Data Architecture

Data architecture frameworks

A data architecture framework is a conceptual structure used to plan, develop, implement, govern and sustain an architecture. The three main frameworks are:

TOGAF: The most commonly used data architecture is TOGAF, developed by The Open Group in 1995. This framework focuses on aligning data architecture strategy and objectives with business goals.

DAMA-DMBoK2: DAMA International originally published the Data Management Body of Knowledge in 2018. This framework focuses on data management and includes definitions and guidelines on data management principles.

Zachman Framework: Developed in 1987 by John Zachman, this framework is a matrix for managing enterprise architecture. It’s designed to help organize elements such as models, specifications and documents. Six questions are central to this framework: who, what, when, where, why and how.

Components of data architecture

Data architecture consists of several different components that organizations bring together to effectively leverage their data. Examples of key components include:

Data storage: Data storage is a mechanism for storing and managing data, ensuring that it is securely saved and organized and is available to be retrieved, processed and analyzed.

Data pipeline: A data pipeline is an end-to-end process for moving data from one system to another — from storage to an application, for example. The pipeline includes all the steps in the data process, such as refinement, storage and analysis.

Data streaming: Data streaming also moves data from one place to another, but the data flows in a continuous stream from source to destination. Data streaming enables real-time processing and analytics.

Data dashboard: A data dashboard is a user interface that visually presents important metrics and insights from various sources. It enables real-time monitoring, analysis and decision-making.

Data governance: Data governance is the process of creating policies and frameworks for efficient data handling. By aligning data-related requirements with business strategy, data governance provides superior data management, quality and visibility. Good data governance empowers an organization to make the most of its data while ensuring security and compliance.

Data integration: When data architecture fosters data integration, data flows easily between systems, and data silos are broken down — unlocking the ability of an organization to capitalize on its data.

Data sharing: Data sharing is the ability to make data available for internal or external use. Data architecture that supports effective data sharing enables collaboration and creates opportunities to generate new revenue streams with data monetization.

Data analytics: Analytics is the process of analyzing and interpreting data. Analytics transforms raw data into actionable insights by uncovering patterns, trends and correlations. Real-time analytics refers to the practice of collecting and analyzing streaming data as it is generated, and it’s often used in applications where timeliness is critical. Real-time analytics is built on the foundational capability of data streaming.

AI and machine learning: The right data architecture is essential for harnessing the power of AI and machine learning, whether an organization is using machine learning models for valuable insights or building AI applications. Data architecture supports AI infrastructure, enables effective data flow and analysis, and directly impacts AI and machine learning results.

Data marketplace: A data marketplace is an online store that enables the exchange of data products between data producers and data consumers.

Types of data architecture

Enterprises have different types of data architectures to choose from based on their needs and objectives. Some of the most common types of data architectures include:

Lambda architecture: Lambda architecture is a way of processing massive quantities of data that takes a hybrid approach to batch-processing and stream-processing methods. Lambda architectures can be highly complex. Administrators must typically maintain separate code bases for batch and streaming layers, which can make debugging difficult.

Data Mesh: Data Mesh is a paradigm that describes a set of principles and logical architecture for scaling data analytics platforms. It unites disparate data from multiple sources through centralized data governance and sharing to improve access and security.

Data warehouse: A data warehouse is a data management system that stores structured data in a predefined schema. It includes current and historical data from multiple sources and facilitates easier insights and reporting. Data warehouses are typically used for business intelligence (BI), reporting and data analysis.

Data lake: A data lake is a data repository that stores data in its native, raw format, enabling cost-effective storage of large amounts of data from various sources. Data lakes allow users to store data as is, without having to structure it first and then run a variety of analytics on it. These analytics can include: dashboards and visualizations, big data processing, real-time analytics, and machine learning.

Data lakehouse: A data lakehouse is an open data management architecture that combines the flexibility, cost-efficiency and scale of data lakes with the data management capabilities of data warehouses. Data lakehouses ensure that teams have the most complete and up-to-date data available for data science, machine learning and business analytics projects without needing to access multiple systems.

Medallion architecture: A medallion architecture is a data design pattern used to logically organize data in a lakehouse. The goal is to incrementally and progressively improve the structure and quality of data as it flows through each layer of the architecture.

Data architecture best practices

The right data architecture is critical for helping an enterprise leverage its data for insights that fuel success. To ensure the best results, data architecture best practices include:

Business alignment: Data architecture should support the company’s long-term goals and business objectives.

Flexibility and scalability: Data architecture should be easily adaptable to changing needs. As data volumes grow, architecture should scale to deliver insights in real time and support machine learning and AI initiatives.

Integrated governance and security: Governance and security should be central to architecture design. Architecture should support efficient governance and the integration of machine learning and AI with data platforms.

Unification: A unified approach allows varied data workloads to operate seamlessly on the same data while ensuring security and governance.

Open foundation: Vendor lock-in with proprietary tools hinders widespread adoption and restricts innovation. Working with an open foundation encourages easy data integration and sharing for better insights.

Data democratization: Data architecture should prevent bottlenecks that hinder teams from using data to innovate while integrating clearly defined data governance policies.

Data architecture on Databricks

The Databricks Data Intelligence Platform, built on the lakehouse architecture, delivers a unified, secure and governed solution for data and AI, emphasizing reliable performance, data-centric AI capabilities, and flexible, cost-effective serverless data warehousing, all without vendor lock-in.

The lakehouse architecture combines the best elements of data lakes and data warehouses to help reduce costs and deliver on data and AI initiatives faster. Built on open source and open standards, the Databricks Platform eliminates the silos that historically complicate data and AI workloads.

As part of the Databricks Data Intelligence Platform, Unity Catalog offers unified governance for data and AI on lakehouses, enabling seamless collaboration, enhancing productivity and ensuring compliance across all platforms.

The Databricks Data Intelligence Platform addresses the multifaceted challenges faced by businesses today. With architecture that safely democratizes data, Databricks helps every team member harness the potential of data for organizational success.