© 2022 by ISMAR
Sponsored by the IEEE Computer Society Visualization and Graphics Technical Committee and ACM SIGGRAPH
If you’re looking to modernize your data strategy, adopting a Data Lakehouse with the Medallion Architecture is a smart move. You can organize raw information through distinct Bronze, Silver, and Gold layers, making data easier to manage and use. Understanding how engines like Apache Spark power this process can give your analytics a real edge. But before you jump in, there are key concepts and best practices you don’t want to overlook.
The Medallion Architecture is an effective framework for structuring data in a lakehouse environment by segmenting it into three layers: Bronze, Silver, and Gold. This structured approach facilitates a systematic process of data management.
The Bronze layer is primarily used for data ingestion, where raw data is stored. This layer emphasizes traceability and serves as a foundational stage for data engineering efforts. By retaining unrefined data, organizations can ensure they've access to original datasets for reference and compliance purposes.
Transitioning to the Silver layer introduces schema enforcement and data cleansing processes. This stage is essential for enhancing data quality, as it involves the application of defined structures and the elimination of inaccuracies. With improved data quality, the Silver layer supports more reliable analytics and decision-making processes.
Lastly, the Gold layer is dedicated to the preparation of analytics-ready data. Data in this layer is optimized for business intelligence (BI) applications, ensuring that it's readily accessible for reporting and analysis. This separation of data into distinct layers reduces complexity within the data pipeline, fostering trust in the reliability and usability of the data.
The Bronze, Silver, and Gold layers within a lakehouse architecture serve distinct purposes that contribute to the overall data management process.
The Bronze layer is responsible for the ingestion of external data in its unprocessed form. This layer retains historical data, which is essential for audit trails and maintaining data integrity. It acts as a foundation for data quality, ensuring that all incoming information is captured without alteration.
The Silver layer focuses on enhancing the data captured in the Bronze layer through processes such as data cleansing, integration, and schema enforcement. This layer aims to provide a cohesive view of the data, which is vital for conducting self-service analytics. By standardizing the data, the Silver layer makes it easier for users to extract meaningful insights without engaging in complex data manipulation.
The Gold layer is designed to produce analysis-ready datasets that are optimized for reporting and visualization purposes. This layer ensures that the data is trustworthy and relevant, allowing stakeholders to make informed decisions based on accurate information.
The structured approach of the Medallion Architecture—moving from raw data in the Bronze layer to refined insights in the Gold layer—facilitates a systematic transition toward actionable analytics. Each layer's functionality is crucial for enhancing data utility and ultimately supports better decision-making.
The Platinum layer enhances the lakehouse architecture by enabling real-time, action-oriented applications, going beyond the capabilities provided by the Gold layer, which offers refined, analysis-ready data for strategic decision-making.
The Platinum layer integrates real-time analytics and machine learning to facilitate immediate insights and actions. This approach supports organizations in adapting swiftly to changing conditions.
In this layer, automated insights and operational analytics are crucial components, allowing for the development of dynamic dashboards and embedded intelligence tools. These features enhance decision-making processes by providing timely and relevant information.
The Platinum layer's robust data frameworks ensure a seamless flow of data, which is essential for agility in business operations.
The implementation of the Platinum layer within Medallion Architecture represents a critical advancement in data capabilities, directing organizations towards more informed and rapid decision-making processes that promote innovation rather than just reliance on analytics.
Data engineering teams require both efficiency and reliability when building data pipelines. Declarative tools such as Lakeflow in Databricks facilitate the construction and management of these pipelines across the Bronze, Silver, and Gold layers of the Medallion Architecture.
These tools allow for the orchestration of data ingestion, including both batch and streaming processes, and utilize Apache Spark to accommodate real-time data demands without necessitating excessive coding.
Automated validations can be configured easily, enabling teams to maintain oversight of data quality throughout the transformation processes. Additionally, integrating with Delta Lake helps maintain data lineage and version control between the Silver and Gold layers, which aids in conducting audits and executing queries more effectively.
The implementation of incremental refresh strategies ensures that complete data reloads are avoided, thereby enhancing the reliability of the data pipelines.
Ensuring data quality throughout the stages of the Medallion Architecture is essential for maintaining trust in analytics. At the Bronze layer, it's important to conduct regular evaluations of row counts and null values to identify any ingestion issues at an early stage.
Progressing to the Silver layer, the focus should shift to data cleansing and transformation. This can be achieved through validation measures such as table-by-table count matching, which helps maintain data integrity.
In the Gold layer, entity count matching and baseline quality tests play a vital role in protecting business-critical metrics.
Implementing proactive monitoring techniques, including automated testing and continuous profiling, is necessary for the rapid detection of any potential problems. Additionally, employing data quality scoring across all layers can assist in identifying discrepancies, ensuring that data flows remain reliable and of high quality.
The Medallion Architecture is designed to enhance the efficiency of data pipelines, but several common pitfalls can compromise its effectiveness. One significant issue arises when organizations treat raw data in the Bronze layer as ready for reporting without implementing thorough data quality checks. This practice can lead to inaccurate analyses and dashboards, ultimately diminishing stakeholder trust in the data.
Another challenge occurs in the Silver layer, where the loading of tables with heavy aggregations or inconsistent key performance indicators (KPIs) can create semantic sprawl. This sprawl complicates data governance, making it more difficult to manage and understand the data landscape effectively. Ensuring consistency and clarity of KPIs within this layer is essential for maintaining the integrity of the overall data architecture.
When it comes to the Gold layer, which is typically optimized for batch processing, there's often an expectation for real-time analytics. This expectation can result in brittle data pipelines that may not perform well under the strain of real-time demands, leading to performance issues and increased maintenance overhead.
It is also crucial to maintain distinct responsibilities across the layers of the architecture. A lack of clear boundaries can lead to confusion and overlapping functions that jeopardize the effectiveness of the layered approach.
Therefore, maintaining clarity and discipline in the management of the Medallion Architecture is vital for supporting scalable and high-quality data pipelines and analytics. Adopting best practices within this framework can help organizations avoid these common pitfalls and enhance their data operations.
Integrating query engines such as Apache Spark™ and Delta Lake into your Medallion Architecture enables efficient data access through the Bronze, Silver, and Gold layers.
These engines facilitate SQL queries, allowing for analytics to be conducted directly on the lakehouse without necessitating data duplication or movement across layers. Utilizing open data formats like Delta and Parquet enhances query performance through features like columnar storage and predicate pushdown.
This setup supports both real-time and batch processing, which can lead to improved decision-making speed and quality.
Query engines equipped with auto-scaling capabilities are capable of effectively managing varying workloads, which helps maintain consistent performance across all layers of the Medallion Architecture.
This integration provides a more streamlined approach to data analytics while optimizing resource usage.
The Medallion Architecture and Data Mesh represent two distinct frameworks for managing data within organizations, each with its own set of principles and practices.
The Medallion Architecture consists of three layers: Bronze, Silver, and Gold. Data progresses from the Bronze layer, where raw data is ingested, through the Silver layer, which involves data transformation and cleansing, to the Gold layer, where curated and high-quality data is made available for analysis. This approach emphasizes the importance of enforcing schemas and maintaining data quality in centralized data lakes, promoting operational efficiency and standardization.
In contrast, the Data Mesh framework adopts a decentralized strategy. It allocates data ownership to domain-specific teams, facilitating ownership and accountability over their respective datasets. This structure fosters rapid iterations and enhances collaboration across domains, as teams can respond more promptly to changes and needs within their specific areas.
The choice between Medallion Architecture and Data Mesh should be informed by specific organizational requirements, taking into account aspects such as governance, scalability, and the level of autonomy desired among data teams.
Medallion Architecture typically emphasizes consistency and operational efficiencies, while Data Mesh offers flexibility and adaptability, reflecting diverse team dynamics and innovation strategies. Each framework has its merits, and the decision should align with the organization's overall data strategy and objectives.
Successful implementation of a data lakehouse requires a strategic approach that emphasizes the integration of appropriate tools, technologies, and industry best practices. One effective framework for data organization is the Medallion Architecture, which segments data into Bronze, Silver, and Gold layers through the use of Delta Lake. This structured approach enhances data management and facilitates the handling of workloads typical in data warehousing scenarios.
Utilizing Apache Spark in conjunction with Databricks pipelines is advisable for creating scalable and efficient data pipelines. These technologies support large-scale data processing and can accommodate varying data volumes and velocities. Ensuring data quality is critical; this can be achieved through automated monitoring systems and schema validation consistently applied across all data layers.
Data governance is another important consideration. Implementing a centralized data catalog, combined with role-based access control, enhances oversight and security. This governance framework helps maintain data integrity and compliance with relevant regulations.
Regular performance evaluations are necessary to identify optimization opportunities and manage operational costs effectively. Employing automated monitoring tools can provide insights into system performance, allowing for data-driven decisions aimed at enhancing efficiency in the lakehouse environment.
With the Medallion Architecture, you can bring order and efficiency to your data lakehouse. By layering data into Bronze, Silver, Gold, and even Platinum, you’ll streamline ingestion, cleansing, and analytics, paving the way for reliable, actionable insights. Embrace modern engines and open formats to boost performance, avoid common pitfalls, and keep your pipelines robust. As you implement these best practices, you’re not just storing data—you’re empowering smarter decisions across your organization.
Sponsored by the IEEE Computer Society Visualization and Graphics Technical Committee and ACM SIGGRAPH