2025 DataOps Predictions - Part 3

As part of APMdigest's 2025 Predictions Series, industry experts offer predictions on how DataOps and related technologies will evolve and impact business in 2025. Part 3 covers data technologies.

CONSOLIDATION OF DATA INFRASTRUCTURE

The Great Infrastructure Consolidation Will Accelerate: In 2025, we'll witness a significant consolidation of data infrastructure driven by economic pressures and the maturation of AI technologies. Organizations will move away from maintaining separate systems for streaming, batch processing, and AI workloads. Instead, they'll gravitate toward unified platforms that can handle multiple workloads efficiently. This shift isn't just about cost savings — it's about creating a more cohesive data ecosystem where real-time streaming, lakehouse storage, and AI processing work in harmony.
Sijie Guo
Founder and CEO, StreamNative

CONSOLIDATION OF DATA ASSETS

In 2025, organizations will focus on consolidating their data assets to build a unified foundation that powers future innovation, insights, and decision-making.
Ram Palaniappan
CTO, TEKsystems Global Services

CONTEXTUALIZING DATA

Contextualizing data will be the next frontier for data platforms. The evolution of the data platform is essential to the evolution of AI. Next year, we'll see breakthroughs that help LLMs better understand the data they're working with through the semantic layer. Today's data platforms are largely missing the semantic layer of data, which is the understanding of what the data means. For instance, when you have financial data in a table, it's typically the developer or the analyst who is tasked with understanding where that data came from, how it was calculated, and what it means — but this understanding should be baked directly into the data platforms. Having to rely on these additional stakeholders and build that understanding into every application you develop on top of your data is extremely burdensome. As a result, the semantic layer must be pushed down close to the data so that AI can understand the nature of it, and do a much better job at analyzing it. Users don't want to, and shouldn't have to, reinvent the semantic concepts for each application. They must push down to the data layer, that's the next evolution. 
Benoit Dageville
President and Co-Founder, Snowflake

SMART INFRASTRUCTURE

We'll see the emergence of "smart infrastructure" platforms that automatically optimize resource allocation and data movement based on workload patterns and cost constraints.
Sijie Guo
Founder and CEO, StreamNative

DATA LAKEHOUSE

The Data Lakehouse Becomes the Standard for Analytics: More companies are moving from traditional data warehouses to data lakehouses. By 2025, over half of all analytics workloads are expected to run on lakehouse architectures, driven by the cost savings and flexibility they offer. Currently, companies are shifting from cloud data warehouses to lakehouses, not just to save money but to simplify data access patterns and reduce the need for duplicate data storage. Large organizations have reported savings of over 50%, a major win for those with significant data processing needs.
Emmanuel Darras
CEO and Co-Founder, Kestra

HYBRID LAKEHOUSE

The Rise of the Hybrid Lakehouse: The resurgence of on-prem data architectures will see lakehouses expanding into hybrid environments, merging cloud and on-premises data storage seamlessly. The hybrid lakehouse model offers scalability of cloud storage and secure control of on-premises, delivering flexibility and scalability within a unified, accessible framework.
Justin Borgman
Co-Founder and CEO, Starburst

SQL RETURNS TO THE DATA LAKE

SQL is experiencing a comeback in the data lake as table formats like Apache Iceberg simplify data access, enabling SQL engines to outpace Spark. SQL's renewed popularity democratizes data across organizations, fostering data-driven decision-making and expanding data literacy across teams. SQL's accessibility will make data insights widely available, supporting data empowerment.
Justin Borgman
Co-Founder and CEO, Starburst

OPEN TABLE FORMATS

Open table formats, particularly Apache Iceberg, are quickly gaining popularity. Iceberg's flexibility and compatibility with various data processing engines make it a preferred choice. Iceberg provides a standardized table format and integrates it with SQL engines as well as with data platforms, enabling SQL queries to run efficiently on both data lakes and data warehouses. Relying on open table formats allows companies to manage and query large datasets without relying solely on traditional data warehouses. With organizations planning to adopt Iceberg over other formats, its role in big data management is expected to expand, thanks to its strong focus on vendor-agnostic data access patterns, schema evolution, and interoperability.
Emmanuel Darras
CEO and Co-Founder, Kestra

POSTGRESQL: EVERYTHING DATABASE

In 2025, PostgreSQL will solidify its position as the go-to "Everything Database" — the first to fully integrate AI functionality like embeddings directly within its core ecosystem. This will streamline data workflows, eliminate the need for external processing tools, and enable businesses to manage complex data types in one place. With its unique extension capabilities, PostgreSQL is leading the charge toward a future where companies no longer have to rely on standalone or specialized databases.
Avthar Sewrathan
AI Product Lead, Timescale

DATA MESH

Data Mesh Gains Momentum Across Organizations: Data mesh is now more than an IT-driven strategy. It's increasingly led by business units themselves, with data mesh initiatives coming from non-IT teams focused on improving data quality and governance. Data mesh promotes decentralized data ownership, enabling business units to manage their data independently. This setup brings faster decision-making, more agility, and better data access. 
Emmanuel Darras
CEO and Co-Founder, Kestra

DATA FABRIC

A data fabric will become accepted as a pre-cursor to using AI at scale: As businesses increasingly adopt AI to drive innovation, one key challenge remains: ensuring that AI is reliable, responsible, and relevant. AI solutions must be trained on real, company-specific data — not synthetic or generalized data — to deliver accurate, actionable insights. To make this a reality, more and more organizations will adopt a data fabric as their data strategy as it provides the semantics and rich business context that AI requires to be used in real business cases. 
Daniel Yu
SVP, SAP Data and Analytics

MULTI-CLOUD NETWORKING

Enhanced Multi-Cloud Networking for Regulatory Compliance: By 2025, companies will increasingly rely on multi-cloud networking solutions, a capability required to meet diverse data sovereignty and industry-specific regulatory requirements. These advanced solutions will enable seamless connectivity and secure data transfer across cloud environments through robust encryption and access controls and they must also possess the critical ability to identify and remediate risks, threats, and vulnerabilities. CIOs and network architects will prioritize network designs that facilitate secure, efficient data flows, actively minimize regulatory risk, and maintain data integrity across cloud platforms.
Ali Shaikh
Chief Product Officer and Chief Operating Officer, Graphiant

STREAMING-FIRST APPROACH

Streaming-first approach grows with AI: Pressure will grow for more AI and applications to respond to real-time information to drive automation and meet the expectations of consumers. Organizations will adopt a "streaming-first" approach when architecting new applications.  These applications are Event-Driven and will replace traditional application architectures that process data at rest and to a large extent involve request/response communication. This will also facilitate more data sharing of real-time data between totally different domains of a business than previously.
Guillaume Aymé
CEO, Lenses.io

STREAMING DATA PLATFORMS: OBSERVABILITY AND SECURITY

In 2025, streaming data platforms will become indispensable for managing the exponential growth of observability and security data. Organizations will increasingly adopt streaming data platforms to process vast volumes of logs, metrics, and events in real-time, enabling faster threat detection, anomaly resolution, and system optimization to meet the demands of ever-evolving infrastructure and cyber threats.
Bipin Singh
Senior Director of Product Marketing, Redpanda

STREAMING DATA PLATFORMS: AI

In 2025, streaming data platforms will serve as the backbone for agentic AI, RAG AI and sovereign AI applications, providing the low-latency, high-throughput capabilities required to power autonomous decision-making systems and ensuring compliance with data sovereignty requirements.
Bipin Singh
Senior Director of Product Marketing, Redpanda

REAL-TIME DATA STREAMING FABRIC

Businesses will look to accelerate hyper-connecting applications and architectures across all parts of their business through real-time data streams. This "streaming fabric" across a business will blur the lines between previously isolated different AI, analytics and software architectures and allow connecting systems across business lines such as finance, ecommerce, manufacturing, distribution, supply chain. This connectivity will allow applications to be built that offer new digital consumer-facing services as well as ones that provide new levels of automation within a business.
Guillaume Aymé
CEO, Lenses.io

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Optimizing Decisions with Edge-First Cognitive Intelligence

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2025 DataOps Predictions - Part 3

As part of APMdigest's 2025 Predictions Series, industry experts offer predictions on how DataOps and related technologies will evolve and impact business in 2025. Part 3 covers data technologies.

CONSOLIDATION OF DATA INFRASTRUCTURE

The Great Infrastructure Consolidation Will Accelerate: In 2025, we'll witness a significant consolidation of data infrastructure driven by economic pressures and the maturation of AI technologies. Organizations will move away from maintaining separate systems for streaming, batch processing, and AI workloads. Instead, they'll gravitate toward unified platforms that can handle multiple workloads efficiently. This shift isn't just about cost savings — it's about creating a more cohesive data ecosystem where real-time streaming, lakehouse storage, and AI processing work in harmony.
Sijie Guo
Founder and CEO, StreamNative

CONSOLIDATION OF DATA ASSETS

In 2025, organizations will focus on consolidating their data assets to build a unified foundation that powers future innovation, insights, and decision-making.
Ram Palaniappan
CTO, TEKsystems Global Services

CONTEXTUALIZING DATA

Contextualizing data will be the next frontier for data platforms. The evolution of the data platform is essential to the evolution of AI. Next year, we'll see breakthroughs that help LLMs better understand the data they're working with through the semantic layer. Today's data platforms are largely missing the semantic layer of data, which is the understanding of what the data means. For instance, when you have financial data in a table, it's typically the developer or the analyst who is tasked with understanding where that data came from, how it was calculated, and what it means — but this understanding should be baked directly into the data platforms. Having to rely on these additional stakeholders and build that understanding into every application you develop on top of your data is extremely burdensome. As a result, the semantic layer must be pushed down close to the data so that AI can understand the nature of it, and do a much better job at analyzing it. Users don't want to, and shouldn't have to, reinvent the semantic concepts for each application. They must push down to the data layer, that's the next evolution. 
Benoit Dageville
President and Co-Founder, Snowflake

SMART INFRASTRUCTURE

We'll see the emergence of "smart infrastructure" platforms that automatically optimize resource allocation and data movement based on workload patterns and cost constraints.
Sijie Guo
Founder and CEO, StreamNative

DATA LAKEHOUSE

The Data Lakehouse Becomes the Standard for Analytics: More companies are moving from traditional data warehouses to data lakehouses. By 2025, over half of all analytics workloads are expected to run on lakehouse architectures, driven by the cost savings and flexibility they offer. Currently, companies are shifting from cloud data warehouses to lakehouses, not just to save money but to simplify data access patterns and reduce the need for duplicate data storage. Large organizations have reported savings of over 50%, a major win for those with significant data processing needs.
Emmanuel Darras
CEO and Co-Founder, Kestra

HYBRID LAKEHOUSE

The Rise of the Hybrid Lakehouse: The resurgence of on-prem data architectures will see lakehouses expanding into hybrid environments, merging cloud and on-premises data storage seamlessly. The hybrid lakehouse model offers scalability of cloud storage and secure control of on-premises, delivering flexibility and scalability within a unified, accessible framework.
Justin Borgman
Co-Founder and CEO, Starburst

SQL RETURNS TO THE DATA LAKE

SQL is experiencing a comeback in the data lake as table formats like Apache Iceberg simplify data access, enabling SQL engines to outpace Spark. SQL's renewed popularity democratizes data across organizations, fostering data-driven decision-making and expanding data literacy across teams. SQL's accessibility will make data insights widely available, supporting data empowerment.
Justin Borgman
Co-Founder and CEO, Starburst

OPEN TABLE FORMATS

Open table formats, particularly Apache Iceberg, are quickly gaining popularity. Iceberg's flexibility and compatibility with various data processing engines make it a preferred choice. Iceberg provides a standardized table format and integrates it with SQL engines as well as with data platforms, enabling SQL queries to run efficiently on both data lakes and data warehouses. Relying on open table formats allows companies to manage and query large datasets without relying solely on traditional data warehouses. With organizations planning to adopt Iceberg over other formats, its role in big data management is expected to expand, thanks to its strong focus on vendor-agnostic data access patterns, schema evolution, and interoperability.
Emmanuel Darras
CEO and Co-Founder, Kestra

POSTGRESQL: EVERYTHING DATABASE

In 2025, PostgreSQL will solidify its position as the go-to "Everything Database" — the first to fully integrate AI functionality like embeddings directly within its core ecosystem. This will streamline data workflows, eliminate the need for external processing tools, and enable businesses to manage complex data types in one place. With its unique extension capabilities, PostgreSQL is leading the charge toward a future where companies no longer have to rely on standalone or specialized databases.
Avthar Sewrathan
AI Product Lead, Timescale

DATA MESH

Data Mesh Gains Momentum Across Organizations: Data mesh is now more than an IT-driven strategy. It's increasingly led by business units themselves, with data mesh initiatives coming from non-IT teams focused on improving data quality and governance. Data mesh promotes decentralized data ownership, enabling business units to manage their data independently. This setup brings faster decision-making, more agility, and better data access. 
Emmanuel Darras
CEO and Co-Founder, Kestra

DATA FABRIC

A data fabric will become accepted as a pre-cursor to using AI at scale: As businesses increasingly adopt AI to drive innovation, one key challenge remains: ensuring that AI is reliable, responsible, and relevant. AI solutions must be trained on real, company-specific data — not synthetic or generalized data — to deliver accurate, actionable insights. To make this a reality, more and more organizations will adopt a data fabric as their data strategy as it provides the semantics and rich business context that AI requires to be used in real business cases. 
Daniel Yu
SVP, SAP Data and Analytics

MULTI-CLOUD NETWORKING

Enhanced Multi-Cloud Networking for Regulatory Compliance: By 2025, companies will increasingly rely on multi-cloud networking solutions, a capability required to meet diverse data sovereignty and industry-specific regulatory requirements. These advanced solutions will enable seamless connectivity and secure data transfer across cloud environments through robust encryption and access controls and they must also possess the critical ability to identify and remediate risks, threats, and vulnerabilities. CIOs and network architects will prioritize network designs that facilitate secure, efficient data flows, actively minimize regulatory risk, and maintain data integrity across cloud platforms.
Ali Shaikh
Chief Product Officer and Chief Operating Officer, Graphiant

STREAMING-FIRST APPROACH

Streaming-first approach grows with AI: Pressure will grow for more AI and applications to respond to real-time information to drive automation and meet the expectations of consumers. Organizations will adopt a "streaming-first" approach when architecting new applications.  These applications are Event-Driven and will replace traditional application architectures that process data at rest and to a large extent involve request/response communication. This will also facilitate more data sharing of real-time data between totally different domains of a business than previously.
Guillaume Aymé
CEO, Lenses.io

STREAMING DATA PLATFORMS: OBSERVABILITY AND SECURITY

In 2025, streaming data platforms will become indispensable for managing the exponential growth of observability and security data. Organizations will increasingly adopt streaming data platforms to process vast volumes of logs, metrics, and events in real-time, enabling faster threat detection, anomaly resolution, and system optimization to meet the demands of ever-evolving infrastructure and cyber threats.
Bipin Singh
Senior Director of Product Marketing, Redpanda

STREAMING DATA PLATFORMS: AI

In 2025, streaming data platforms will serve as the backbone for agentic AI, RAG AI and sovereign AI applications, providing the low-latency, high-throughput capabilities required to power autonomous decision-making systems and ensuring compliance with data sovereignty requirements.
Bipin Singh
Senior Director of Product Marketing, Redpanda

REAL-TIME DATA STREAMING FABRIC

Businesses will look to accelerate hyper-connecting applications and architectures across all parts of their business through real-time data streams. This "streaming fabric" across a business will blur the lines between previously isolated different AI, analytics and software architectures and allow connecting systems across business lines such as finance, ecommerce, manufacturing, distribution, supply chain. This connectivity will allow applications to be built that offer new digital consumer-facing services as well as ones that provide new levels of automation within a business.
Guillaume Aymé
CEO, Lenses.io

Related Links

Hot Topics

The Latest

Manufacturing Organizations Doubled AI Investment Yet Only 37% Fully Prepared to Operationalize AI

While 87% of manufacturing leaders and technical specialists report that ROI from their AIOps initiatives has met or exceeded expectations, only 37% say they are fully prepared to operationalize AI at scale, according to The Future of IT Operations in the AI Era, a report from Riverbed ...

Optimizing Decisions with Edge-First Cognitive Intelligence

Many organizations rely on cloud-first architectures to aggregate, analyze, and act on their operational data ... However, not all environments are conducive to cloud-first architectures ... There are limitations to cloud-first architectures that render them ineffective in mission-critical situations where responsiveness, cost control, and data sovereignty are non-negotiable; these limitations include ...

The New Perimeter Is the User: Why Identity Is the Real Network Edge

For years, cybersecurity was built around a simple assumption: protect the physical network and trust everything inside it. That model made sense when employees worked in offices, applications lived in data centers, and devices rarely left the building. Today's reality is fluid: people work from everywhere, applications run across multiple clouds, and AI-driven agents are beginning to act on behalf of users. But while the old perimeter dissolved, a new one quietly emerged ...

When AI Infrastructure Stops Behaving Like Infrastructure

For years, infrastructure teams have treated compute as a relatively stable input. Capacity was provisioned, costs were forecasted, and performance expectations were set based on the assumption that identical resources behaved identically. That mental model is starting to break down. AI infrastructure is no longer behaving like static cloud capacity. It is increasingly behaving like a market ...

Enterprise Resilience: Understanding the Shift from Static to Dynamic

Resilience can no longer be defined by how quickly an organization recovers from an incident or disruption. The effectiveness of any resilience strategy is dependent on its ability to anticipate change, operate under continuous stress, and adapt confidently amid uncertainty ...

Mobile Users Expect Perfection in 2026

Mobile users are less tolerant of app instability than ever before. According to a new report from Luciq, No Margin for Error: What Mobile Users Expect and What Mobile Leaders Must Deliver in 2026, even minor performance issues now result in immediate abandonment, lost purchases, and long-term brand impact ...

AI Confidence Is High. Readiness Is Not. What the Data Tells Us - and Why It Matters

Artificial intelligence (AI) has become the dominant force shaping enterprise data strategies. Boards expect progress. Executives expect returns. And data leaders are under pressure to prove that their organizations are "AI-ready" ...

Agentic AI, Realistic Expectations and the Future of IT Operations in 2026

Agentic AI is a major buzzword for 2026. Many tech companies are making bold promises about this technology, but many aren't grounded in reality, at least not yet. This coming year will likely be shaped by reality checks for IT teams, and progress will only come from a focus on strong foundations and disciplined execution ...

Trust, But Verify: Building Confidence in AI Through Human Oversight

AI systems are still prone to hallucinations and misjudgments ... To build the trust needed for adoption, AI must be paired with human-in-the-loop (HITL) oversight, or checkpoints where humans verify, guide, and decide what actions are taken. The balance between autonomy and accountability is what will allow AI to deliver on its promise without sacrificing human trust ...

Data Centers Plan to Reduce Reliance on Grid

More data center leaders are reducing their reliance on utility grids by investing in onsite power for rapidly scaling data centers, according to the Data Center Power Report from Bloom Energy ...