Accelerating business innovation with modern data architecture

The era of data explosion is here! As a business, you must be familiar with large volumes of data hitting your organization at a high

February 28, 2023

The era of data explosion is here! As a business, you must be familiar with large volumes of data hitting your organization at a high velocity. The question is – what do you do to derive optimized value from your data? Consider adopting a modern data architecture. Research says that the global data center infrastructure market is expected to reach USD 120 billion by 2030.

This blog shall cover all the crucial aspects around modern data architecture:

 

  • Meaning of modern data architecture
  • Top technologies to enable a modern data architecture
  • Components of modern data architecture
  • Benefits of modern data architecture

 

Read on!

What is modern data architecture?

Simply put, modern data architecture is the is the set of tools and technologies that create, manage, and support data collection, processing, analytical and machine learning workloads. It is cloud-based and provides businesses with a pay-as-you-go and on-demand model.

Key characteristics of a modern data architecture

As modern data architecture is based on cloud computing, it tends to reduce up-front project costs and empowers organizations to scale as per their requirements. One of the best parts about modern data architecture is its capability to automatically analyze and work on current data holistically.

 

Let’s try to understand this with the help of an example. Imagine a data-led company has a data infrastructure set up in place to derive data requirements for marketing purposes. When the company upgrades to modern data architecture, it becomes capable to validate and monitor every aspect of its data.

 

Cloud solution giants like Snowflake, AWS, Azure, and others offer an array of technologies that support modern cloud data lakes and cloud data warehouses.

Evolution of data architecture

In the past – almost around 1980s, organizations typically developed static and IT-driven data architecture called data warehouses. They were less complex and mostly involved structured data. Such data structures generally required a large team of people to look after their maintenance. There were operational databases and and an ETL infrastructure.

Then, with the adoption of big data technologies throughout the mid-2000s, semi structured and unstructured data were added to data architectures. Data lakes were created to store all kinds of structured and unstructured data.

Now enters modern data architecture! It tends to support machine learning and artificial intelligence applications along with basic reporting and business intelligence of data warehouses. The data lakes in modern data architecture tend to provide scalable data storage.

Modern data architecture involves the usage of ELT to process large data sets required for big data analytics. One of the best advantages of ELT is the reduction in load times when compared to the ETL model. It enables businesses to move the entire data set as it exists in the source systems to the target. This means that organizations have the raw data at their disposal in the data warehouse. ELT is a good match for data utilized within cloud environments that involve applications accessed on-demand.

Components of modern data architecture

In a modern data architecture, multiple layers are stacked on top of each other, and each layer tends to have its own function.

1. Data ingestion

 

It is defined as the process of transporting data from various sources like server logs, third-party applications, and others to a single cloud-based storage system. The data is further accessed and analyzed there itself.

 

2. Data storage

 

This component involves a data warehouse or data lake solution that is typically cloud-based. It is leveraged to store all the data collected from the data ingestion tool.

 

3. Data transformation

 

Being one of the key components of modern data architecture, data transformation comprises tools that help transform raw data into user-friendly data models. Data transformation makes it easier for data scientists to query the data and extract actionable insights. It also helps build dashboards and ML models.

 

4. Analytics

 

This component involves data analysis and creation of dashboards for data exploration with the help of modern data analytics tools.

 

5. Data governance

 

It enables organizations to leverage tools that can help them make better sense of their data. The data governance component helps in enhancing data discoverability, quality, and more.

 

6. Data privacy

 

Within this component, organizations can use tools that help them to adhere to data compliance laws and policies. It helps businesses level up their data protection processes.

How modern data architecture is enabling businesses to transform

Let’s look at some of the top advantages of leveraging a modern data architecture:

1. Generate automated insights

 

With the integration AI and machine learning, cloud-based data systems help businesses automatically process huge chunks of data and derive actionable insights at the right time. A modern data architecture involves effective automation that enables alert-based reporting systems. The power of automation helps minimize downtime and enhance customer experiences.

 

2. Enable self-serve

 

Self-serve is one of the biggest benefits of modern data architecture. It helps different teams across the organization to work on a single copy of truth. Modern data architecture helps analysts to work on multi-dimensional data modelling.

 

3. Expand data availability

 

A modern data architecture has a lower storage cost and provides businesses with easy access to unstructured data. This, further, enables organizations to expand the data available. With increased access to real-time data, businesses can manage advanced analytics projects in a more agile way.

 

4. Facilitate better scalability and elasticity

 

Modern data architecture involves the latest configurations that include accessible cloud environments. They tend to push the demand for ever-growing data storage and workloads for machine learning. Also, modern data architecture not only scales on-demand but also tends to roll back resources as and when required. This means that based on compute and storage needs, businesses can scale or shrink.

 

5. Cut down extra expenses

 

Modern data architecture is generally based on the pay as you scale model. As it does not require ETL, modern data architecture has a lower data management overhead. Moreover, it saves businesses from incurring costs on expensive hardware or setting up cluster. Modern data architecture also helps save legacy compute costs.

Wrapping up

There’s no denying that data architecture needs to evolve consistently to keep up with the changing business demands. Modern data architecture tends to enhance data transparency, agility, and democratization. At LumenData, our team has core expertise in cloud, on-premise, and hybrid technologies. Let us connect to discuss how we can drive more innovation for your organization via the implementation of a flexible data architecture.

 

Also, check out our webinar to dig into some interesting facts and figures around the impact of modern data architecture.

Authors

Authors:

Shalu Santvana

Content Crafter

Ankit

Ankit Kumar

Technical Lead

Shalu Santvana

Content Crafter

Ankit

Ankit Kumar

Technical Lead

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