Businesses worldwide are grappling with the challenge of shifting from a digital economy to a data economy to benefit from the power of data and to stay competitive. This has led to the need for moving from digital infrastructure to a data-centered architecture. 

Currently, AI technologies are reshaping the way businesses think about data architecture. This makes the need for modernization a must-have business requirement and not just a means to a competitive edge. There is a sudden need to create integrated AI-enabling infrastructure technologies and components across enterprises. These stacks must work together to enable data integration and flexible processing. 

This CDO’s guide to 2026 data infrastructure takes a dive into the key shifts taking place, the most promising frameworks, and the priorities paving the path ahead.

The Evolution of Data Infrastructure

Data architectures have come a long way from conventional on-premise databases to advanced, cloud-based data ecosystems. These architectures are no longer monolithic. They were also often siloed traditionally, affecting data scalability and keeping integration prospects limited. 

The emergence of Big Data means that conventional data warehouses can no longer handle data volumes, variety, and speed. This led to the appearance of distributed systems such as Spark and Hadoop, enabling scalable processing and storage of extensive datasets. Now, cloud-based data architectures lead the ecosystem. Cloud platforms offer scalability and flexibility while being cost-efficient. The services they offer include data storage, processing, machine learning, and analytics. 

More businesses are expanding their business locations. With this comes the need for data sovereignty. Surveys show that a significant percentage of businesses consider data compliance and sovereignty as essential to future data and IT infrastructures.

It is recommended to redefine your data architecture priorities and implement the right frameworks. All the current market trends indicate a need for distributed data architectures. 7 in 10 North American businesses in surveys claim they have not invested adequately in analytical and infrastructure tools. According to 72% of businesses, data management is the number one hindrance to expanding AI implementation. Over 8 in 10 enterprises still rely on siloed data.

Data Infrastructure Priorities

Modern data architecture revolves around data democratization and real-time data processing. Data needs to be made accessible to everyone in the organization, not just the data experts. It is supported by technologies such as data meshes that decentralize data ownership and data fabrics that connect data across different systems such as on-premise, cloud, and hybrid.

The following are the key priority areas where your business should be focused on when it comes to embracing next-gen data infrastructures:

i. Microservices Architecture

Embracing microservices architecture can help streamline your data processing workflows. This step can help expand scalability and enables independent or standalone deployment and expansion of individual services. Additional benefits include resource allocation optimization and increased data system efficiency.

ii. Hybrid Data Management Models

A hybrid data management model can offer you the benefits of both cloud and on-premise infrastructures. When designed and managed effectively, it can help optimize:

• Control costs
• Performance
• Improved security

On-premise resources can simplify sensitive data management, while at the same time, you can benefit from the vast resources on the cloud for advanced processing and analytics.

iii. Data Observability

Data observability tools can help monitor data lineage, quality, and performance. These tools can trace the entire data journey from its origin through its final destination. They can ensure data quality through continuous monitoring of data freshness, accuracy, and completeness. They can also monitor latency, resource utilization, and throughput across pipelines. All this can help you detect and address issues in a timely manner and maintain data reliability and integrity.

iv. Distributed Data Environments

Data architecture can be designed to efficiently manage distributed data environments. This can be achieved by leveraging technologies such as data meshes and data lakes. You can expect improvements in the following parameters across different geographical locations: 

• Data consistency 
• Data accessibility
• Data performance

v. Data Versioning

Implementing data versioning can help track changes in datasets across different timelines. This can help with data-based applications in the areas of auditing, reproduction, and consistency. The benefits are more profound in the areas of analytics and machine learning.

It is important to design a flexible data architecture that can readily scale up and down. This can be achieved with the help of cloud-native services, serverless computing, and containerization wherever required.

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Strategies for Redesigning Your Data Architecture

It is recommended to implement the following strategies to redesign your data architecture:

i. Gain a Deep Understanding of Your Business Goals

Identify the core objectives of your organization. This can help you align your data initiatives with your priorities. For example, when your objective is to improve customer experience, the data architecture should focus on custom data management and analytics.

This step requires engaging stakeholders across different departments to learn about their unique data challenges and needs. This can help you design a data architecture addressing diverse requirements while fulfilling your business goals. 

ii. Set Up Access Controls & Authentication

Strong access controls and authentication systems should be implemented to prevent unauthorized access to sensitive data. It is recommended to implement multi-factor authentication using:

• Passwords
• Biometric verification
• Security token

Regular security monitoring and periodic security audits can help detect and respond to potential security threats. Data security is an important element of data architecture.

iii. Indexing & Partitioning

Indexing and partitioning can help manage large datasets and optimize database performance. 

• Indexing: This involves the creation of a data structure to improve data retrieval speeds. Query performance can speed up significantly when key columns are indexed in a database. This reduces the time needed to access records.
• Partitioning: Large datasets should be divided into small, manageable segments. The two different options include horizontal and vertical partitioning.

iv. Maintain Accurate & Up-to-date Metadata

Metadata provides key information on data, including usage, origin, meaning, and structure.  When regularly updated, it can assist with data management and usage. Users can rely on accurate metadata to understand data context, integrate, and utilize data properly.

It is recommended to:

• Create metadata management practices and set up the right tools
• Engage subject matter experts and data stewards in the metadata management process

Ensuring high-quality and updated metadata improves data discoverability, compliance, and usability. This can result in improved data-based decision-making.

v. Focus on Low-Latency Data Ingestion & Processing

Optimize data pipelines and infrastructure to prevent delays. This can also help with the timely availability of data. Low-latency data infrastructure design involves efficient data ingestion. This can be achieved by utilizing stream processing frameworks such as Apache Flink, Apache Kafka, and Amazon Kinesis. They can help with real-time data gathering and processing. Your organization will then be able to respond to insights and events in real-time. Latency can further be reduced by supplementing batch processing with micro-batching methods.

Data access and retrieval speeds increase with optimized storage solutions. Data read-and-write times can be reduced by using in-memory databases and implementing caching strategies. The application of Apache Spark and other similar distributed processing frameworks can increase the performance and scalability of data processing tasks.

vi. Set Up Data Quality Metrics & Monitoring

Setting up data quality metrics focuses on defining standards for data accuracy, consistency, completeness, timeliness, and validity. They set the benchmark for measuring and assessing data quality. 

Data quality monitoring can be achieved by deploying automated tools that consistently evaluate data against the preset standards. The following methods can be used to identify and address data quality challenges as part of the process:

• Data validation
• Data anomaly detection
• Data profiling

Ensuring regular data quality audits and evaluations can help maintain data integrity over time.

 

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Popular Data Infrastructure Frameworks & Tools

Some of the notable data architecture frameworks and tools that can be implemented to bring transformation include:

Enterprise Architecture & Governance Frameworks

The Open Group Architecture Framework (TOGAF)

TOGAF offers a structured approach to design, plan, implement, and manage enterprise data architectures. Some of its key attributes are as follows:

• Developing a clear blueprint of the latest and future data architecture
• Aligning business and IT goals
• Techniques and tools to facilitate acceptance, development, utilization, and maintenance of information architectures
• Architecture Development Method (ADM) helps data architects through the entire process of developing and managing the architecture
• Focus on stakeholder management to ensure the needs of all stakeholders are addressed 

The use of TOGAF can help your business increase efficiency, ability, and flexibility for effective change management.

Zachman Framework

This enterprise architecture platform offers a structured approach to define and analyze your business’s information architecture. It leverages a 2D matrix to organize and classify different architectural artifacts, providing a complete view of the enterprise.

Some of its key features include:

• It features six columns that stand for different perspectives
• There are 6 rows, each for a different stakeholder, ranging from the planner to the end user
• Your organization can capture and document all aspects of the architecture, improving understanding and communication between the stakeholders

Zachman Framework simplifies the process of aligning your business and IT goals while ensuring all data architecture elements are taken into account.

Data Management Capability Assessment Model (DCAM)

DCAM was developed by the Enterprise Data Management (EDM) Council. It is designed to evaluate and enhance data management capabilities. It offers a series of standards and best practices to manage data.

The framework assesses different elements of data management, such as:

• Governance
• Architecture
• Quality
• Operations

The utilization of DCAM can help you identify weaknesses and strengths in your data management practices, create focused improvement plans, and track progress. The results include improved data quality, optimized value from data assets, and ensuring regulatory compliance.

Data Processing & Orchestration Tools

Apache Spark

Apache Spark is a unified analytics engine that offers seamless AI and machine learning workflow integration. It can manage batch and real-time processing via a single engine. The Unified Engine architecture enables your data analytics teams to seamlessly switch between Python, SQL, and R in the same application.

Some of its key features include:

• Native cloud integration with leading providers
• Improved GPU support for AI and machine learning workloads
• Better memory management through Dynamic Resource Allocation 2.0

Airflow

This workflow management framework is designed to schedule and run complex data pipelines in big data systems. Airflow helps data specialists ensure all workflow tasks are completed in proper order while providing them access to the right system resources. 

It relies on Python to create workflows. You can use Airflow to develop machine learning models, data transfers, and much more. Its main features include:

• Scalable and modular architecture based on the concept of directed acyclic graphs
• Pre-built integrations with leading cloud platforms and various third-party services
• Web application user interface that provides insights into data pipelines. This makes it easy to monitor development status and address issues.

Databases, Query Engines & Analytics Tools

Druid

This real-time analytics database features low latency for data queries. Its benefits include instant visibility, multi-tenant capabilities, and high concurrency. Druid allows multiple users to query data without any performance delays.

It is seen as a high-performance alternative to conventional data warehouses. It is ideal for event-based data. Some of its main features are as follows:

• Faster data searches and filtering through native inverted search indexes
• Flexible schemas featuring native support for nested and semi-structured data
• Time-based data querying and partitioning

Presto

This framework has evolved as a reliable SQL engine for big data analytics. It is capable of querying data wherever it resides, including S3, Hadoop, and other conventional databases. Presto is widely popular with data scientists and analysts.

Its federation capabilities enable you to maintain a single query interface across different data sources. It has become even more powerful with recent enhancements in caching and query optimization.

Some of Presto’s main features include:

• Better memory management
• Advanced cost-based optimizer
• Improved security features
• Native support for different data types

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Conclusion

There is an innate demand for on-demand data. Still, there are many businesses with unstructured or siloed data and poor data governance models. So, it is important to make changes to your data architecture with the key technology shifts taking place in the market. 

If you want to gain a competitive edge while embracing the latest frameworks and models in data architecture, Infojini Consulting can help you make the right technology decisions. Reach out to us to book a consultation today.