Many factors to consider to build data analytics on top of Data Mesh
Using data analytics on top of a Data Mesh, even when data is fragmented across multiple domains, can be a challenging but essential task. The fragmented nature of data in a Data Mesh is a feature, not a bug, as it promotes data ownership and self-service. Here's how you can effectively use data analytics in such an environment:
Federated Queries: Query engines like Presto allow querying across disparate data sources and formats. This enables joining data from multiple domains.
Unified Analytics Layer: A dedicated analytics environment can aggregate data from domains into a centralized analytics schema/warehouse to power BI and reporting.
Domain Replication: Relevant domain data can be replicated into a centralized analytics substrate for performance and consistency.
Catalog-driven Discovery: Leverage the catalog for analysts to understand what data exists where and how to access it.
Streaming Pipelines: Use streaming pipelines to bring domain data products together for real-time analytics.
Caching: Strategically cache data from frequently used domains to speed up analysis.
Virtualization: Data virtualization tools can provide unified virtual views across domains for easier analytics.
Data Product APIs and Standardization: Each domain within the Data Mesh should expose data products through standardized APIs. These APIs provide a consistent way to access data and ensure that data consumers have a clear interface to interact with.
Data Catalog and Discovery: Leverage the data catalog and discovery tool within the Data Mesh to find and understand available data products. This tool should provide metadata, schema information, and descriptions of data products to help data analysts identify the data they need.
Metadata Management: Utilize metadata management tools to capture and maintain metadata about data products, schemas, lineage, and transformations. This metadata is critical for data analysts to understand the context and quality of the data.
Data Quality Assurance: Before performing analytics, ensure that the data products meet the expected quality standards. Data quality frameworks like Great Expectations can be used to validate data against predefined expectations.
Data Transformation and Wrangling: Since data products might not always align perfectly with your specific analysis needs, you may need to perform data transformation and wrangling to shape the data according to your requirements. Use data transformation tools like Apache Spark or data wrangling platforms.
Collaboration with Domain Experts: Engage with domain experts from the data product teams. They have in-depth knowledge of the data and can provide context, domain-specific insights, and assistance in understanding the data.
Cross-Domain Queries: If your analysis requires data from multiple domains, design queries or pipelines that can pull data from different data products and combine them as needed. Ensure that you have the necessary access permissions.
Data Observability: Continuously monitor the health and quality of data products using data observability tools. This ensures that your analysis is based on up-to-date and reliable data.
Version Control and Reproducibility: Implement version control for data pipelines and analysis code. This ensures that your analyses are reproducible, even when the underlying data evolves.
Data Governance: Adhere to data governance policies and ensure that you are compliant with data privacy regulations, especially when working with sensitive data.
Data Reporting and Visualization: Utilize data reporting and visualization tools to present your analytical findings. These tools should support dynamic data retrieval and updates based on the evolving data products.
Feedback Loop: Establish a feedback loop with data product teams. If you find issues with data quality or need additional data, communicate your findings and requirements to the relevant domain teams. This iterative process can help improve data over time.
Documentation and Knowledge Sharing: Document your data analytics processes, including data sources, transformations, and insights. Share your findings with other stakeholders to foster knowledge sharing within the organization.
Using data analytics on top of a Data Mesh does require adaptability and collaboration. The fragmentation of data is intentional and serves the purpose of data ownership and autonomy within each domain. By following these best practices and leveraging the tools and infrastructure of the Data Mesh, you can effectively perform data analytics and gain valuable insights while respecting the principles of the Data Mesh architecture.
Sash Barige
Aug/31/2022
Photo credit: unsplash.com
Data Mesh: https://www.oreilly.com/library/view/data-mesh/9781492092384/ by Zhamak Dehghani
Data Mesh Architecture: https://www.datamesh-architecture.com/ by Google Cloud
How to select technology for Data Mesh - Thoughtworks: https://www.thoughtworks.com/insights/blog/data-strategy/how-to-select-technology-data-mesh by Thoughtworks
What is a data mesh? - IBM: https://www.ibm.com/topics/data-mesh by IBM
Data Mesh in Practice: https://www.thoughtworks.com/insights/e-books/data-mesh-in-practice by Max Schultze and Arif Wider
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