing governance based on company standards, and regular maintenance of tables for optimal performance. These principles ensure self-service capabilities for users while maintaining data integrity and compliance.
How does OpenHouse improve table management for data engineers?
OpenHouse allows data engineers to self-serve the creation of managed tables, reducing the onboarding time from weeks to seconds. It simplifies table management by providing a unified control plane that integrates various components of data management, thereby enhancing collaboration and compliance.
What impact has OpenHouse had on LinkedIn's data management?
OpenHouse has transformed LinkedIn's data management by enabling 65% of tables to be centrally managed, improving sharing capabilities, and reducing operational complexities. This shift allows for more efficient collaboration and adherence to compliance requirements, significantly enhancing the developer experience.
Key Statistics & Figures
Percentage of self-managed tables at LinkedIn
65%
This statistic highlights the need for a more streamlined approach to table management, as a significant portion of tables lack consistent management practices.
Time taken to onboard tables traditionally
2 to 3 weeks
This timeframe illustrates the operational complexities faced by Site Reliability Engineers (SREs
Technologies & Tools
Some links below are affiliate links. We may earn a commission if you make a purchase.
Database
Apache Iceberg
Used as the table format for managing data in OpenHouse, allowing for features like ACID transactions and time travel queries.
Storage
Hdfs
Serves as the distributed storage system for OpenHouse tables.
Orchestration
Kubernetes
Used to deploy OpenHouse's table service and data services.
Key Actionable Insights
1Implement OpenHouse to streamline table management processes in your data lakehouse.By adopting OpenHouse, organizations can reduce the complexity of managing tables, allowing data engineers to focus on core tasks rather than juggling multiple systems.
2Utilize the declarative APIs provided by OpenHouse for table creation and management.This approach not only simplifies the process but also ensures that tables are compliant with governance standards, enhancing data integrity.
Common Pitfalls
1
Failing to enforce governance standards can lead to data inconsistencies and compliance issues.
Without a unified control plane like OpenHouse, organizations may struggle to maintain data integrity across various systems, resulting in operational challenges.
Related Concepts
Data Lakehouse Architecture
Metadata Management
Data Governance
Open Source Data Management Solutions