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The emerging trends in modern data infrastructure

Like any other domain or industry, 2021 propelled the data world into a full-throttle fast-forward. COVID forced businesses to rethink their operational models and quickly adapt to the new normal.

With everyone accessing data from different locations and systems, the technologies that make up the data infrastructure became a big priority

As more businesses moved to work remotely, storing data in the cloud became an absolute necessity. With everyone accessing data from different locations and systems, the technologies that make up the data infrastructure, including cloud, analytics, AI/ML, not sparing data governance and security, became a big priority. More intelligent applications and products powered by AI became appealing now that historical models were meaningless. In short, organizations realized they needed to build better and more efficient data systems.

Data investments went up dramatically and organizations sought to upgrade their systems

Enterprises are now building software applications to manage data rather than the other way around where  the primary business value comes from data analysis rather than the software itself. These systems enable data-driven decision-making and drive data-powered products, including machine learning.

We’re now beginning to see the rise of massive, complex infrastructure built around data resulting in a fast-moving trend across the industry, including the emergence of new roles, new startups providing infrastructure, and tooling around data.

In fact, data investments went up dramatically and organizations sought to upgrade their systems to create the perfect data stack.

With 2021 in hindsight, we’re now looking ahead to a new and hopefully better year. What will 2022 bring to the data world? How will data infrastructure evolve to keep up with the latest innovations and changes?

This year, we’ll see several new data trends, including:

  • The emergence of new data roles and data quality frameworks
  • The rise of the modern data stack and modern metadata solutions
  • The convergence of data lakes and warehouses

Yet, despite all of this energy and momentum, we see tremendous confusion around what technologies are on the leading edge of this trend and how they are used in practice. This blog post will share the most prominent data infrastructure trends in 2022 and the future.

# 1 Enormous growth in the data infrastructure industry

Data infrastructure has undergone massive growth over the last few years. Gartner predicted global data center infrastructure to reach $200 billion in 2021, expected to grow year-over-year through 2024.

We also see the race towards data in the job market – Data analysts, data engineers, and machine learning engineers roles are fastest-growing. Sixty percent of the Fortune 1000 employ Chief Data Officers, according to NewVantage Partners, and these companies significantly outshine their peers in McKinsey’s growth and profitability studies.

Most importantly, data and data systems are contributing directly to business results in Silicon Valley tech companies across every industry, even traditional ones – be it Amazon, Netflix, Airbnb, or McDonald’s. The need for efficient data infrastructure can be seen everywhere.

# 2 A unified and cohesive data infrastructure 

Due to the high growth rate of the data infrastructure market, the tools and best practices for data infrastructure are also evolving incredibly quickly. So much that it gets challenging to get a cohesive view of how all the pieces fit together.

A unified data infrastructure architecture is a vision every leading data organizations seek, and they attempt to build their internal technology stacks around this vision of a unified architecture that supports all use cases. These use cases cover and extend from data sources, ingestion, transformation, storage, historical, and predictive to output – right from query to processing.

# 3 Data analytics, AI/machine learning converge 

As mentioned at the start of this article, data infrastructure serves two essential purposes.

  • First, to provide the vital information that helps managers and business leaders make better decisions with the help of analytic systems or use cases.
  • Second, to build data intelligence, leveraging AI and machine learning, into customer-facing applications such as operating systems or use cases.

Two parallel architectures have grown around these above two use cases – It’s the convergence of data warehouse and data lake.

  • The data warehouse forms the basis for the data analytics use case because most warehouses store data in a structured format. These systems are designed to generate insights from core business metrics quickly.
  • On the other hand, the data lake supports the operational use case. Storing data in its raw format allows data applications utilizing AI and machine learning to interpret the data at scale and provide meaningful information from this data. This aptly suits bespoke applications with more advanced data processing needs. Data lakes operate on a wide range of languages, including Java, Python, R, and SQL.

The interesting trend here is that modern data warehouses and data lakes are beginning to resemble one another after a long battle.

As we advance, this trend would be interesting to watch whether data warehouses and data lakes would converge to form a standard stack. While we’re not there yet, some experts believe this is happening. While others believe parallel ecosystems will persist due to differences in use cases, languages, or other factors.

# 4 Broad architectural shifts 

A series of broad architectural shifts are occurring in data infrastructure across the software industry, including the move to cloud, open-source, and SaaS business models. Several shifts are unique to data infrastructure, driving the architecture forward.


As stated at the beginning of this discussion, modern business organizations consider data a precious currency and prized commodity. However, without analysis and interpretation, data on its own has no value. As a result, the necessity to analyze and decode enormous data volumes and turn them into practical and valuable information drives the latest trends in data infrastructure.

In short, data infrastructure has to support the storage and analysis of structured and unstructured data in data lakes, data warehouses, or a combination of both. As the data volumes rise, different data types needing analysis increase. The data architecture and the analysis tools must evolve alongside the immediate rise in data volumes.

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