Data platform architecture provides a structured framework for organizing and managing an organization’s data assets. It aligns technical solutions with data governance standards and policies to meet business needs.
A robust data platform architecture is crucial for organizations seeking to effectively leverage their data assets. It provides a structured framework that not only organizes and manages data but also aligns technical solutions with data governance standards and policies. This ensures compliance, reduces operational costs, simplifies maintenance, and promotes efficient data utilization to meet business objectives.
Problems to Solve by a Data Platform Architecture
The decision to design a new data platform architecture often arises from the challenges and limitations posed by existing legacy systems. These legacy architectures, while potentially functional in the past, may struggle to meet the evolving needs of modern data-driven organizations. Common issues include difficulties in integrating data from diverse sources, ensuring compliance with data regulations, and providing efficient access to data for analysis.
Outdated infrastructure can lead to operational inefficiencies, security vulnerabilities, and increased costs. A new architecture addresses these shortcomings by providing a structured and scalable foundation for data management. This ensures that the organization can effectively leverage its data assets for informed decision-making, improved operational efficiency, and gaining a competitive advantage.
Here are some of the key problems that a well-designed data architecture aims to solve:
- System Incompatibilities: Existing systems may be outdated and unable to work together, hindering data access and analysis.
- Compliance Risks: Lack of proper data governance can lead to violations of regulations and data security breaches.
- Data Discoverability: Users struggle to find the data they need or have to rely on outdated information.
- High Costs: Maintaining inefficient data platforms with unused data and resources leads to unnecessary expenses.
- Operational Inefficiency: Simple data management tasks take too long, slowing down projects and analysis.
- Security Vulnerabilities: Inadequate security measures leave data vulnerable to unauthorized access and misuse.
Goals to Achieve by a Successful Data Platform Architecture
Business sponsors and chief data officers have high expectations for a successful data architecture. They envision a system that not only supports current needs but also adapts to future demands. Key performance indicators (KPIs) will likely be used to measure success, focusing on aspects like reduced data management costs, improved data quality, faster time-to-insight, and increased compliance with data regulations. A successful architecture should empower the organization to extract maximum value from its data assets, fostering innovation, and driving better business decisions.
A well-defined data architecture strives to achieve the following goals:
- Automation: Automate repetitive tasks using reusable components and metadata-driven configuration. This reduces manual effort, minimizes errors, and frees up valuable resources.
- Cost Optimization: Reduce operational costs by optimizing resource usage and licensing fees. This includes efficient storage, processing, and data management practices.
- Simplified Maintenance: Make it easy for all team members to add new data sources and maintain the system. This reduces complexity and ensures long-term sustainability.
- Strong Governance: Ensure compliance with all regulations and enable root cause analysis through clear data lineage. This minimizes risks and promotes trust in the data.
The following cheat sheet summarizes all concepts of a successful data platform architecture.
Fundamentals of Data Platform Architecture
The success of a data platform architecture hinges on its ability to scale and adapt to the evolving needs of an organization. This requires careful consideration of how the architecture will support new data products and solutions, facilitate efficient development processes, and ensure effective metadata management. A well-defined architecture should provide a foundation for building and deploying data solutions with ease, while maintaining consistency and promoting reusability.
To achieve these objectives, the following fundamental components must be addressed:
- Reusability: Design reusable components and templates for common tasks like data ingestion and transformation. This accelerates development, reduces errors, and ensures consistency across data pipelines.
- Example: All data ingestion pipelines are created from a standardized template.
- Metadata Management: Establish a system for defining and keeping metadata synchronized across all datasets. This enables data discovery, facilitates data governance, and supports data lineage tracking.
- Example: Automatically register target tables in a data catalog.
- Data Governance: Prioritize and enforce data standards and policies across all data domains. This ensures data quality, compliance with regulations, and promotes trust in the data.
- Example: All output tables adhere to defined data contracts and are validated.
- Migration Planning: Analyze existing systems, identify risks, and create a plan for migrating to the new architecture. This ensures a smooth transition and minimizes disruptions to existing data processes.
- Example: Convert legacy ETL jobs into metadata-driven pipelines.
Layers of Data Platform Architecture
A comprehensive data platform architecture must define a clear approach for handling data at each stage of its lifecycle. Different layers require specialized tools and techniques to address their unique challenges. For instance, data ingestion involves acquiring raw data from various sources and may necessitate robust data integration tools. In contrast, the presentation layer focuses on delivering processed data to end-users and applications, prioritizing query performance and ease of use.
Here’s a breakdown of the key layers and their considerations:
- Data Ingestion: Define how data in different formats (batch, streaming) is acquired and initially stored. This involves establishing efficient data pipelines, handling data validation, and ensuring data quality at the source.
- Example: Incoming files are organized in date-partitioned folders within cloud storage.
- Data Transformation: Design and test standardized templates for data transformation processes. This includes data cleansing, enrichment, and aggregation to prepare data for analysis and consumption.
- Example: All data transformations use a common set of functions and libraries.
- Data Storage: Determine the appropriate storage solutions for raw, processed, and published data based on cost, security, and lifecycle needs. This might involve a combination of data lakes, data warehouses, and operational databases.
- Example: Use a cloud-based data warehouse for storing curated data marts.
- Data Publishing: Define how users and applications access the data. This involves providing appropriate access controls, data formats, and interfaces for different consumption needs.
- Example: Provide data analysts with a SQL endpoint to access data for analysis and reporting.
Designing Data Platform Architecture for Maintainability
Data platforms and the data products they support are long-term investments. They must be designed to withstand changes in data volume, data models, and user demands. A resilient architecture anticipates these evolving needs and provides mechanisms for adaptation and maintenance. This begins with embedding monitoring and data observability into the platform’s core. By proactively identifying potential issues, organizations can ensure the continuous health and performance of their data ecosystem.
When problems arise, efficient incident management processes are crucial. This includes clear roles and responsibilities for data operations teams, as well as streamlined workflows for issue resolution. Furthermore, the architecture should facilitate smooth updates and deployments to address bugs, incorporate new features, and adapt to changing requirements.
Finally, a robust data quality framework is essential to address data-related issues such as data drifts and schema changes. These issues can significantly impact the reliability and trustworthiness of data products.
Here’s how to ensure the long-term maintainability of your data architecture:
- Monitoring: Monitor pipelines, infrastructure, and data quality to ensure system health and identify issues. This includes real-time monitoring of data flows, infrastructure performance, and data quality metrics.
- Example: Set up alerts for pipeline failures and data quality violations.
- Incident Management: Define clear roles, responsibilities, and workflows for handling and escalating incidents. This ensures prompt responses to critical issues and minimizes downtime.
- Example: Automatically generate support tickets for critical data quality issues.
- Deployment: Plan and automate the deployment of architecture components across the infrastructure. This enables efficient and reliable updates, minimizing disruptions to data operations.
- Example: Use a CI/CD pipeline to deploy updates to data processing applications.
- Data Quality: Implement data quality checks within pipelines and establish ongoing data quality testing procedures. This includes data profiling, validation, and reconciliation to identify and address data quality issues proactively.
- Example: Include data profiling and validation steps in data pipelines to identify and address data quality issues.
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What is the DQOps Data Quality Operations Center
DQOps is a data observability platform designed to monitor data and assess the data quality trust score with data quality KPIs. DQOps provides extensive support for configuring data quality checks, applying configuration by data quality policies, detecting anomalies, and managing the data quality incident workflow.
DQOps is a platform that combines the functionality of a data quality platform to perform the data quality assessment of data assets. It is also a complete data observability platform that can monitor data and measure data quality metrics at table level to measure its health scores with data quality KPIs.
You can set up DQOps locally or in your on-premises environment to learn how DQOps can monitor data sources and ensure data quality within a data platform. Follow the DQOps documentation, go through the DQOps getting started guide to learn how to set up DQOps locally, and try it.
You may also be interested in our free eBook, “A step-by-step guide to improve data quality.” The eBook documents our proven process for managing data quality issues and ensuring a high level of data quality over time. This is a great resource to learn about data quality.
