TOP Trends in Big Data in 2025
- marianfaryna
- 5 days ago
- 5 min read
Author: Iurii (Yuri) Iurchenko
The field of data analytics and big data continues to grow at an impressive pace. 2024 closed with major milestones: the launch of Microsoft Fabric’s Data-as-a-Platform, a 60% surge in Databricks revenue, a wave of mergers and strategic partnerships, and a sharp rise in real-world applications of generative AI.
Market changes directions with the speed of light. And now, even for professionals with a profound background, it is sometimes hard to understand what is happening and what the next step will be. To structure and simplify things, let us begin with the Data Governance wheel from DAMA. That wheel will be a navigating compass for us to dive deeper into the trends, without losing our minds.
Also, as we move into the second quarter of 2025, several additional strong forces are shaping the Big Data landscape. Let's observe it together. But first, let’s look at the key forces driving them:
Force #1: AI
AI continues to lead the charge—from predictive analytics to intelligent automation—reshaping how we store, process, and interpret data.
Force #2: Profit
In volatile markets, companies that leverage dynamic data platforms to adapt quickly are gaining a clear competitive edge. Time-to-market has become a critical factor in every production cycle.
Force #3: Basic Engineering Foundations
Here, the DAMA data governance wheel comes into the game. Information security, high-quality data, and reliable data platforms remain foundational to every data-driven business.
Taking into consideration the forces above, let us distill the key trends in the Big Data field.
Trend #1: Seamless AI Integration
More and more data platforms are integrating AI into their ecosystems. Microsoft, for example, offers Copilot within Azure Fabric, along with seamless integration with Azure AI services - including LLMs, image processing, and prebuilt models for healthcare and other industries. Databricks provides Mosaic AI, a powerful tool for building customized large language models. Snowflake offers a similar solution with Snowflake Cortex. Other vendors are also actively moving in the same direction.
The goal behind these efforts is clear: explore AI capabilities at the early stages of their maturity, identify value opportunities, and eventually monetize them.
What can consumers do? The same - understand their most critical needs and smartly leverage AI capabilities to meet them.
Trend #2: Data as a Service (DaaS)
A growing number of companies are choosing to outsource their data needs by leveraging native cloud data platforms. Why is this a smart move?
Let’s put it simply:
If you invest X thousand dollars in a data platform and generate a 2X, 5X, or even 1.5X return, wouldn’t that be worth exploring?
Data as a Service (DaaS) offers three major benefits:
Easy Start - no need to invest in IoT infrastructure, configure dozens of services, or handle complex integrations. All you need is a clear business goal, an understanding of your challenges, and a focused data strategy. The platform handles the rest.
Seamless Scalability - once you’ve successfully implemented a single data pipeline with solid ROI and fast time-to-market, you can scale confidently. That’s how companies stay competitive in a rapidly evolving market.
Rapid Delivery - modern data platforms come with a comprehensive toolbox - integrations, automations, and AI capabilities built-in. Compared to traditional on-premise solutions, which often spend 20–30% of implementation time on configuration and integration (plus another 10–15% on tool selection), DaaS helps businesses save time, effort, and cost, especially in early project stages.
And yes, all the key players - Microsoft Fabric, Databricks, Snowflake, and others - are already operating in this space.
Trend #3: Business Domain Expertise as a Differentiator
Many tech professionals today - data engineers, analytics engineers, business analysts - are worried about their future. And rightly so. But the more important question is: how can we mitigate this risk and operate effectively at the C-level in such uncertainty? One potential answer is to invest in business expertise. Soon, many technical tasks will be easily handled by machines. For IT professionals at all levels, developing domain knowledge is no longer optional - it’s essential.
For example, if you’re a data engineer working in healthcare, it’s not enough to understand tables and schemas. You should also grasp how cash flow is generated, what drives patient satisfaction and good health, and how your work contributes to the business outcomes. With this understanding, engineers or analysts can actively challenge decisions, shape strategy, and improve data platforms in real time, and be on the same page with their colleagues.
Terms like TTM (Time to Market), ROI, and industry-specific language are becoming powerful differentiators. Start investing your professional energy in business literacy - not tomorrow, but today.
Trend #4: Reinforced Information Security
Almost everyone has heard of GDPR, HIPAA, PHI, and PII. For those unfamiliar, these are key standards governing information security and privacy in the U.S., Europe, and globally.
Now, imagine you’re developing a simple application where users log in and provide passwords. If you fail to follow these standards, personal data could be compromised, leading to serious legal and financial consequences. That’s why data security and privacy have become critical areas of focus for all data professionals.
Surprisingly, many data engineers still lack the knowledge to build secure data platforms, such as identifying sensitive data, applying data masking, or using surrogate identifiers to protect user information.
Raising awareness in this area makes professionals more valuable, and it helps businesses avoid costly breaches and comply with regulations. For tech professionals, this is a chance to stand out. For businesses, it’s an opportunity to protect trust and save millions.
Trend #5: Resilient and Scalable Core Pipelines
Let’s look at the opposite side of the hype surrounding new technologies. Often, when organizations face budget constraints or aim to maximize ROI, they begin by building core data awareness. Instead of chasing trends, they focus on the most critical business processes and build data solutions specifically around them. This approach saves money, simplifies implementation, and delivers near-immediate results.
In today’s dynamic market, where uncertainty and complexity are constant, this is quickly becoming a smart trend.
Action point for C-level leaders: Focus on what brings the highest value or, conversely, what can be significantly optimized in terms of cost. Start there.
Action point for data professionals: Ask yourself a similar question, aligned with your scope of responsibility. What area can you improve or simplify to deliver business impact efficiently?
Conclusion
To summarize, we’ve explored the five most influential trends in the data field, at least from my perspective. These trends impact professionals at all levels involved in data-driven decision-making: from leaders to technical experts, including business analysts, data engineers, analytics engineers, and VPs of Data.
What’s inspiring is this: the (Big) Data space is still very much in the game, just with slightly updated rules, and a very bright future. Stay in tune with us.
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