XenonStack Recommends

Enterprise Data Management

What is Modern Data Infrastructure ? | A Brief Study

Chandan Gaur | 15 Apr 2023

Emerging Modern Data Infrastructure | A Brief Study

What is Data Infrastructure?

An implementation that defines a path to use the collected data and deliver it in a maintainable body is called a Data infrastructure. The data needs are increasing daily, and every business will get empowered with a data infrastructure setup. For example, a data-led company may use the data to help boost the marketing campaign and build a better product. But for this much information, that company needs Data infrastructure.

Modern Data Infrastructure

The need for data infrastructure leads to problems, such as running scalable data pipelines on scalable machines, and when the scalable infrastructure is defined, then there is a requirement to automate and validate.

Automation and Validation are the main pillars of Modern data infrastructure. There is no infrastructure where you can not trust your data inputs without Validation and automation. The distributed ecosystem and programs came into the picture with heavy data usage, emphasizing pipeline automation and data validation through monitoring ecosystems. These monitoring systems are also one of the essential high-level implementations known as instrumentation.

Modern Data Infrastructure Example

If a Data-let company has a data infrastructure setup to derive the data needs for marketing, they may not automatically make decisions. If the same company upgrades its ecosystems to modern data infrastructure, they validate and monitor every aspect of the data. That company/industry will be able to lead the marketing campaign based on better Data Visualization.

Key Components for Modern Data Infrastructure

Data infrastructure comprises analytical and operational systems to make better decisions and build data-powered products. The data infrastructure market has seen tremendous growth in the past 5-6 years, where they are spending billions of dollars to monitor and scale the solutions. This has led to a shift in many basic implementations of data infrastructure to make it a modern data infrastructure.

These are key components of modern data infrastructure:

Data agnostic architecture and its infrastructure

Data architecture can and should provide a mechanism to manage data across many platforms and infrastructures simultaneously, regardless of the type of data. This comprises on-premises high-performance computing that can migrate to the cloud or hybrid cloud architectures or platforms.

Parallel, Distributed Processing

High-performance computing necessitates high-performance data throughput. Life sciences, genome sequencing, data modeling, and artificial intelligence/machine learning workloads all require a large amount of data and a rapid, reliable mechanism to access and interpret it. Modern designs must implement fast technologies to facilitate parallel processing across the infrastructure.


Scalability is a direct answer to the limits of a typical systems approach to data architecture elements like data lakes, data stores, and databases and is perhaps the most significant component here. The need for ever-increasing data storage and workloads for machine learning and life science applications is being pushed by new configurations with fast and accessible cloud environments and on-prem private clouds.

Open Data Access

Aside from compliance and security requirements, employees, researchers, and engineers should be able to access vital data on a regular basis without having to worry about role ownership.

Characteristics of Modern Data Infrastructure

The main characteristics of modern data infrastructure are mentioned below:


Modern architectures are just too large for direct administration to be effective. To ensure system integrity at scale, automation is required in the data structure, relational schemas, predictive analytics, and so on.


A data architecture must never sacrifice speed in the face of parallel processing, improved NVMe-native connectivity, and widespread public or private clouds.


One thing is scalability. On the other hand, modern data architecture necessitates the capacity to scale up or down on demand and the ability to roll back resources as necessary. Managing high-performance machine learning workloads, for example, may necessitate quickly growing computing resources to satisfy short-term demand. System elasticity indicates that depending on your computing and storage requirements, you should be able to scale up or down based on your requirements rather than the architecture's restrictions.


Intelligent systems powered by AI and machine learning are increasingly becoming the backbone of new data infrastructures alongside automation. With real-time insights and digital twin models, AI can assist operators in making better decisions and enable more effective and efficient automation.


This trait isn't as technical as the others, but it's still significant. Data architectures necessitate well-thought-out and well-executed data governance, which addresses how and by whom data is accessible for what objectives.


Your engineers and workers should be able to access data regardless of the platform or system it is stored on, and they should be able to do it in the same way, no matter where they are.

Benefits of Modern Data Infrastructure

These are the benefits of modern data infrastructure:

Cloud Data Warehouse

On-premise data warehouses have problems when there is a need to scale the infrastructure and make it more flexible. A cloud data warehouse is now given preference over an on-premise data warehouse. These systems come with flexibility, scalability, and manageability. Fully managed cloud data warehouses remove overhead to scale and manage demand and supply. Suppose an organization wants to scale the infrastructure to manage the demands and supply, but they don’t have enough on-prem resources available. In this case, the best scenario is to have the infrastructure deployed fully managed, which can be achieved through cloud data warehouse migration.

Next-Generation Data Lakes

Hadoop systems are now overtaken by extended data lakes, which provide more serverless computing and warehousing. These include relational databases and interactive query solutions. Cloud service providers provide fully managed services with IAM roles control, and users will have to pay for storage and Pay as they use service patterns. Having such accessibility and control over the Data Lakes makes Next-Generation Data Lakes the best solution to look for. 

Earlier, users were stuck managing access to Hadoop services if no Skill Set was available. But with Next Generation data lakes deployed on a serverless warehouse, it is now easy to manage the access, and cloud providers help set up the same.


ELT is now considered more consistent and reliable than ETL with modern data lakes and its automation. The reason observed can be any of the following:

  1. Loading data is faster due to the cost of in-memory shuffling

  2. Raw data time travel feature engineering

  3. Eliminating Storage and computing in the same place

  4. Storing the data in Staging Tables and then transforming it into final tables as per the requirement

  5. Storing Real-time data is a priority, and Batch processing can help to transform data.

Organizations following the ETL approach faced a challenge in debugging and navigating data flow because engineers had to follow a path to achieve the ETL. This is then identified and found that ELT can help navigate and find the data path [Data Lineage] to debug and reproduce the stability.

Dataflow Automation

From designing to reporting, the data flow automation helps to capture, build and collaborate on the scale to identify and increase the efficiency of systems with more optimized and controllable reporting (sometimes called self-served reports). Data flow automation delivers daily alerts such as what went wrong, the optimization possibilities, etc.

Automation serves organizations to design business processes effectively as they have more engineering and collaboration capabilities to administer and identify the scope of improvements.

Automated Insights

Automation through superset and looker-type solutions can serve the insights in an alert-based reporting system where conclusions and recommendations can be made without delays. This helps in better key takeaways. Many tools can now create dashboards and display recommendations about design and engineering. 

Suppose a Monitoring dashboard is set up, but it provides you insights only when you try to access it. But what if a monitoring dashboard is set up and provides alerts through emails and other communication media about takeaways? Wouldn’t it be helpful? Sure, it is because users don’t have to worry about accessing the dashboards and identifying the key takeouts.

Data Governance

Data Governance is the most important aspect of Emerging Data Infrastructure. Data Governance helps in tracing the standards, regulations, and rules and, at the same time, making sure that all the requirements are fulfilled and transparency is maintained. This whole scenario helped to inhale and exhale better compliance. 

Governance is helpful for organizations dealing with data that have laws and regulations applied to it. Data Governance also provides the capability to define the Data lineage, Data Rules, and so on, all under one roof.