•Sumanth Srinivasa Krishnaswamy, Matt Mathew, Sonali Goyal•13 min read•intermediate•
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•View OriginalOverview
The article discusses the evolution of Data Lifecycle Management (DLM) at Uber, detailing the journey from initial implementations to the development of a unified system. It highlights the importance of DLM in achieving compliance, cost efficiency, and data reliability while managing diverse data operations across various datastores.
What You'll Learn
1
How to implement Data Lifecycle Management policies across various datastores
2
Why a unified DLM system is essential for compliance and cost efficiency
3
When to apply lifecycle operations based on data characteristics
Prerequisites & Requirements
- Understanding of data lifecycle operations and compliance requirements
- Familiarity with data management practices(optional)
Key Questions Answered
What are the key goals of Data Lifecycle Management at Uber?
The key goals of Data Lifecycle Management at Uber are compliance with regulations like GDPR and HIPAA, achieving cost efficiency through timely data deletion, and ensuring data reliability through effective backup and recovery processes. These goals are critical for managing vast amounts of data across various datastores.
How does Uber's DLM system handle diverse data operations?
Uber's DLM system manages diverse data operations by categorizing data based on its age, type, and access frequency. This allows for tailored lifecycle operations such as moving data to cold storage or deleting outdated information, ensuring efficient data management across multiple platforms.
What challenges did Uber face in implementing DLM?
Uber faced challenges such as siloed execution of data lifecycle operations, varying requirements across teams, and the need for a holistic approach to manage diverse datasets effectively. These challenges necessitated the development of a unified DLM system to streamline operations and ensure compliance.
Key Statistics & Figures
Data stored in Uber's Hadoop Data Lake
over 1EB
This highlights the scale at which Uber operates, necessitating robust data management practices.
Number of Hive tables at Uber
500K+
This indicates the complexity and volume of data that the DLM system must manage effectively.
Technologies & Tools
Data Storage
Hadoop
Used for storing large datasets in Uber's data lake.
Workflow Orchestration
Cadence
Used for executing asynchronous long-running business logic in a scalable manner.
Database
Docstore
An internal distributed SQL database used for managing metadata.
Key Actionable Insights
1Implement a unified DLM system to streamline data management processes across various datastores.A unified system helps in centralizing policy management and monitoring, which is crucial for compliance and operational efficiency.
2Regularly review and update data lifecycle policies to adapt to evolving compliance requirements.As regulations change, it's important to ensure that data management practices remain compliant, thus avoiding potential legal issues.
3Utilize automation for data lifecycle operations to reduce manual errors and improve efficiency.Automating processes such as data deletion and tiering can save time and resources while ensuring accuracy in data management.
Common Pitfalls
1
Failing to centralize policy management can lead to inconsistent data handling practices across teams.
Without a unified approach, different teams may implement varying policies, resulting in compliance risks and operational inefficiencies.
Related Concepts
Data Lifecycle Management
Compliance Regulations Like Gdpr And Hipaa
Data Storage Solutions Such As Hdfs And Mysql