Data Sharing Best Practices in Snowflake - Beinex

Data Sharing Best Practices in Snowflake

Imagine a world where sharing data is easy, secure, and efficient. No more struggling with complicated transfers and synchronisation issues. Thanks to Snowflake, organisations can enter a new era of possibilities, using advanced data-sharing features to boost their analytics and insights like never before.

For example, put yourself in the shoes of a data engineer or analyst dealing with complex data integration challenges. How can you streamline your workflow and enhance collaboration with external partners? Snowflake has the answer.

13 Feb 2024

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    Snowflake Data Sharing

    Data sharing in Snowflake equips you to share specific objects with another Snowflake account or a designated reader account. The beauty of this process lies in the fact that the data isn’t duplicated or moved between accounts.

    Now, why is this a game-changer for organisations? When constructing data pipelines and developing data products, it’s a common practice to shuttle data between databases and diverse systems to blend different datasets.

    Consider this scenario: You have transactional data within your online transactional processing (OLTP) database, and you wish to integrate it with external data for a machine learning model. Traditionally, organizations would export data into a data lake, import external data, and then employ tools like Apache Spark for analysis.

    But what if, instead, you could simply deposit your data into Snowflake, and the external data source could seamlessly share its data with your organisation, eliminating the need to load it separately? This eradicates the challenge of keeping data copies synchronised, resulting in savings on storage, computing costs, and maintenance efforts.

    Imagine your company possesses valuable information that can guide other companies in making informed decisions. For instance, let’s say your company can provide precise estimates for product delivery times based on proprietary data, and you want to offer this information for sale to your customers.

    Enter Snowflake data sharing—it empowers you to precisely do that.

    Case Studies: Snowflake Data Sharing

    Citing two instances where leading organisations use Snowflake to improve actionable data sharing, collaboration and reporting capabilities.

    1. A Pioneering Technology Leader
    A well-known Swedish-Swiss multinational corporation successfully implemented a streamlined data strategy using the Snowflake Data Cloud, adopting an “extract once, use everywhere” approach that simplified data consolidation and enablement. By transitioning from nightly extracts, which caused significant system overhead, to a single, near real-time Change Data Capture (CDC) process, the company achieved efficient replication of information to Snowflake with minimal impact. The utilisation of Snowflake Secure Data Sharing facilitated secure and governed data collaboration across the four business areas.

    2. A Leading fast-food Restaurant Chain
    Snowflake’s data-sharing capabilities have revolutionised decision-making for a fast-food restaurant chain. They can effortlessly share crucial sales, inventory, and operational data with external entities, expanding from three to over 30 parties.

    With a high-performance database platform hosting over 2 million transaction records, the restaurant chain has established a robust data management and analysis infrastructure through Snowflake, empowering its operational and marketing endeavours.

    Moreover, by consolidating all data onto Snowflake, the organisation has achieved a remarkable 70% reduction in operational IT costs, demonstrating the platform’s efficiency and cost-effectiveness.

    Centralising and sharing data with Snowflake significantly eased the development of data products for various purposes, including marketing campaign analytics, quotation success metrics, production line tools, and supply chain dashboards. These data products are utilised by thousands of users globally, including internal stakeholders and external vendors, enhancing collaboration and efficiency across the organisation.

    What are the best practices for Snowflake data sharing?

    Optimize your Snowflake data sharing experience with these essential practices. Ensure data security by utilizing secure views to filter and mask sensitive information. Enhance clarity and understanding by employing descriptive names and comments for your shares. Monitor and fine-tune your sharing activities using Snowflake Information Schema or Account Usage views. Foster communication and collaboration with your consumers to create a seamless workflow.

    Take command of your data sharing environment by setting quotas and limits with the ALTER SHARE command. Keep your consumers informed about any changes or updates to your shares, and actively seek feedback to refine your data-sharing strategy. Explore additional data sources through Snowflake Data Exchange or Data Marketplace to enrich your analytics.

    These best practices safeguard sensitive data, ensure compliance with data privacy regulations, clarify the purpose of each share, and provide insights into usage and performance, ultimately enhancing your data analysis capabilities. Below are some best practices for data sharing with Snowflake:

    1. Understand Snowflake Data Sharing
    2. Familiarize yourself with Snowflake’s data sharing features, such as Secure Data Sharing (SDS) and Sharehouse, to leverage the platform effectively.

    3. Role-Based Access Control (RBAC)
    4. Implement strong RBAC policies to control who can share data and who can access shared data. Define roles and permissions to ensure data security and compliance.

    5. Secure Data Sharing
    6. Use Secure Data Sharing (SDS) to securely share data with external parties without copying or moving the data. Implement encryption and access controls to protect sensitive information.

    7. Sharehouse Best Practices
    8. If using Sharehouse, follow best practices for creating and managing share objects. This includes defining share schemas, tables, and using the appropriate share options for your use case.

    9. Data Masking and Redaction
    10. Apply data masking or redaction policies to shared data to protect sensitive information. Ensure that shared data complies with privacy regulations and internal data governance policies.

    11. Query Performance Optimization
    12. Optimize query performance for shared data by using clustering keys, partitioning, and indexing. This helps enhance the efficiency of queries on large datasets.

    13. Versioning and Change Tracking
    14. Implement versioning and change tracking mechanisms to keep track of updates and changes in shared data. This ensures data lineage and helps with auditing and troubleshooting.

    15. Documentation and Metadata
    16. Maintain comprehensive documentation and metadata for shared datasets. Include information about the source, purpose, and any transformations applied. This helps users understand the shared data context.

    17. Governance and Monitoring
    18. Establish governance practices for data sharing, including regular reviews of shared data objects and access logs. Monitor data-sharing activities to identify any anomalies or potential security issues.

    19. Educate Users
    20. Provide training and documentation for users involved in data-sharing activities. Ensure they understand the best practices, security protocols, and the impact of data sharing on performance.

    21. Regular Audits and Reviews
    22. Conduct regular audits and reviews of shared data objects, permissions, and access controls. This helps maintain data integrity, security, and compliance with organizational policies.

    23. Cost Monitoring

    Keep an eye on the costs associated with data sharing, especially if you’re sharing data externally. Understand the pricing model and optimize data sharing configurations to manage costs effectively.

    By adhering to these best practices, you can:

    1. Shield Sensitive Data: Employ secure views to fortify sensitive information.

    2. Navigate Data Privacy Regulations: Ensure compliance with data privacy regulations by controlling access and usage.

    3. Illuminate the Purpose of Each Share: Maintain transparency regarding the intended purpose and content of each shared dataset.

    4. Efficiently Monitor Usage and Performance: Keep a finger on the pulse of usage patterns and optimize performance for streamlined data sharing.

    5. Elevate Your Data Analysis Journey: Enrich your analytics by exploring diverse data sources and unlocking fresh perspectives.

    What’s Next

    1. Enhanced Data Collaboration Tools:

  • Snowflake is expected to introduce advanced features for more streamlined and efficient data collaboration, addressing the evolving needs of industries.
  • 2. Innovations for Privacy-Preserving Analytics:

  • Snowflake aims to develop and implement solutions that uphold data privacy while enabling powerful analytics, aligning with the growing concerns in this area.
  • 3. Adaptation to Changing Industry Dynamics:

  • Snowflake is likely to evolve its platform to meet the challenges posed by changes in industry dynamics, ensuring it remains a robust solution for data management and collaboration.
  • 4. Integration of Cutting-Edge Technologies:

  • Snowflake may integrate emerging technologies, enhancing its capabilities to support new use cases and data-intensive analytic methods.
  • 5. Continued Emphasis on Secure Data Collaboration:

  • Future updates from Snowflake may include even more robust security measures and innovations in data clean rooms, reflecting a commitment to secure data collaboration in the face of a dynamic data landscape.
  • Best Way to Share Data for Your Business

    For secure collaboration, old ways of copying data are no longer the best. If you’re working with trusted partners and it’s privacy-compliant, Snowflake Secure Data Sharing is a quick and secure option. But, if you’re dealing with sensitive or regulated data, especially when the risk is high, consider using a data clean room for an extra layer of security and compliance.

    Beinex + Snowflake Offerings

    Beinex’s partnership with Snowflake enables us to offer you advanced features like automated tuning and elastic compute, along with analytics modernisation services, to help your organisation realise exponential Return on Investment.