Technical and Strategic Best Practices for Building Robust Data Platforms

Originally published at https://www.dataversity.net on December 2, 2024.

Technical and Strategic Best Practices for Building Robust Data Platforms – DATAVERSITY

In the AI era, organizations are eager to harness innovation and create value through high-quality, relevant data. Gartner, however, projects that 80% of data governance initiatives will fail by 2027. This statistic underscores the urgent need for robust data platforms and governance frameworks. A successful data strategy outlines best practices and establishes a clear vision for data architecture, ensuring data remains accessible, secure, and ready for advanced analytics and AI.

By investing in modern data platforms, fostering a data-centric culture, and implementing strong governance, organizations can fully leverage data and AI to drive transformative outcomes and stay competitive in an increasingly data-driven landscape.

Defining a Data Platform

A data platform is an integrated system that manages the entire data lifecycle from ingestion to analysis, serving as a unified foundation for collection, storage, processing, and access. Evolving beyond traditional warehouses, modern data platforms now support real-time analytics and machine learning (ML) to meet growing demands for flexibility.

A modern data platform architecture consists of four core layers: infrastructure, data management, processing, and access. The infrastructure layer includes storage, compute, network, and security, forming the platform’s physical foundation. Data management oversees metadata, data quality, master data, and lifecycle processes. The processing layer supports both batch and real-time analytics, while the access layer provides APIs, query tools, and self-service access, enabling secure, scalable, and efficient data retrieval.

Data management components include multimodal storage for structured, semi-structured, and unstructured data types, batch and streaming processing engines, integration for seamless data connectivity, and a serving layer offering APIs and interfaces for on-demand access. The core services of integration, processing, governance, and operations collectively enhance platform functionality. Integration handles ETL/ELT processes, processing supports batch and real-time analytics, governance maintains data quality and compliance, and operational services optimize platform performance and ensure smooth functioning.

The data lifecycle comprises ingestion, storage, processing, serving, and archival. To achieve scalability and persistence, data is gathered from diverse sources, validated, and securely stored. Processing transforms raw data into insights through cleansing and aggregation, supporting real-time and batch needs. The serving layer provides end-users access to processed data through APIs and dashboards, while archival moves less-accessed data to cost-effective storage, ensuring compliance. Together, these stages form a complete, secure data journey from ingestion to actionable insights.

Essential Non-Functional and DataOps Attributes

A modern data platform requires essential non-functional and DataOps features to operate effectively. Scalability, elasticity, reliability, and performance ensure the platform can handle growing data volumes and user demands. Security and compliance protect data access and enforce regulatory standards while monitoring, telemetry, and observability provide continuous diagnostics and insights. Availability, maintainability, and supportability facilitate consistent performance, minimal downtime, and streamlined operations. Cost-efficiency and portability optimize resource use and flexibility across environments. These non-functional characteristics create a robust, adaptable platform designed for long-term success.

A strong, robust approach to data strategy relies on a multimodal storage architecture that supports structured, semi-structured, and unstructured data-each with unique management needs. Structured data follows defined schemas, semi-structured data includes hierarchical markers, and unstructured data lacks predefined models. Choosing appropriate storage options (block, object, or file) is crucial for balancing availability and performance. Architectural patterns are shaped by the five Vs of data: volume requires scalable storage, velocity demands real-time processing, variety accommodates diverse data types, veracity focuses on maintaining data quality, and value drives the need for advanced analytics and ML to generate actionable insights.

Organizations can address operational and analytical data requirements within a unified architecture combining databases, data warehouses, and data lakehouses. This approach incorporates the transactional consistency of traditional databases alongside the flexibility of data lakes, supporting both online transaction processing (OLTP) and online analytical processing (OLAP) workloads. Data warehouses serve as the data-serving layer, offering centralized, optimized environments for analytics. These platforms can manage large-scale data using SQL-like queries, generating reports, dashboards, and insights essential for decision-making.

The modern lakehouse architecture utilizes a medallion architecture to organize data in bronze (raw data), silver (validated and conformed), and gold (business-level aggregates) layers. This tiered approach ensures lineage, performance optimization through cached aggregates, and data quality control at every stage, balancing performance with cost efficiency and supporting operational and analytical needs.

Technical Implementation Strategy

Effective data integration is crucial, enabling a continuous data flow between systems with batch and real-time stream processing. Batch processing remains essential for high-volume data, frequently using tools to manage large-scale datasets with speed and efficiency. Real-time streaming capabilities are achieved through technologies like Apache Kafka and Flink, while change data capture (CDC) maintains data consistency, ensuring that decision-making is based on the latest information available.

Data engineering plays a central role in data platform architecture, with pipelines designed to support diverse processing requirements while ensuring reliability. Typical pipeline elements include source connectors, transformation engines, quality controls, and monitoring systems. Orchestration tools automate workflows, providing error handling and data movement management that help maintain platform integrity.

Optimizing a data platform for performance requires tailored strategies for OLTP and OLAP workloads. In OLTP environments, efficient indexing and buffer management are vital for high transaction volumes, whereas OLAP optimization leverages partitioning, query rewriting, and materialized views to improve analytic speed. Federated query engines enhance performance by allowing seamless queries across diverse data sources without replicating data. By employing techniques like data partitioning, predicate pushdown, and adaptive query execution, these engines deliver unified insights across the data landscape with minimal overhead.

Governance and Security Framework

A well-defined governance framework supports data quality, compliance, and value across strategic, tactical, and operational levels, ensuring effective policy and control implementation. Metadata management is vital and enables data discovery and consistent governance by automatically capturing technical, business, and operational metadata, thus supporting regulatory compliance.

Comprehensive multilayer data security protects data throughout its lifecycle. Critical security measures include multifactor authentication, identity and access management (IAM) with SSO, role- and attribute-based access control (RBAC/ABAC), and encryption (both at rest and in flight).

Best practices, such as the principle of least privilege, regular access reviews, and security monitoring, enhance protection. Compliance with regulations like GDPR and CCPA ensures that data meets industry standards for privacy and protection.

Data Quality Management

Data quality includes accuracy, completeness, consistency, timeliness, and validity. The implementation strategy for data quality management includes automated quality checks, quality scoring mechanisms, issue resolution processes, and continuous monitoring.

Implementing a robust data platform requires a phased approach that starts with foundational elements and gradually integrates more advanced capabilities. Successful implementation relies on effective change management — engaging users, offering training, and gathering feedback to ensure adoption.

Ultimately, a strong data platform transforms data into a strategic asset, driving innovation and competitiveness. Commitment to best practices and continuous improvement enhances operational efficiency and enables data to fuel ML and deeper insights, empowering organizations to lead in an increasingly data-centric world.