Overview
The article discusses the open sourcing of OpenHouse, a control plane designed for managing tables in a data lakehouse. It highlights its features, implementation at LinkedIn, and the benefits it brings to data management and governance.
What You'll Learn
1
How to utilize OpenHouse for managing tables in a data lakehouse
2
Why implementing retention policies can optimize data management
3
How to leverage OpenHouse's pluggability for custom implementations
Key Questions Answered
What is OpenHouse and how does it improve data management?
OpenHouse is a control plane that provides a managed experience for users in data lakehouse environments. It simplifies table management by automating tasks like data retention and governance, allowing data infra teams to focus on higher-level concerns while improving user experience.
What key features does OpenHouse offer for table management?
OpenHouse includes features such as a RESTful Catalog for table operations, retention management for automatic data cleanup, sharing capabilities, and governance through column tagging. These features enhance the efficiency and governance of data management processes.
How does OpenHouse handle data governance?
OpenHouse enables users to assign tags to columns for compliance and governance purposes. It also incorporates instrumentation to audit events related to table operations, ensuring that data governance is maintained throughout the data lifecycle.
What are the benefits of using OpenHouse for data sharing?
OpenHouse simplifies data sharing by allowing users to set sharing policies on tables and manage permissions through SQL commands. This reduces the complexity associated with data sharing and enhances collaboration across teams.
Key Statistics & Figures
Managed OpenHouse tables in production
3,500
This number reflects the scale at which OpenHouse is currently utilized at LinkedIn.
Daily active users of OpenHouse
550
This indicates the level of engagement and reliance on OpenHouse within LinkedIn's data management processes.
Reduction in time-to-market for dbt implementation
over 6 months
This statistic highlights the efficiency gains achieved through the use of OpenHouse.
Reduction in end-user toil associated with data sharing
50%
This demonstrates the effectiveness of OpenHouse in streamlining data sharing processes.
Datasets onboarded to OpenHouse from AI use cases
1,000
This showcases the versatility of OpenHouse in supporting various data applications, including AI.
Technologies & Tools
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Control Plane
Openhouse
Used for managing tables in a data lakehouse environment.
Data Processing
Apache Spark
Integrated with OpenHouse for executing standard SQL operations on tables.
Table Format
Apache Iceberg
Used for organizing data on distributed storage with versioning support.
Data Replication
Apache Gobblin
Extended by OpenHouse to provide cross-geography replication functionality.
Key Actionable Insights
1Implementing OpenHouse can significantly reduce operational toil for data infrastructure teams.By automating table management tasks, OpenHouse allows teams to focus on strategic initiatives rather than routine maintenance, leading to improved efficiency.
2Utilizing retention policies in OpenHouse can help manage storage costs effectively.By automatically deleting outdated data, organizations can optimize their storage usage and reduce costs associated with data retention.
3Leveraging OpenHouse's pluggability can enhance integration with existing systems.Custom implementations for storage and authentication can be developed, allowing organizations to tailor OpenHouse to their specific infrastructure needs.
Common Pitfalls
1
Failing to implement retention policies can lead to excessive storage costs and data clutter.
Without automated retention management, organizations may struggle with managing large volumes of outdated data, resulting in inefficiencies and increased costs.
2
Neglecting to utilize OpenHouse's pluggability may limit integration capabilities.
Organizations that do not customize OpenHouse to fit their existing infrastructure may miss out on potential efficiency gains and streamlined operations.
Related Concepts
Data Governance
Data Lakehouse Architecture
Big Data Management