What Aerospike’s double recognition in the 2025 Gartner Hype Cycle really means

Gartner just validated what Aerospike has been building toward for more than a decade: real-time is no longer a specialized use case, but a foundation of modern systems. In the 2025 Hype Cycle for Data Management, Aerospike is recognized in two categories:

• Operational intelligence, now in the Plateau of Productivity

• Distributed transactional databases, rising along the Slope of Enlightenment

These are not speculative technologies. They represent the architectural shift happening now, where decisions must be made quickly, consistency must be preserved across systems and regions, and artificial intelligence (AI) is embedded into every operational layer.

Operational intelligence means running analytics, machine learning (ML), and even AI models inside the transaction flow itself, eliminating the lag between insight and action. Instead of pushing data to a separate analytics layer, organizations use operational intelligence to act on live data in the moment.

Distributed transactional databases are essential for ensuring consistency in high-performance systems and applications, including for payments, billing, auctions, betting, reservation systems, and ticketing. Aerospike Database 8 delivered the first real-time distributed ACID transaction database with high performance at scale. With Aerospike 8, applications can coordinate updates across large, globally distributed data sets with enterprise-grade reliability and speed.

Together, these capabilities form the data backbone of today’s most intelligent and accurate systems. They power fraud detection within global payment systems at industry-leading low false positive levels, personalization for millions for gaming and e-commerce that increases spend, and real-time bidding with markedly increased successful bid rates, all while reducing infrastructure expenditures.

Below, we break down the drivers Gartner says are fueling adoption in each of these categories and show how Aerospike is delivering against them in real-world, high-scale production environments.

Operational intelligence: The brain within the transaction

Operational intelligence is no longer an architectural luxury. It is fast becoming a competitive necessity, especially for systems that depend on real-time responsiveness, embedded intelligence, and uninterrupted decision loops.

The momentum behind operational intelligence reflects a fundamental shift: Instead of pushing operational data downstream to analytical systems, today’s applications analyze and act on that data in place, in the moment. It steers business processes dynamically, responds to fraud signals mid-transaction, personalizes experiences on the fly, and continuously updates pricing, routing, and resource allocation based on current context.

What once required handoffs between siloed systems, such as OLTP engines, caches, data stores, and warehouses, is now delivered within one real-time infrastructure. This shift is not theoretical. It is already happening at petabyte scale, in production, and across industries ranging from finance to retail to AdTech.

Gartner’s recognition of Aerospike in this category reflects not just a capability match but proof of execution. With mature data distribution, cluster management, and a patented storage engine, and native support for transactional and analytical convergence, Aerospike helps leading enterprises support operational intelligence across high-throughput, mission-critical workloads.

Here’s how we align with each of the drivers Gartner says are pushing operational intelligence into the mainstream.

1. Streamlining transactional and analytical infrastructure

For years, organizations have treated transactional and analytical workloads as distinct domains, each with its own infrastructure and performance profile. That divide wasn’t ideal; it was a workaround born out of necessity. Traditional transactional databases lacked the scale, concurrency, and latency guarantees to support analytics in real time. As a result, companies were forced to extract and move operational data into dedicated analytics platforms, incurring lag, complexity, and infrastructure bloat.

Aerospike’s patented Hybrid Memory Architecture (HMA) and tiered storage support transactional and real-time analytical access patterns within one database. HMA has the performance of in-memory systems, but with the persistence and cost profile of SSDs. It places indexes in memory, persists data on SSDs, and can tier data for long-term retention. Yet, with single-digit millisecond retrieval. As a result, applications can execute both operational logic and low-latency transactions and analytics on larger amounts of data, without introducing contention or delay. This eliminates redundant caching layers and load on more expensive data lakes or warehouses. And because Aerospike scales linearly with predictable performance, it supports this workload convergence even at extreme throughput.

Flipkart, one of the world’s largest e-commerce platforms, uses Aerospike for real-time inventory visibility and flash-sale pricing during nationwide retail events. Rather than relying on delayed syncs or external analytic stores, it executes pricing and availability checks in line with customer activity for responsiveness under peak load conditions, and simplifies backend architecture in the process.

Transactional and analytical processing no longer require separate systems. With Aerospike, they operate in sync, inside the same flow, on the same data.

2. Reducing reliance on legacy analytical systems

Historically, data had to be extracted, transformed, and loaded from transactional systems into analytical engines, often hours or days later. This created fragile pipelines, high infrastructure costs, and a fundamental gap between when a transaction occurred and when it could inform business decisions. In modern times, data gets pooled into data lakes, lakehouses, and warehouses for analysis. But it is expensive and not available for real-time analysis. Analytics became reactive, not operational.

With high throughput, low latency, and strong consistency, Aerospike allows real-time data to remain in place, not just for transactions but also for immediate analysis and model execution. With integrations to tools such as Trino (formerly PrestoSQL) and Spark SQL, Aerospike feeds today’s analytical stack for streaming and interactive analytics on fresh operational data without offloading or duplication. This architecture means companies reduce their dependence on expensive relational systems while supporting a moderate level of analytics, enabling predictive modeling that requires feature extraction and model scoring at scale.

Adobe’s real-time CDP made this shift by replacing a multi-layered stack of Redis, Cassandra, and HBase with one Aerospike deployment. It retained flexibility through downstream analytics integrations while simplifying data movement and making operational decisions faster. 

3. Automated recommendations based on real-time analysis can improve decisions

The promise of real-time decisions inside transactions has existed for years, but most systems have not been able to deliver on it. Historically, performance and scalability limits of transactional databases forced organizations to run analytics after the fact, using separate systems, delayed workflows, and data that was only eventually consistent. By combining high-throughput transactional processing with low-latency data access, Aerospike supports real-time analytics and model execution within the transaction path, even at massive scale. This runs fraud scoring, eligibility checks, offer personalization, or dynamic risk evaluations as the data is being written, not minutes or seconds later.

For example, PayPal uses Aerospike in its fraud prevention systems to evaluate user behavior and transaction context on the fly. With sub-millisecond latency and consistent uptime, it makes inline approval or rejection decisions that are both fast and accurate.

But fraud detection is just one example. Other systems, such as recommendation engines, need to evaluate real-time context and serve dynamic, personalized content based on users’ interactions. Whether it’s recommending products mid-session or rejiggering search results based on live inventory, model execution happens in the transaction stream, powered by live operational data.

4. Predictive ML inside the database

Model-driven decisions can’t happen downstream anymore. It needs to be embedded, fed with real-time features, scored with minimal latency, and executed within the flow of user interaction.

Aerospike combines millisecond read/write latency and native integration with AI/ML pipelines. It retrieves model inputs from the same system that processes the transaction, so decisions are accurate, timely, and based on live state. This includes time-windowed aggregates, behavioral features, session context, and transactional history, all accessible without data duplication or sync delays. Aerospike integrates directly with Apache Spark, Kafka, and stream processors, so teams run both operational inference and longer feedback loops on a shared, production-grade data foundation. Plus, Aerospike’s storage engine efficiency and performance enable more features to be served in SLA time windows, leading to more accurate predictions, be they for AdTech/real-time bidding, recommendation engines, or risk assessment systems.

The result is one system that powers everything from fraud model scoring to feeding GenAI context windows while preserving consistency and auditability across billions of decisions per day.

Distributed transactional databases: Consistency at global scale

The shift toward global systems is not new, but the expectations placed on them have changed dramatically.

Today’s applications are not just distributed. They are interactive, stateful, and increasingly intelligent. They must accept writes from users and machines around the world, reflect consistent outcomes in real time, and continue operating during failures, outages, or shifts in infrastructure. All of this must happen without sacrificing data integrity or developer speed.

Distributed transactional databases meet this challenge by supporting ACID-compliant operations across geographically dispersed nodes, cloud regions, and microservice boundaries. But building one that actually performs at scale, with the durability, consistency, and speed that today’s use cases require, has been challenging for most platforms.

That’s why Aerospike’s recognition in this category matters. Aerospike Database 8 introduced multi-record, strongly consistent, distributed transactions, backed by a battle-tested data distribution layer and a linearly scalable architecture. These capabilities help teams build applications that are not just highly available but fundamentally correct and globally consistent, without compromising performance or maintainability. Whether you're coordinating high-frequency trades, syncing product inventory across global storefronts, or maintaining trust in financial data across zones, Aerospike helps real-time systems behave predictably, recover gracefully, and scale confidently.

Below, we break down the five drivers Gartner identified as the main accelerators for the adoption of distributed transactional databases, and how Aerospike is helping customers put those principles into production.

1. Distributed transactions for global business operations 

For years, businesses operating across multiple regions were forced to choose between data availability with high performance and data integrity with low performance. While they could deploy databases in each region for low-latency access, coordinating transactions across those regions with strong consistency was considered impractical or too costly to scale, let alone doing so with high performance. The prevailing mindset was that global correctness came with performance penalties.

Aerospike Database 8 removes this tradeoff by offering distributed, multi-record ACID-compliant transactions that span nodes, regions,  and data centers. Aerospike performs transactions in order across globally distributed infrastructure, so applications perform complex, multi-entity updates across partitions without sacrificing latency or scale. This changes how teams design global applications. Developers no longer need to build around the limitations of local-only transactions or accept slow performance or low scale - this can now be achieved at the database level. Whether it’s updating a global financial ledger, ensuring a consistent game state, or maintaining accurate inventory across fulfillment centers, Aerospike allows systems to operate in multiple locations while thinking like one.

Organizations that once had to choose between performance and integrity can now have both, at scale, in production, and under real-world operational constraints.

2. Local writes with global views

Historically, distributed databases struggled to balance local responsiveness with global data consistency. Enterprises with regionally distributed infrastructure often faced this tradeoff: Either maintain fast, low-latency writes in local zones or enforce global consistency through complex coordination, often at the cost of performance and developer complexity.

Many teams resorted to writing data locally and pushing updates asynchronously to other regions. This pattern introduced sync lag, made global state hard to ascertain, and left systems vulnerable to conflict or data drift during failures or load imbalance. For time-sensitive applications, such as retail checkout, session tracking, or multi-device sync, this often disappointed users and made systems less reliable. 

Aerospike addresses this in multiple ways: asynchronous active-active replication via Aerospike Cross Datacenter Replication (XDR) for high-speed regional updates; synchronous active-active replication with Aerospike strong consistency and rack awareness features (a.k.a. “multi-site clustering”) for single records; and more recently,  distributed ACID transactions, introduced with the Aerospike Database 8 release. XDR supports low-latency writes, allowing asynchronous changes to other regions and ensuring high availability. For systems that require correctness guarantees across regions, Aerospike’s multi-site clustering enforces strict serializability at the database layer.

In addition, with Aerospike 8, applications can coordinate multi-record updates across global infrastructure while maintaining strong consistency and sub-millisecond local performance, meaning that systems no longer have to trade off availability, accuracy, or speed. For organizations like Myntra and Flipkart, it ensures that cart contents, product availability, and user actions remain accurate and consistent across regions and devices, even under extreme concurrency and load.

3. Portability across cloud and on-premises

As cloud strategies matured, enterprises increasingly needed flexibility in where and how their data platforms run. Traditional databases, designed for one deployment model, often made this difficult. Systems optimized for on-premises had trouble scaling to cloud-native environments, while those born in the cloud lacked features or cost control needed for hybrid or edge use cases. This forced organizations to maintain multiple databases for different deployment contexts, increasing operational burden and complicating application development. Worse, moving between clouds or from cloud to on-premises often required rethinking the data model or accepting different consistency and availability guarantees, slowing down innovation and locking teams into suboptimal architectures.

Aerospike was designed to operate in any environment: public cloud, private data center, hybrid cloud, or edge. Its Kubernetes-native orchestration and flexible storage engine let teams use one logical system across infrastructure boundaries, without rewriting applications or compromising on durability, performance, or how a system behaves when performing transactions.

In addition to supporting these varied deployments, Aerospike has been integral to hybrid-scale success stories, most notably at Wayfair. As Ken Bakunas detailed at an Aerospike Summit session, Wayfair operated mixed on‑prem data centers and Google Cloud clusters. During this hybrid phase, the team used XDR to migrate roughly 60 billion records from bare‑metal to cloud, maintaining data consistency and performance across sites in the U.S. and Europe. Aerospike served as the unified platform for use cases including customer identity tracking, recommendation engines, AdTech scoring, dynamic product content, and fulfillment, with sustained throughput at 20 million reads/sec and billions of ad impressions served annually. This highlights how Aerospike enables portability, supporting seamless transition and coexistence between on‑prem and cloud environments with consistent performance and centralized data operations.

As another example, Adobe uses Aerospike to coordinate data across availability zones using tiered storage, optimizing for both cost and latency while looking the same to developers and operators. Whether storing high-velocity transactional updates or longer-term analytical data, the system behaves the same and scales predictably. This architectural neutrality means enterprises shift workloads, expand regionally, or comply with changing regulations without redesigning the data backbone.

4. Alignment with microservice architectures

Today’s applications are built from independently deployed, independently scaled services. But microservices expect shared infrastructure to support concurrency, isolation, and fast access to shared data without forcing coordination overhead or sacrificing consistency guarantees.

Legacy databases often required tradeoffs here. Some tradeoffs meant services operated through a central data layer, creating bottlenecks. Others were limited to eventual consistency at the expense of correctness. Teams were left choosing between building their coordination logic or not behaving consistently.

Aerospike’s architecture supports record-level isolation, high concurrency, and multi-model data access, so microservices interact with shared datasets while retaining independence. Services write and read at high throughput without lock contention, and coordinate using distributed ACID transactions when needed. This makes Aerospike a natural fit for microservices that require local autonomy and global correctness, such as processing payments, applying inventory changes, or triggering downstream business logic based on real-time user interactions. Systems grow service-by-service without redesigning the data layer.

Instead of working around the database, developers build with one that aligns with how distributed applications are actually structured.

5. Support for high-volume, streaming, resilient applications

Some systems must do more than scale. They must withstand failure, handle traffic bursts, ingest high-frequency events, and enforce strong guarantees about data durability and correctness.

In traditional architectures, this often required layering multiple systems: an ingest pipeline to handle streaming data, a cache to absorb write bursts, a transactional system for correctness, and a separate store for long-term durability. The result was complexity, inconsistency, and operational fragility.

Aerospike supports these workloads natively. It provides linearly scalable write throughput, sub-millisecond latency, and strong consistency, making it suitable for real-time ingestion, stream processing, and transactional decisions. Native integrations with Kafka, Pulsar, and other event stream processing systems (e.g., HTTP/1 or HTTP/2 compatible ones) mean applications consume and persist high-volume data while keeping data correct and available. 

For tasks such as fraud detection, financial event tracking, Internet of Things (IoT) telemetry, and ad bidding, Aerospike delivers the combination of scale, resilience, and transactional behavior systems required under load. This is not just about performance. It is about predictability, durability, and system trust when it matters most.

The takeaway: Aerospike is where real-time gets real

Gartner’s recognition of Aerospike in both operational intelligence and distributed transactional is more than a milestone. It confirms that Aerospike is in the early mainstream as the database powering the next era of intelligent systems.

When infrastructure must think as fast as it transacts, Aerospike delivers a rare combination of capabilities:

• Real-time AI support without fragile workarounds

• Distributed ACID transactions without performance or scalability trade-offs

• High availability and consistency without added complexity

• Massive scale and sub-millisecond latency without architectural sprawl

Aerospike is not just built for today’s workloads. It is built for today’s decision-making. It lets global enterprises unify streaming data, transactional updates, and operational AI into one, always-on platform that scales linearly, runs lean, and performs predictably.

If you’re designing systems where milliseconds matter, where consistency must span continents, and where intelligence cannot be delayed or degraded, Aerospike is the infrastructure you can build on with confidence.