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What is Data Architecture? A Comprehensive Guide to Its Benefits, Types, Components, and More

What is Data Architecture? A Comprehensive Guide to Its Benefits, Types, Components, and More

Updated on 27 January, 2025

7.97K+ views

• 17 min read

Table of Contents

What is Data Architecture? Benefits and Role of Data Architect
Different Types of Data Architecture
Core Components of Data Architecture
Key Frameworks in Data Architecture You Should Know
How to Overcome Common Challenges in Data Architecture?
Top Best Practices for Effective Data Architecture
What Does a Data Architect Do?
How upGrad Can Help You Excel in Data Architecture?

By 2025, the global datasphere will reach 175 zettabytes. Innovations like Lonestar’s lunar data center address these challenges. This highlights the growing importance of understanding what is data architecture and how frameworks like TOGAF can help manage this growth.

This guide explores definitions, components, frameworks, and careers. It connects these advancements to the future of data architecture.

What is Data Architecture? Benefits and Role of Data Architect

Data architecture is like the blueprint for how data flows and is organized in a system. Imagine you’re building a house, before you start, you need a plan to decide where the rooms, windows, and doors will go.

In the same way, data architecture is the plan that shows how data is collected, stored, accessed, and used in a company or system.

It defines the rules and standards for handling data.

This includes

where the data will be stored (databases or cloud storage),
how different pieces of data are connected (like relationships between tables),
and how the data will move between different parts of the system (data pipelines).

Good data architecture ensures that data is easy to find, secure, and reliable, which is important for making decisions and solving problems.

Let’s explore the benefits of modern data architecture in detail:

1. Scalability for Growing Data Needs

India's data volume has been growing at a compound annual growth rate (CAGR) of 54%. This growth increases the need for scalable data architectures to manage storage and processing effectively. Advanced data architectures ensure scalability to manage increasing data volumes effectively.

2. Support for Decentralized Frameworks

Modern architectures like data mesh promote domain-oriented ownership of data. This supports distributed teams and ensures better adaptability for large enterprises.

3. Enhanced Decision-Making
AI-powered architectures integrate real-time analytics. Real-time analytics helps businesses process large datasets faster, enabling quicker and more accurate decisions.
4. Improved Data Quality and Governance
Data architectures implement policies and standards for consistent, high-quality data. High-quality data ensures regulatory compliance and improves operational efficiency across organizations.

5. Increased Flexibility with Cloud Integration
Sovereign cloud initiatives in India reduce reliance on global providers and ensure localized data control, enhancing compliance and operational efficiency.

6. Increased Flexibility with Cloud Integration
Sovereign cloud initiatives in India reduce reliance on global providers. Localized systems improve data control and enhance digital security for businesses. Flexible architectures enable localized data management and therefore enhanced data control.

7. Cost Optimization
Advanced architectures streamline workflows and eliminate redundancy. Efficient workflows in modern data architectures reduce storage costs and optimize expenses for scaling operations.

8. Facilitation of Real-Time Collaboration
Modern systems provide decentralized data access. Decentralized access enables real-time collaboration and efficient data sharing."

9. Future-Proof Infrastructure
It is expected that by 2027, more than 50% of organizations will adopt cloud data warehousing for scalability. Cloud data warehousing with modern data architecture ensures infrastructures remain adaptable to emerging technologies and needs.

10. Global Competitiveness
Projects like OpenAI's $500 billion Stargate initiative highlight global advancements. Modern data architectures equip Indian businesses to stay competitive in the digital economy.

11. Better Resource Utilization
Decentralized frameworks reduce bottlenecks and improve efficiency. Domain ownership improves resource allocation for better operational efficiency.

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There are some characteristics of it that ensure businesses can efficiently adapt to evolving data challenges while maintaining performance, security, and usability. Let’s take a look at it.

Characteristics of Modern Data Architecture

Modern data architecture leverages AI-driven systems, shared data platforms, and elastic resources to address the challenges of growing data complexity. Modern data architecture frameworks emphasize user-driven designs and AI integration, ensuring systems adapt to evolving data needs.

Below are its key characteristics explained with practical depth:

User-Driven
Modern data architecture empowers users by enabling self-service analytics. Tools like Power BI provide drag-and-drop interfaces for creating reports without coding. This reduces reliance on IT teams, speeding up insights and decision-making processes.
Built on Shared Data
Shared data architecture eliminates silos, enabling seamless access for all stakeholders. For instance, Amazon Redshift uses centralized storage, ensuring data consistency and avoiding duplication. Shared datasets also improve collaboration across departments, aligning everyone with a unified source of truth.
Automated
Automation in data architecture reduces manual interventions by automating processes like data ingestion, cleaning, and transformation. Apache NiFi, for example, automates data pipelines by setting triggers and workflows, ensuring tasks are completed consistently and without delays.
AI-Driven
AI integration enables intelligent decision-making and real-time processing. Systems like Snowflake use AI to detect anomalies in data, optimize query performance, and suggest data trends. This ensures businesses extract meaningful insights quickly and stay competitive.
Elastic
Elastic architectures dynamically scale resources based on workloads, ensuring uninterrupted operations during high demand. For instance, Google BigQuery scales up automatically, while scaling down during low usage periods reduces operational costs. This elasticity also helps control costs, as unused resources are scaled down during low usage periods.
Simple
Simplicity is achieved through intuitive designs and minimal configurations. Databricks offers a unified platform for data engineering and machine learning, simplifying complex workflows into user-friendly dashboards. This reduces training time and boosts adoption.
Secure
Security is reinforced through encryption, access controls, and compliance standards. For instance, Azure Data Lake encrypts data both at rest and in transit, while role-based access ensures only authorized users handle sensitive information. Built-in compliance with standards like GDPR further safeguards organizations from legal risks.

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The advanced features of modern data architecture, such as AI integration, scalability, and robust security, drive its efficiency and adaptability. However, implementing these characteristics often depends on the type of data architecture chosen.

Let’s explore the various types of data architecture and their unique applications.

Different Types of Data Architecture

Data architecture evolves to meet diverse business demands, emphasizing scalability and security. Frameworks like Data Mesh and Cloud-Native Architecture enable adaptability.

Here’s a detailed breakdown of key types, their features, and examples integrated into descriptions:

Type	Key Features	Use Cases
Traditional Data Architecture	Centralized and rigid structure. Relies on on-premise databases with limited scalability and flexibility. For example, older ERP systems like SAP ECC used traditional architectures to manage fixed workloads and static data.	Ideal for legacy systems in manufacturing and small-scale businesses with predictable data requirements.
Modern Data Architecture	Highly flexible, scalable, and AI-ready. Integrates cloud and real-time analytics. Platforms like Amazon and Flipkart use modern architectures to offer real-time inventory tracking and personalized recommendations.	Suitable for dynamic industries like e-commerce, healthcare, and logistics requiring agile data systems.
Data Mesh	Decentralized and domain-oriented. Assigns data ownership to individual teams for efficiency. Netflix uses data mesh to manage microservices, ensuring scalability while empowering teams with independent data ownership.	Ideal for large organizations with distributed teams, enabling better collaboration and autonomy.
Data Lakes	Stores raw, unstructured, and semi-structured data in its native format. Spotify processes vast amounts of raw audio and user data in data lakes to drive analytics and recommendation algorithms.	Perfect for industries dealing with high volumes of raw data, like streaming services and IoT platforms.
Data Warehouses	Optimized for structured data and analytics. Requires data transformation during ingestion. Banks use data warehouses to generate quick financial reports, such as dashboards tracking account balances.	Best for structured, query-intensive operations in finance, retail, and supply chain management.
Hybrid Data Architecture	Combines on-premise and cloud systems. Ensures compliance with local regulations while leveraging cloud benefits. Indian banks use hybrid setups during cloud migrations to balance security and regulatory requirements.	Suitable for industries transitioning from legacy systems while adhering to strict compliance norms.
Cloud-Native Data Architecture	Fully cloud-based and scalable. Easily integrates with AI/ML tools for automation and advanced analytics. Snowflake powers AI-driven analytics for companies, offering dynamic scaling based on workloads.	Ideal for startups and enterprises focused on scalability, automation, and diverse data processing needs.

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Each type of data architecture, from traditional to cloud-native systems, is built on foundational components. These include data pipelines, cloud storage, and real-time analytics, which enable efficient operations and scalability.

Let’s explore these core components in detail.

Core Components of Data Architecture

Efficient data architecture relies on core components that streamline data collection, processing, storage, and analysis for seamless operations. Key components, such as data pipelines and APIs, are integral to understanding what data architecture is in practice.

Below is a detailed breakdown of these components:

1. Data Pipelines

Data pipelines manage the flow of data from collection to refinement and storage. They automate tasks such as cleaning, transforming, and aggregating data.

Example: Companies like Uber use Apache Kafka-based pipelines to process millions of real-time ride and driver updates.
Recent Update: DataOps platforms, such as Informatica’s Intelligent Data Pipeline, now integrate AI for predictive pipeline monitoring and optimization.

2. Cloud Storage

Cloud storage provides scalable and secure repositories for data. It allows access to data from anywhere with an internet connection.

Example: Google Cloud Storage is widely used for storing large datasets for analytics and machine learning projects.
Recent Update: The rise of sovereign clouds in India, such as RBI’s proposed cloud services, ensures compliance with local data protection regulations.

3. APIs (Application Programming Interfaces)

APIs enable seamless communication between systems, allowing applications to share data efficiently.

Example: Stripe’s API allows real-time payment processing for e-commerce platforms.
Recent Update: OpenAPI 3.1 introduced improved JSON Schema support, simplifying API integration and enhancing developer productivity.

4. AI & ML Models

AI and ML models automate data processing, improve predictions, and extract insights from vast datasets.

Example: Spotify’s recommendation engine uses ML models to analyze user listening habits and provide personalized suggestions.
Recent Update: Companies like OpenAI integrate fine-tuned AI models within data architectures to enhance natural language processing capabilities.

5. Data Streaming

Data streaming involves processing continuous flows of data in real-time for instant analysis and decision-making.

Example: Netflix uses Apache Flink for real-time streaming to improve user experience by minimizing playback delays.
Recent Update: Confluent’s streaming platform now supports advanced schema registry features, reducing errors during data serialization.

6. Kubernetes

Kubernetes manages the deployment and scaling of containerized applications, including data processing workloads.

Example: Shopify uses Kubernetes to handle traffic spikes during seasonal sales by scaling its applications dynamically.
Recent Update: Kubernetes 1.28 introduced security enhancements, such as network policy support for sandboxed containers, boosting data safety.

7. Cloud Computing

Cloud computing powers the analysis, storage, and management of data without on-premise infrastructure. Multi-cloud setups enhance flexibility and reduce vendor lock-in.

Example: AWS Lambda provides serverless computing to execute code in response to events, reducing operational overhead.
Recent Update: Multi-cloud solutions like Anthos by Google allow businesses to operate seamlessly across multiple cloud platforms.

8. Real-Time Analytics

Real-time analytics processes and analyzes data as it is generated, enabling quick decision-making.

Example: Stock trading platforms use real-time analytics to process and analyze market data instantly for better trades.
Recent Update: Google BigQuery launched real-time anomaly detection for identifying irregular patterns in streaming data.

Also Read: Data Mining Architecture: Components, Types & Techniques

Core components like data pipelines and cloud computing form the backbone of robust data architecture. However, to ensure these components work cohesively, structured frameworks provide essential guidance and standardization.

Let’s examine key frameworks that simplify and optimize data architecture.

Key Frameworks in Data Architecture You Should Know

Data architecture frameworks define clear methods for building systems that ensure data consistency, support compliance, and address evolving business needs effectively. Data architecture frameworks like TOGAF and Zachman simplify creating scalable and compliant systems. They ensure consistency, scalability, and alignment with business goals.

Below are key frameworks with their roles and significance:

Framework	Role in Data Architecture	Use Cases and Updates
DAMA-DMBOK 2	A comprehensive framework by DAMA International for data management. It standardizes terminologies and guides policies for consistent governance.	Used in large organizations to establish uniform data standards across teams. Updated definitions reflect emerging needs like data ethics and AI governance.
Zachman Framework	Developed by John Zachman at IBM, it includes multiple layers for creating data models, databases, and physical structures.	Employed in enterprise-wide projects where alignment of data layers is critical. Widely used in finance for regulatory compliance models.
TOGAF (The Open Group Architecture Framework)	Focuses on enterprise architecture development, with Phase C dedicated to designing data architecture and roadmaps.	Popular in software enterprises for building scalable solutions. Recent updates include cloud-native strategies for faster deployment.
Data Vault 2.0	A methodology for scalable data warehouses. Focuses on historical tracking and provides flexibility for changing business needs.	Ideal for real-time analytics and data integration in industries like healthcare. Incorporates AI for automated ETL processes.
Cloud Adoption Framework (CAF)	A framework designed by cloud providers like AWS and Microsoft Azure to simplify cloud migration and architecture.	Adopted by Indian banks for secure cloud migrations while meeting RBI’s compliance standards. Updated for multi-cloud strategies.

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Frameworks like TOGAF, DAMA-DMBOK 2, and Data Vault 2.0 offer structured approaches for building scalable and efficient data architectures. Yet, implementing these frameworks comes with challenges such as integration complexity, data security concerns, and cost management.

Here’s how organizations can overcome these obstacles and create resilient data systems.

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How to Overcome Common Challenges in Data Architecture?

Integrating data from multiple sources often leads to compatibility issues and delays. Organizations adopting hybrid architectures face challenges syncing on-premise systems with cloud environments. Addressing these obstacles effectively ensures scalability, security, and efficient data operations. Here are the key challenges and strategies to overcome them:

Complexity in Integration
Integrating data from multiple sources often leads to compatibility issues and delays. Organizations adopting hybrid architectures face challenges syncing on-premise systems with cloud environments.
- Solution: Use integration platforms like MuleSoft or Informatica to streamline API connections. Netflix, for instance, uses APIs to ensure seamless data transfer between microservices.
- Update: Multi-cloud integration tools like Google Anthos simplify managing hybrid and multi-cloud systems.
Data Security Concerns
Data breaches and compliance violations remain top concerns, especially with sensitive information. Migrating to cloud systems raises risks without proper security measures.
- Solution: Implement end-to-end encryption, identity access management, and real-time threat detection tools. Azure’s advanced security features ensure compliance with GDPR and RBI standards.
- Update: Kubernetes 1.28 introduced network policy upgrades, improving containerized application security for data-centric organizations.
Data Quality and Governance Issues
Poor-quality data impacts decision-making and leads to inefficiencies. Inconsistent governance across teams results in fragmented systems and errors.
- Solution: Implement frameworks like DAMA-DMBOK 2 for governance and automated tools for quality checks. Snowflake integrates governance features like automated data lineage and version control.
- Update: AI-powered governance tools, such as Informatica CLAIRE, ensure data quality and consistency in real-time.
Cost and Resource Management
Scaling data systems without overspending is challenging. Cloud services often lead to spiraling costs without careful monitoring.
- Solution: Use cloud cost management platforms like AWS Cost Explorer to track and optimize spending. Real-time scaling tools like BigQuery help minimize resource wastage.
- Update: Microsoft Azure introduced pricing calculators for multi-cloud strategies, aiding cost-efficient deployments.
Rapid Technological Changes
New technologies disrupt existing architectures, leading to compatibility and scalability challenges. Adopting AI and ML requires significant infrastructure adjustments.
- Solution: Invest in adaptable frameworks like Data Vault 2.0 and adopt microservices-based architectures.
- Update: OpenAI’s ongoing integration with cloud platforms demonstrates how scalable architectures are vital for supporting large-scale AI models.

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Addressing challenges in data architecture requires leveraging modern frameworks and tools. These frameworks enable robust, efficient, and future-proof systems.

By combining them with best practices, organizations can further enhance adaptability and long-term success.

Top Best Practices for Effective Data Architecture

Netflix’s adoption of a data mesh showcases how following best practices can enable scalability and collaboration for large, distributed teams. Here are more practices to consider:

Promote Collaboration Across Teams
Collaboration between business stakeholders and IT teams is critical. Decision-makers identify essential data, while architects design systems to meet those needs.
- Example: In e-commerce, marketing teams collaborate with IT to ensure customer data is accessible for targeted campaigns.
- Update: Platforms like Jira and Confluence now offer features tailored for cross-department collaboration in real-time data projects.
Prioritize Data Governance
High-quality, well-governed data is crucial for effective decision-making. Implementing governance frameworks ensures compliance, security, and consistency.
- Example: Financial institutions use DAMA-DMBOK 2 to manage sensitive transaction data. Data stewards maintain quality and enforce governance policies.
- Update: Informatica’s CLAIRE now integrates AI to automate data governance tasks like lineage tracking and anomaly detection.
Ensure Scalability and Agility
Data architecture must adapt to new technologies and changing data types. Avoid rigid systems tied to specific platforms or formats.
- Example: Netflix’s transition to data mesh demonstrates how agile architectures enable rapid scalability for distributed teams.
- Update: Google’s BigQuery Omni now supports multi-cloud scaling, ensuring agility across cloud platforms like AWS and Azure.
Embed Security at Every Level
Security protects sensitive data across industries, from healthcare to e-commerce and government. Use encryption, access controls, and compliance frameworks.
- Example: E-commerce platforms secure customer payment data using PCI DSS standards, while government systems enforce encryption for classified information. Azure offers advanced security layers for compliance and protection
- Update: Kubernetes 1.28 enhances container security, making it easier to safeguard data in distributed environments.
Leverage Real-Time Analytics
Organizations need insights from real-time data to stay competitive. Architectures should support streaming data for instant analysis.
- Example: Stock trading platforms use Apache Kafka for streaming analytics to make instant trade decisions.
- Update: Real-time analytics tools, like Snowflake’s Streaming Data Service, simplify implementing streaming pipelines in complex architectures.
Design for Multi-Cloud and Hybrid Environments
Businesses increasingly adopt multi-cloud strategies for flexibility and risk mitigation. Architectures must support seamless integration across platforms.
- Example: Indian banks use hybrid cloud setups to comply with RBI regulations while leveraging cloud scalability.
- Update: Microsoft’s Cloud Adoption Framework now includes guidance for multi-cloud governance and optimization.
Focus on Documentation and Standards
Clear documentation ensures teams understand the data architecture and its workflows. Adopting widely used standards, like ISO/IEC 27001, promotes consistency and security. Following industry standards promotes consistency and simplifies onboarding.
- Example: Retailers document supply chain and customer data flows, ensuring alignment between logistics and analytics teams for better decision-making.
- Update: OpenAI’s standardized API documentation approach has become a benchmark for clarity in complex data systems.

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Adopting best practices ensures data systems are secure, scalable, and well-aligned with business goals. However, designing and maintaining such systems requires specialized expertise. This is where the role of a data architect becomes essential. Let’s dive into what a data architect does and their importance today’.

What Does a Data Architect Do?

Understanding what a data architect does means examining their role in using data architecture frameworks to design and implement scalable solutions. They define strategies, establish standards, and ensure efficient data flow.

As industries move towards AI, IoT, and cloud technologies, the role of data architects has evolved to meet these modern demands.

Below is a table summarizing the top data architecture roles, their salaries, and key responsibilities.

Job Title	Average Annual Salary	Key Responsibilities
Data Architect	₹25.7 L	Designs secure and scalable systems. Ensures alignment with business goals and efficient data storage solutions.
Senior Data Architect	₹30 L	Oversees large-scale architecture projects. Integrates AI/ML solutions and ensures compliance with regulations.
Database Architect	₹21 L	Optimizes database structures for performance. Focuses on indexing, storage, and minimizing query latencies.
Big Data Architect	₹20 L	Designs infrastructures for large-scale data. Works with platforms like Hadoop for processing and analyzing big data.
Cloud Data Architect	₹20 L	Builds and manages cloud-based systems. Optimizes cost and performance for platforms like AWS, Azure, and GCP.

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Data architects play a pivotal role in creating scalable and efficient systems that align technical solutions with business needs. Mastering these skills requires hands-on training and a strong foundation in modern tools and frameworks.

upGrad offers tailored programs to help you excel in this growing field. Let’s learn more about them.

How upGrad Can Help You Excel in Data Architecture?

To succeed as a Data Architect, master data modeling, cloud computing, and governance. upGrad’s courses teach frameworks like TOGAF through real-world projects. Gain skills with hands-on learning and expert guidance to advance in this growing field. Build confidence to tackle complex data challenges effectively.

Join over 1 million learners worldwide and access programs designed to boost your career as a data architecture professional.

Here are some relevant courses to consider:

Get personalized career counseling to chart your ideal learning path. You can also visit your nearest upGrad center for hands-on training and guidance.

Take the next step toward mastering data architecture with upGrad today!

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Frequently Asked Questions (FAQs)

1. What is the explanation of data architecture?

Data architecture is a framework for managing and organizing data assets to ensure alignment with business goals and efficient data operations.

2. Why is data architecture important?

It improves scalability, ensures data quality, enhances efficiency, and helps organizations comply with regulatory requirements.

3. What does a data architect do?

A data architect designs data models, ensures data quality, integrates data sources, and collaborates with stakeholders to implement effective solutions.

4. What are the key benefits of modern data architecture?

Modern data architecture offers scalability, real-time analytics, cost optimization, and improved collaboration across teams.

5. What are the top frameworks in data architecture?

Popular frameworks include DAMA-DMBOK 2, Zachman Framework, TOGAF, Data Vault 2.0, and Cloud Adoption Framework (CAF).

6. How does cloud computing fit into data architecture?

Cloud computing provides scalable, secure storage and processing, enabling organizations to handle large datasets efficiently and cost-effectively.

7. What are some challenges in building data architecture?

Challenges include integration complexity, data security concerns, maintaining data quality, cost management, and adapting to rapid technological changes.

8. What are common types of data architecture?

Types include traditional, modern, data mesh, data lakes, data warehouses, hybrid, and cloud-native architectures.

9. How is data architecture evolving with AI?

AI-driven data architectures enable real-time analytics, anomaly detection, and automation, enhancing decision-making and operational efficiency.

10. What skills are required to excel in data architecture?

Skills include data modeling, cloud computing, data governance, real-time analytics, and proficiency in tools like Apache Kafka and Snowflake.

11. How can upGrad help you in data architecture?

upGrad offers specialized courses with practical training, expert guidance, and hands-on projects to build your skills in data architecture.

Reference Links:

https://www.seagate.com/files/www-content/our-story/trends/files/Seagate-WP-DataAge2025-March-2017.pdf
https://www.prnewswire.com/news-releases/lonestar-successfully-tests-data-center-concept-en-route-to-the-moon-302065399.html
https://www.thehindu.com/sci-tech/technology/india-cenbank-plans-2025-launch-of-cloud-services-countering-dominance-of-global-firms/article68881446.ece
https://pixelplex.io/blog/ai-statistics/
https://www.investindia.gov.in/blogs/indias-internet-surge-catalyzing-change-telecom-landscape
https://cloud.google.com/blog/transform/top-cloud-computing-trends-facts-statistics-2023
https://openai.com/index/announcing-the-stargate-project/
https://paytm.com/blog/loan/repo-rate/rbi-local-cloud-storage-pilot-2025/
https://pixelplex.io/blog/ai-statistics/
https://www.glassdoor.co.in/Salaries/data-architect-salary-SRCH_KO0,14.htm
https://www.glassdoor.co.in/Salaries/senior-data-architect-salary-SRCH_KO0,21.htm
https://www.glassdoor.co.in/Salaries/database-architect-salary-SRCH_KO0,18.htm
https://www.glassdoor.co.in/Salaries/big-data-architect-salary-SRCH_KO0,18.htm
https://www.glassdoor.co.in/Salaries/cloud-data-architect-salary-SRCH_KO0,20.htm
https://www.uber.com/en-IN/blog/kafka-tiered-storage/
https://www.databricks.com/blog/intelligent-data-engineering-enterprise-ai-databricks-and-informatica
https://www.openapis.org/blog/2021/02/18/openapi-specification-3-1-released
https://medium.com/@rohitlokwani17/how-spotify-uses-ai-to-make-every-playlist-feel-personal-c6b4774c9406
https://netflixtechblog.com/streaming-sql-in-data-mesh-0d83f5a00d08
https://blog.google/products/google-cloud/kubernetes-engineers-keep-your-favorite-software-running/
https://kubernetes.io/blog/2023/08/15/kubernetes-v1-28-release/
https://aws.amazon.com/lambda/features/
https://cloud.google.com/learn/what-is-multicloud
https://cloud.google.com/bigquery/docs/anomaly-detection-overview