In today’s hyper-connected world, the ability to act on insights the moment they emerge isn’t just an advantage—it’s a necessity. This is especially true for Large Language Models (LLMs), which are rapidly transforming how businesses operate. From hyper-personalized customer interactions to sophisticated risk assessments, LLMs thrive on immediate, contextual data to deliver accurate and impactful results. And in the high-stakes realm of FinTech, where milliseconds can mean millions, real-time inference pipelines are no longer a luxury but a fundamental requirement for deploying LLMs effectively.

This is precisely where DeltaStream steps in, positioning itself as the ideal real-time data infrastructure for building and deploying robust, low-latency LLM inference pipelines.

The Real-Time LLM Inference Imperative: Context is King

LLMs are powerful, but their true potential is unlocked when they operate on fresh, comprehensive data. Whether an LLM is generating personalized financial advice or flagging a suspicious transaction, the quality and relevance of its output are directly tied to the timeliness and completeness of the information it receives. Traditional, batch-oriented data architectures simply can’t keep pace with the demands of real-time LLM inference, leading to:

• Stale Context: LLMs performing on outdated information deliver less accurate and less useful responses.
• Delayed Decisions: Slower inference means missed opportunities for instant customer engagement or rapid fraud prevention.
• Increased Complexity: Trying to force real-time capabilities onto legacy systems creates brittle, hard-to-maintain data pipelines.

This creates a critical need for a platform that can seamlessly ingest, process, and deliver real-time, unified data directly to your LLMs for instantaneous, context-rich inference.

DeltaStream: The End-to-End Solution for Real-Time LLM Inference

DeltaStream is engineered to eliminate these bottlenecks, providing a unified, serverless platform that seamlessly integrates streaming, real-time, and batch analytics. This makes it an unparalleled foundation for building real-time LLM inference pipelines:

• Streaming-First Ingestion: DeltaStream is purpose-built for data in motion. It ingests high-velocity data streams from any source – think real-time market data, customer clickstreams, or transaction logs – with minimal latency. This ensures your LLMs always have the freshest inputs to work with
• Unified Data Processing for Rich Context: LLMs thrive on comprehensive context. DeltaStream brings together streaming and historical data, allowing you to perform complex feature engineering that combines real-time events with rich historical patterns. This could involve creating features like a customer’s recent spending habits, credit history, or common transaction types, all critical for grounding your LLM with relevant information. DeltaStream leverages the right engine (Flink for streaming, Spark for batch) to process this unified data efficiently.
• Real-Time Materialized Views for Lightning-Fast Retrieval: This is a game-changer for LLM inference. DeltaStream allows you to create continuously updated, low-latency materialized views. These views pre-aggregate, enrich, and transform data into the precise format your LLM needs for lightning-fast retrieval. Imagine your LLM instantly accessing a customer’s real-time risk profile, personalized product recommendations, or up-to-the-second market sentiment without any delays. This pre-computation drastically reduces latency for critical LLM queries.
• SQL-First Simplicity: Empower your data scientists and engineers to build sophisticated real-time data pipelines and materialized views using familiar SQL. This accelerates development, reduces time-to-market for new LLM-powered features, and lowers the barrier to entry for real-time analytics, letting your teams focus on model refinement, not data wrangling.
• Serverless and Scalable: Focus on refining your LLM and building innovative applications, not managing infrastructure. DeltaStream handles the heavy lifting of provisioning, scaling, and managing underlying compute resources (Flink, Spark, ClickHouse). This ensures your LLM inference pipelines can effortlessly handle fluctuating data volumes and model demands, providing consistent low-latency responses.

Real-World FinTech Use Case: Real-Time Personalized Financial Advice with LLMs

Imagine a FinTech application providing real-time, personalized financial advice. A customer asks, “Should I invest in XYZ stock?” or “How can I reduce my credit card debt?” For an LLM to answer effectively, it needs immediate and highly contextual information.

Here’s how DeltaStream powers this real-time LLM inference pipeline:

1. Real-Time Data Ingestion

• Market Data: Real-time stock prices, news sentiment, and trading volumes are ingested into DeltaStream as high-velocity streams
• Customer Financial Data: Transaction history, investment portfolios, credit scores, and savings goals are continuously updated and streamed.
• User Interactions: Past conversations with the LLM, clickstream data on financial products, and Browse history are also ingested in real-time.

2. Dynamic Feature Engineering & Contextualization

DeltaStream’s streaming pipelines (powered by Flink) continuously enrich this raw data:

• Market Insights: Calculating real-time moving averages for stocks, identifying sudden volume spikes, or aggregating sentiment from financial news feeds.
• Customer Financial Health: Deriving features like current debt-to-income ratio, available credit, recent large expenditures, or investment diversification, blending real-time transactions with historical financial records.
• Behavioral Context: Analyzing a customer’s recent interest in specific financial products or their typical saving habits.

3. Real-Time Materialized Views for LLM Grounding

All these processed and enriched features are continuously updated in low-latency materialized views within DeltaStream. These views act as a continuously refreshed knowledge base for your LLM, providing:

• mv_realtime_market_data: Up-to-the-second stock prices, volatility, and relevant news snippets
• mv_customer_financial_profile: The customer’s most current credit score, debt levels, investment holdings, and recent financial activities.
• mv_user_interaction_context: Summaries of recent financial queries or product interests.

4. Instant LLM Inference and Response Generation

When a customer poses a question, the LLM inference service queries these real-time materialized views within DeltaStream. The LLM is then grounded with this fresh, relevant data, allowing it to:

• Understand Context: The LLM knows the customer’s specific financial situation and current market conditions.
• Generate Accurate Advice: It can provide precise, personalized recommendations based on the most current data, e.g., “Given your current debt, consider prioritizing higher-interest credit cards before investing in XYZ stock, which has shown recent volatility.”
• Explain Rationale: The LLM can elaborate on why it’s giving certain advice, referencing the real-time data it accessed.

5. Continuous Feedback Loop

The customer’s interaction with the LLM and the outcome of the advice (e.g., did they act on it? was it helpful?) are fed back into DeltaStream. This data is used to continuously refine and fine-tune the LLM, ensuring it becomes even more accurate and helpful over time.

This approach transforms generic advice into highly personalized, real-time guidance, significantly enhancing customer satisfaction and engagement.

The Future is Real-Time and LLM-Powered

The synergy between real-time data infrastructure and LLMs is profoundly reshaping FinTech. DeltaStream provides the robust, unified, and simplified foundation necessary to build these cutting-edge, low-latency LLM inference pipelines. Don’t let fragmented data hold your LLM ambitions back. Embrace DeltaStream and empower your LLMs to deliver instant, intelligent, and context-aware financial solutions.

What kind of real-time LLM application are you eager to build?

This blog was written by the author with assistance from AI to help with outlining, drafting, or editing.

The digital age has brought unprecedented speed and complexity to financial transactions, making the fight against Anti-Money Laundering (AML) more challenging than ever. Traditional, rule-based AML systems, often reliant on batch processing, struggle to keep pace with sophisticated financial criminals who exploit these very characteristics

Enter the power of real-time processing combined with the analytical prowess of Large Language Models (LLMs). Imagine a system that can detect suspicious patterns, anomalies, and hidden relationships in financial flows as they happen, enabling immediate intervention. This isn’t a distant dream; it’s achievable today by leveraging platforms like DeltaStream to construct a robust, real-time AML inference pipeline powered by LLMs.

Why LLMs for Anti-Money Laundering?

LLMs have revolutionized natural language understanding and generation, but their utility extends far beyond chatbots. In AML, LLMs offer a significant leap over conventional methods due to their ability to:

• Uncover Complex Patterns: Unlike rigid rules, LLMs can learn intricate, non-obvious patterns within vast datasets of transaction descriptions, communications, and customer data. This includes identifying “structured” transactions (like smurfing, where large sums are broken into smaller amounts) that are designed to evade detection.
• Contextual Understanding: LLMs can process unstructured data (e.g., free-text fields, notes) to extract crucial context, infer intent, and link seemingly disparate pieces of information.
• Behavioral Anomaly Detection: By learning “normal” customer behavior from historical data, LLMs can flag deviations, enabling proactive identification of risky activities as they emerge.
• Automate and Enhance Reporting: They can assist in generating more accurate and detailed Suspicious Activity Reports (SARs) by summarizing relevant information and even pre-populating forms.
• Reduce False Positives: While a challenge, well-tuned LLMs can potentially reduce the high false positive rates often associated with traditional AML systems, allowing human analysts to focus on truly suspicious cases.

Why DeltaStream for Real-Time AML?

Building a real-time AML pipeline demands a platform capable of handling continuous data streams, performing complex transformations, and serving insights with minimal latency. DeltaStream, built on Apache Flink, is ideally suited for this role:

• Real-Time Data Ingestion & Processing: DeltaStream excels at ingesting massive volumes of streaming financial transaction data from sources like Kafka or Kinesis. Its Flink-powered engine ensures low-latency processing.
• SQL-Native Stream Processing: It allows financial institutions to define sophisticated real-time analytics and transformations using familiar SQL, simplifying the development of complex feature engineering logic.
• Unified Streaming Catalog & Governance: DeltaStream provides a centralized catalog for all streaming data, alongside robust Role-Based Access Control (RBAC), ensuring data security and compliance in a highly regulated industry.
• Materialized Views for Operational Efficiency: It can maintain real-time materialized views of aggregated data, crucial for powering dashboards, alerts, and downstream systems.
• Cost Efficiency: By “shifting left” data processing from batch data warehouses, DeltaStream can reduce overall operational costs associated with large-scale analytics.

Architecting the Real-Time AML Inference Pipeline

A real-time AML pipeline combining LLMs and DeltaStream typically follows a Feature/Training/Inference (FTI) architecture:

1. Data Ingestion & Feature Engineering (Powered by DeltaStream)

• Streaming Sources: Financial transactions, customer updates, sanctions lists, and other relevant data stream continuously into DeltaStream.
• Real-time Transformations: DeltaStream processes these raw streams. This is where crucial feature engineering takes place. Using SQL, you can:
  
  
  • Extract entities (names, addresses, transaction types, amounts).
  • Calculate real-time aggregations (e.g., total transactions in the last 5 minutes for a customer).
  • Join streaming data with static reference data (e.g., customer profiles, known risky entities).
  • Generate textual features for LLM input (e.g., combining transaction description with counterparty name).
• Feature Store Integration: The engineered features are often pushed to a real-time feature store (like Feast), ensuring consistency between the data used for training the LLM and the data used for live inference.

2. LLM Training (Offline/Batch)

• Historical Data: The LLM is trained on historical, labeled financial data (transactions, customer information, past SARs, known illicit activities). This training leverages the features prepared by the feature pipeline.
• Model Selection & Fine-tuning: This involves choosing an appropriate LLM architecture and fine-tuning it for AML-specific tasks like anomaly detection, risk scoring, and suspicious activity classification.

3. Real-Time LLM Inference (DeltaStream as the Orchestrator)

• Triggering Inference: As new, real-time features are generated by DeltaStream, they are fed to the deployed LLM for inference.
• LLM Prediction: The LLM analyzes these features, identifies potential anomalies, unusual patterns, or links to known illicit activities. It can provide a risk score, classify the type of suspicious activity, or even generate a preliminary narrative.
• DeltaStream for Post-Inference Processing: The LLM’s predictions are streamed back into DeltaStream. Here, further real-time logic can be applied:
  
  
  • Thresholding & Rule Application: Apply business rules or thresholds to LLM scores to filter out low-risk alerts.
  • Alert Generation: If a transaction meets the criteria, DeltaStream can trigger immediate alerts to AML analysts.
  • Contextual Enrichment: Enrich the alert with additional real-time context from other streams (e.g., customer’s recent activity, news related to involved entities).
  • Downstream System Integration: Push alerts and enriched data to case management systems, fraud investigation platforms, or even directly to transaction blocking systems.

4. Feedback Loop & Continuous Improvement

• Analyst Feedback: Insights from human analysts (e.g., confirmed money laundering cases, false positives) are fed back into the system.
• Model Retraining: This feedback is crucial for retraining and fine-tuning the LLM, ensuring it adapts to new money laundering techniques and improves its accuracy over time.

Benefits and Challenges

Benefits:

• Speed and Agility: Detect and react to financial crime in real-time, significantly reducing the window for illicit activities.
• Enhanced Accuracy: LLMs can uncover subtle and complex patterns that traditional rules miss, leading to more precise detection.
• Reduced False Positives: While still an area of development, LLMs have the potential to minimize the investigative burden by focusing on higher-probability threats.
• Scalability: Both LLMs and DeltaStream are designed for scalability, handling massive volumes of data and requests.
• Adaptability: LLMs can adapt to evolving money laundering typologies with continuous retraining.

Challenges:

Explainability: Understanding why an LLM flagged a transaction can be complex, posing challenges for regulatory compliance and investigations. Techniques like LIME or SHAP are crucial here.

Data Quality and Bias: LLMs are highly dependent on the quality and representativeness of their training data. Biases in historical data can lead to unfair or inaccurate predictions.

Computational Cost: Running LLM inference in real-time can be computationally intensive, requiring optimized serving infrastructure.

Regulatory Scrutiny: The use of AI, especially “black box” models, in critical areas like AML is under increasing regulatory scrutiny, demanding robust governance and auditability.

Conclusion

The combination of LLMs and DeltaStream represents a powerful paradigm shift in the fight against financial crime. By building real-time inference pipelines, financial institutions can move from reactive to proactive AML, leveraging the deep analytical capabilities of LLMs for sophisticated anomaly detection and the unparalleled speed and processing power of DeltaStream for continuous, actionable intelligence. As these technologies mature and best practices evolve, we can anticipate a significant strengthening of our defenses against money laundering in the real-time financial world.

This blog was written by the author with assistance from AI to help with outlining, drafting, or editing.

Stream processing has become a cornerstone of modern data architectures, enabling real-time analytics, event-driven applications, and continuous data pipelines. From tracking user activity on websites to monitoring IoT devices or processing financial transactions, stream processing systems allow organizations to handle data as it arrives. However, not all stream processing is created equal. Two fundamental paradigms—stateless and stateful stream processing—offer distinct approaches to managing data streams, each with unique strengths, trade-offs, and use cases. This blog’ll explore the technical differences, dive into their implementations, provide examples, and touch on when each approach is most applicable.

What is Stream Processing?

First, a little background. Stream processing differs from traditional batch processing by operating on continuous, unbounded flows of data—think logs, sensor readings, or social media updates arriving in real time. Frameworks like Apache Kafka Streams, Apache Flink, and Spark Streaming provide the infrastructure to efficiently ingest, transform, and analyze these streams. The key distinction between stateless and stateful processing lies in how these systems manage information across events.

Stateless Stream Processing: Simplicity in Motion

Stateless stream processing treats each event as an isolated entity, processing it without retaining memory of prior events. When an event arrives, the system applies predefined logic based solely on its content and then moves on.

How Stateless Works:

• Input A stream of events, e.g., {user_id: 123, action: "click", timestamp: 2025-03-13T10:00:00}.
• Processing Apply a transformation or filter, such as “if action == ‘click’, increment a counter” or “convert timestamp to local time.”
• Output Emit the result (e.g., a metric or enriched event) without referencing historical data.

Technical Characteristics:

• No State Storage Stateless systems don’t maintain persistent storage or in-memory context, reducing resource overhead.
• Scalability Since events are independent, stateless processing scales horizontally easily. You can distribute the workload across nodes without synchronization.
• Fault Tolerance Recovery is straightforward—lost events can often be replayed without affecting correctness, assuming idempotency (i.e., processing the same event twice yields the same result).

Latency Processing is typically low-latency due to minimal overhead.

Example Use Case Consider a real-time clickstream filter that identifies and forwards “purchase” events from an e-commerce site. Each event is evaluated independently: if the action is “purchase,” it’s sent downstream; otherwise, it’s discarded. No historical context is needed.

Implementation Example (Kafka Streams):

Trade-Offs: Stateless processing is lightweight and simple but limited. It can’t handle use cases requiring aggregation, pattern detection, or temporal relationships—like counting clicks per user over an hour—because it lacks memory of past events.

Stateful Stream Processing: Memory and Context

Stateful stream processing, in contrast, maintains state across events, enabling complex computations that depend on historical data. The system tracks information—like running totals, user sessions, or windowed aggregates—in memory or persistent storage.

How Stateful Works:

• Input The same stream, e.g., {user_id: 123, action: "click", timestamp: 2025-03-13T10:00:00}.
• Processing Update a state store, e.g., “increment user 123’s click count in a 1-hour window.”
• Output Emit results based on the updated state, e.g., “user 123 has 5 clicks this hour.”

Technical Characteristics:

• State Management requires mechanisms to track state, such as key-value stores (e.g., RocksDB in Flink) or in-memory caches.
• Scalability More complex due to state partitioning and consistency requirements. Keys (e.g., user_id) are often used to shard state across nodes.
• Fault Tolerance State must be checkpointed or replicated to recover from failures, adding overhead but ensuring correctness.
• Latency Higher than stateless due to state access and updates, though optimizations like caching mitigate this.

Example Use Case: A fraud detection system that flags users with more than 10 transactions in a 5-minute window. This requires tracking per-user transaction counts over time—a stateful operation.

Implementation Example (Flink):

Trade-Offs: Stateful processing is powerful but resource-intensive. Managing state increases memory and storage demands, and fault tolerance requires sophisticated checkpointing or logging (e.g., Kafka’s changelog topics). It’s also prone to issues like state bloat (e.g., expiring old windows) if not properly managed.

When to Use Stateless vs. Stateful?

• Stateless: Opt for stateless processing when your application involves simple transformations, filtering, or enrichment without historical context. It’s ideal for lightweight, high-throughput pipelines where speed and simplicity matter.
• Stateful: Choose stateful processing for analytics requiring memory—aggregations (e.g., running averages), sessionization, or pattern matching. It’s essential when the “why” behind an event depends on “what came before.”

Wrap-up

Stateless and stateful stream processing serve complementary roles in the streaming ecosystem. Stateless processing offers simplicity, scalability, and low latency for independent event handling, making it a go-to for straightforward transformations. In contrast, more complex and resource-heavy, stateful processing provides more advanced capabilities like time-based aggregations and contextual analysis, which are critical for real-time insights. Choosing between them depends on your use case: stateless for speed and simplicity, stateful for depth and memory. Modern frameworks often support both, allowing hybrid pipelines where stateless filters feed into stateful aggregators. Understanding their mechanics empowers you to design efficient, purpose-built streaming systems.

Consumer expectations for speed of service has only increased since the dawn of the information age. The ability to process information quickly and cost-effectively is no longer a luxury, it’s a necessity. Businesses across industries are racing to extract value from their data in real-time, and a transformative approach known as “shift left” is gaining traction. With streaming technologies, organizations can move data processing earlier in the pipeline to slash storage and compute costs, cut latency, and simplify operations. Let’s dive into what shift left means, why it’s a game-changer, and how it can reshape your data strategy.

Streaming Data: The Backbone of Modern Systems

Streaming data is ubiquitous in today’s tech ecosystem. From mobile apps to IoT ecosystems, real-time processing powers everything from convenience to security. Consider the scale of this trend: Uber runs over 2,500 Apache Flink jobs to keep ride-sharing seamless; Netflix manages a staggering 16,000 Flink jobs internally; Epic Games tracks real-time gaming metrics; Samsung’s SmartThings platform analyzes device usage on the fly; and Palo Alto Networks leverages streaming for instant threat detection. These examples highlight a clear truth: batch processing alone can’t keep pace with the demands of modern applications.

The Traditional ELT Approach: A Reliable but Rigid Standard

Historically, organizations have leaned on Extract, Load, Transform (ELT) pipelines to manage their data. In this model, raw data is ingested into data warehouses or lakehouses and then transformed for downstream use. Many adopt the “medallion architecture” to structure this process:

1. Bronze Raw, unprocessed data lands here.
2. Silver Data is cleansed, filtered, and standardized.
3. Gold Aggregations and business-ready datasets are produced.

This approach has been a staple thanks to the maturity of batch processing tools and its straightforward design. However, ELT’s limitations are glaring as data volumes grow and real-time needs intensify.

The Pain Points of ELT

1. High Latency Batch jobs run on fixed hourly, daily, or worse schedules, leaving a gap between data generation and actionability. For time-sensitive use cases, this delay is a dealbreaker.
2. Operational Complexity When pipelines fail, partial executions can leave a mess. Restarting often requires manual cleanup, draining engineering resources.
3. Cost Inefficiency Batch processing recomputes entire datasets, even if only a fraction has changed. This overprovisioning unnecessarily inflates compute costs.

Shift Left: Processing Data in Flight

Enter the shift left paradigm. Instead of deferring transformations to the warehouse, this approach uses streaming technologies—like Apache Flink—to process data as it flows through the pipeline. By shifting computation upstream, organizations can tackle data closer to its source, unlocking dramatic improvements.

Why Shift Left Wins

1. Reduced Latency Processing shrinks from hours or minutes to seconds—or even sub-seconds—making data available almost instantly.
2. Lower Costs Incremental processing computes only what’s new, avoiding the waste of rehashing unchanged data. Reduced storage costs from data filtering before it lands and no redundant data copies.
3. Simplified Operations Continuous streams eliminate the need for intricate scheduling and orchestration, reducing operational overhead.

A Real-World Win

Consider a company running batch pipelines in a data warehouse, costing $11,000 monthly. After shifting left to streaming, their warehouse bill dropped to $2,500. Even factoring in streaming infrastructure costs, they halved their total spend—while slashing latency from 30 minutes to seconds. This isn’t an outlier; it’s a glimpse of shift left’s potential.

Bridging the Expertise Gap

Streaming historically demanded deep expertise—think custom Flink jobs or Kafka integrations. That barrier is crumbling. Platforms like Delta Stream are democratizing stream processing with:

• Serverless Options No need to manage clusters or nodes.
• Automated Operations Fault tolerance and scaling are handled behind the scenes.
• SQL-Friendly Interfaces Define transformations with familiar syntax, not arcane code.
• Reliability Guarantees Exactly-once processing ensures data integrity without extra effort.

This shift makes streaming viable for teams without PhDs in distributed systems.

Transitioning Made Simple

Adopting shift left doesn’t mean scrapping your existing work. If your batch pipelines use SQL, you’re in luck: those statements can often be repurposed for streaming with minor tweaks. This means you can:

1. Preserve your business logic.
2. Stick with SQL-based workflows your team already knows.
3. See instant latency and cost benefits.
4. Skip the headache of managing streaming infrastructure.

For example, a batch query aggregating hourly sales could pivot to a streaming windowed aggregation with near-identical syntax—same logic, faster results.

The Future Is Streaming

Shifting left isn’t just an optimization, it’s a strategic evolution. As data grows and real-time demands escalate, clinging to batch processing risks falling behind. Thanks to accessible tools and platforms, what was once the domain of tech giants like Netflix or Uber is now within reach for organizations of all sizes. The numbers speak for themselves: lower costs, sub-second insights, and leaner operations. For competitive businesses, shifting left may soon transition from a smart move to a survival imperative. Ready to rethink your pipelines? Take a look at our on-demand webinar for more, Shift Left: Lower Cost & Reduce Latency of your Data Pipelines.

The Apache Flink crew dropped version 2.0.0 on March 24, 2025, and it’s the kind of update that makes you sit up and pay attention. I wrote about what was likely coming to Flink 2.0 back in November, and the announcement doesn’t disappoint. This isn’t some minor patch cobbled together over a weekend—165 people chipped in over two years, hammering out 25 Flink Improvement Proposals and squashing 369 bugs. It’s the first big leap since Flink 1.0 landed back in 2016, and as someone who’s been in the data weeds for more years than I care to remember, I’m here to tell you it’s a release that feels less like hype and more like a toolset finally catching up to reality. Let’s dig into the details.

The Backdrop: Data’s Moving Fast, and We’re Still Playing Catch-Up

Nine years ago, Flink 1.0 showed up when batch jobs were still the default, and streaming was the quirky sidekick. Fast forward to 2025, and the game’s flipped; real-time isn’t optional; it’s necessary. Whether it’s tracking sensor pings from a factory floor or keeping an AI chatbot from spitting out stale answers, data’s got to move at the speed of now. The problem is that most streaming setups still feel like they’re held together with duct tape and optimism, costing a fortune and tripping over themselves when the load spikes. With Flink 2.0, this all becomes more manageable. 

What’s Cooking in Flink 2.0

The official rundown’s got plenty of details, but I’m not here to parrot the press release. Here’s my take on what matters:

1. State Goes Remote: Less Baggage, More Breathing Room
   Flink’s new trick of shoving state management off to remote storage is a quiet killer. No more tying compute and state together like they’re stuck in a toxic relationship; now they’re free to do their own thing. With some asynchronous magic and a nod to stuff like ForStDB, it’s built to scale without choking, especially if you’re running on Kubernetes or some other cloud playground. This feels like a lifeline for anyone who’s watched a pipeline buckle under big state.
2. Materialized Tables: Less Babysitting, More Doing
   Ever tried explaining watermarks to a new hire without their eyes glazing over? Flink’s Materialized Tables promise to deal with the details. You toss in a query and a freshness rule, and it figures out the rest, the schema, refreshes, and all the grunt work. That means you can build a pipeline that works for batch and streaming relatively easily. Practical, not flashy.
3. Paimon Integration: Expanded Lakehouse Support
   The Apache Paimon support was interesting to see. I’ve been curious about what might happen in that space for a while now. I wrote about it in late 2023. The focus is on the concept of the Streaming Lakehouse. 
4. AI Nod: Feeding the Future
   They hint at AI and large language models with a “strong foundation” line but don’t expect a manual yet. My guess is that Flink is betting on being the real-time engine for fraud alerts or LLM-driven apps that need fresh data to stay sharp, which just makes sense. Flink CDC 3.3 introduced support for OpenAI chat and embedding models, so keep an eye on those developments.

Why I’m Excited for this Flink Release

Flink 2.0 doesn’t feel like it’s chasing trends; it’s tackling the stuff that keeps engineers up at night. Compared to Kafka Streams, which is lean but light on heavy lifting, or Spark Streaming, which still leans on micro-batches like it’s 2015, Flink can handle the nitty-gritty of event-by-event processing. This release doubles down with better cloud smarts and focuses on keeping costs sane. It’s not about throwing more hardware at the problem; it’s about working more innovatively, and that’s a win for anyone who’s ever had to justify a budget.

The usability updates really can’t be understated. Stream processing can be a beast to learn, but those Materialized Tables and cleaner abstractions mean you don’t need to be a guru to get started. It’s still Flink—powerful as ever—but it’s not gatekeeping anymore.

The Rough Edges: Change Hurts

Fair warning: This isn’t a plug-and-play upgrade if you’re cozy on Flink 1.x. Old APIs like DataSet are deprecated, and Scala’s legacy bits got the boot. Migration’s going to sting if your setup’s crusty. But honestly? That’s the price of progress. They’re trimming the fat to keep Flink lean and mean; dealing with the pain now will provide many years of stability.

The Bottom Line: Flink 2.0 is Worth a Look

Flink 2.0 isn’t here to reinvent the wheel but to make the wheel roll more smoothly. It’s a solid, no-nonsense upgrade that fits the chaos of 2025’s data demands: fast, scalable, and less of a pain to run. The community’s poured real effort into this, and it shows. Get all the details from the Flink team in their announcement and then start planning for your updates. Or take a look at DeltaStream if you’re interested in all the functionality of Flink, but without the required knowledge and infrastructure.

Over the past decade, the way businesses handle data has fundamentally changed. Organizations that once relied on batch processing to analyze data at scheduled intervals are now moving toward streaming analytics—where data is processed in real-time. While early adopters of streaming technologies were primarily large tech companies like Netflix, Apple, and DoorDash, today, businesses of all sizes are embracing streaming analytics to make faster, more informed decisions.

But what’s driving this shift? Below, we explore the key trends pushing organizations toward streaming analytics and highlight the most common use cases where it’s making a significant impact.

1. Rising Customer Expectations for Real-Time Insights

“ 74% of IT leaders report that streaming data enhances customer experiences, and 73% say it enables faster decision-making.” Source: VentureBeat

Modern consumers expect instant interactions. Businesses that rely on batch-processed analytics struggle to keep up with customer demands for instant responses. Streaming analytics allows companies to react in real-time, improving customer satisfaction and competitive advantage. 

Example Use Cases:

• E-commerce: Dynamic pricing and personalized recommendations based on real-time browsing behavior.
• AdTech: Update ad bids dynamically based on audience engagement.
• Gaming: Tailors in-game rewards based on real-time player activity.

2. Enterprise-Ready Solutions Make Streaming More Accessible

“ The streaming analytics market is projected to grow at a CAGR of 26% from 2024 to 2032, reaching $176.29B.” Source: GMInsights

Previously, streaming analytics required specialized expertise and was considered too complex and costly for most organizations. Today, the rise of streaming ETL and continuous data integration–combined with cloud-native solutions such as Google Dataflow, RedPanda, Confluent, and DeltaStream–is lowering the barrier to adoption. These platforms provide enterprise-friendly managed solutions that eliminate operational overhead, allowing businesses to implement streaming analytics without needing large in-house engineering teams. 

Example Use Cases:

• Data Warehousing: Ingests and updates analytics data in real time, ensuring dashboards reflect the latest insights.
• IoT Platforms: Aggregates and processes sensor data instantly for real-time monitoring and automation.
• Financial Services: Streams transactions into risk analytics models to detect fraud as it happens.

3. The Rise of LLMs and the Need for Fresh, Real-Time Data

“ AI and ML adoption are driving a 40% increase in real-time data workloads.” Source: InfoQ

The rapid adoption of LLMs has shifted the focus from model capabilities to data freshness and uniqueness. Foundational models are becoming increasingly commoditized, and organizations can no longer rely on model performance alone for differentiation. Instead, real-time access to fresh, proprietary data determines accuracy, relevance, and competitive advantage.

The recent partnership between Confluent and Databricks highlights this growing demand for real-time data in AI workloads. Yet, stream processing remains a critical gap—organizations need ways to transform, enrich, and prepare real-time data before feeding it into RAG pipelines and other AI-driven applications to ensure accuracy and relevance.

Example Use Cases:

• Real-Time Feature Engineering: Continuously transforms raw data streams into structured features for AI models.
• News & Financial Analytics: Filters, enriches, and feeds LLMs with the latest market trends and breaking news.
• Conversational AI & Chatbots: Incorporates real-time business data, technical support, and events to improve AI-driven interactions.

4. Regulations are Driving Real-Time Monitoring Needs

“ On November 12, 2024, the UK’s Financial Conduct Authority (FCA) fined Metro Bank £16.7 million for failing real-time monitoring of 60 million transactions worth £51 billion, a direct violation of their Anti-Money Laundering (AML) regulations.” Source FCA

Industries with strict compliance requirements are now mandated to monitor and report data events in real-time. Whether it’s fraud detection in banking, patient data security in healthcare, or GDPR compliance in data privacy, organizations must implement streaming analytics to meet these regulatory requirements. Real-time monitoring ensures businesses can detect anomalies instantly and prevent costly compliance violations.

Example Use Cases:

• Banking: Anti-money laundering (AML) compliance.
• Telecom: Real-time call monitoring for regulatory audits.
• Government: Cybersecurity and national security threat detection.

Conclusion: Streaming Analytics is No Longer Optional

What was once a niche technology for highly technical organizations is now a necessity for businesses across industries. The push toward real-time analytics is being fueled by customer expectations, technological advancements, AI adoption, regulatory requirements, and competitive pressures.

Whether businesses are looking to prevent fraud, optimize supply chains, or personalize customer experiences, the ability to analyze data in motion is now a crucial part of modern data strategies.

For organizations still relying on batch processing, it is time to evaluate how streaming analytics can transform their data-driven decision-making. The future is real-time—how will you be ready?

In the past decade, we’ve witnessed a fundamental transformation in the way companies handle their data. Traditionally, organizations relied on batch processing, which involves collecting and processing data at fixed intervals. This worked well in slower-paced industries where insights weren’t needed instantly. However, in a world where speed and real-time decisions are everything, batch processing can feel like an outdated relic, unable to keep up with the demands of real-time decisions and customer expectations. So, how do you know if your business is ready to make the leap from batch to real-time processing? Below, we’ll explore five telltale signs that it’s time to leave batch behind and embrace real-time systems for a more agile, responsive business.

1. Delayed Decision-Making Is Impacting Outcomes

In many industries, the ability to make decisions quickly is the difference between seizing an opportunity and losing it forever. If delays consistently hinder your decision-making in data availability caused by batch processing, your business is suffering.

For example, imagine a retailer that runs inventory updates only once a day through batch processes. If a product sells out in the morning but isn’t flagged as unavailable until the nightly update, the company risks frustrating customers with out-of-stock orders. In contrast, a real-time system would update inventory levels immediately, ensuring always accurate availability.

Delayed decisions caused by outdated data can also lead to financial losses, missed revenue opportunities, and compliance risks in industries such as banking, healthcare, and manufacturing. If you say, “We could’ve avoided this if we had known sooner,” consider real-time processing.

2. Customer Expectations for Real-Time Experiences

Today’s customers expect instant gratification. Whether they want real-time updates on their food delivery, immediate approval for a loan application, or a seamless shopping experience, the demand for speed is non-negotiable. With its inherent lag, batch processing simply can’t meet these expectations.

Take, for example, the rise of ride-sharing apps like Uber or Lyft. These platforms rely on real-time data to match drivers with riders, calculate arrival times, and adjust pricing dynamically. A batch system would create noticeable delays and undermine the entire user experience.

If you receive complaints about laggy services, slow responses, or poor user experience, this is a strong indicator that you need to adopt real-time systems to meet customer expectations.

3. Data Volumes Are Exploding

The amount of data businesses collect today is staggering and growing exponentially. Whether it’s customer interactions, IoT device outputs, social media activity, or transaction data, the challenge is collecting and processing this data efficiently.

Batch processing often struggles to handle high data volumes. Processing large datasets in a single batch can lead to delays, system overloads, and inefficiencies. On the other hand, real-time processing is designed to handle continuous streams of data, breaking them into manageable chunks and processing them as they arrive.

If your data pipelines are becoming unmanageable and your batch processes are taking longer and longer to run, it’s time to shift to a real-time architecture. Real-time systems allow you to scale as data volumes grow, ensuring your business operations remain smooth and efficient.

4. Operational Bottlenecks in Data Pipelines

Batch processing systems can create bottlenecks in your data pipeline, where data piles up waiting for the next scheduled processing window. These bottlenecks can cause delays across your organization, especially when multiple teams rely on the same data to perform their functions.

For example, a finance team waiting for overnight sales reports to run forecasts, a marketing team waiting for campaign performance data, or an operations team waiting for stock updates can all face unnecessary delays due to batch processing constraints.

With real-time systems, data flows continuously, eliminating these bottlenecks and ensuring that teams have access to the insights they need, exactly when they need them. If your teams constantly wait for data to do their jobs, it’s time to break free of batch and move to real-time processing.

5. Business Use Cases Demand Continuous Insights

Certain business use cases simply cannot function without real-time data. These include fraud detection, dynamic pricing, predictive maintenance, and real-time monitoring of IoT devices. Batch processing cannot support these use cases because it relies on processing data after the fact – by which point, the window to act has often already closed.

Take fraud detection as an example. Identifying and preventing fraudulent transactions requires real-time monitoring and analysis of incoming data streams in banking. A batch system that only processes transactions at the end of the day would miss the opportunity to block fraudulent activity in real-time, exposing the business and its customers to significant risks.

If your business expands into use cases requiring immediate action based on fresh data, batch processing will hold you back. Real-time systems provide the continuous insights needed to support these advanced use cases and unlock new growth opportunities.

Making the Transition from Batch Processinto Real-Time

Transitioning from batch to real-time processing is a significant shift, but it pays off. Moving to real-time systems, you can respond instantly to customer needs, operational challenges, and market changes. You’ll also future-proof your organization, ensuring you can scale with growing data volumes and stay competitive in an increasingly real-time world.

If you see one or more of these signs in your business – delayed decisions, lagging customer experiences, overwhelmed data pipelines, or a need for continuous insights, it’s time to act. Although leaving batch processing behind may feel daunting, it’s a necessary step to meet the demands of modern business and thrive in a real-time world.

The sooner you make the move, the sooner you can start capitalizing on the benefits of real-time systems – faster decisions, happier customers, and a more agile business. So, are you ready for real-time? The signs are all there.

With Apache Flink 2.0 fast approaching, and as a companion to our recent blog, “What’s Coming in Apache Flink 2.0?” I thought I’d look back on some of the impactful updates we’ve seen since it was released in 2014. Apache Flink is an open-source, distributed stream processing framework that has become a cornerstone in real-time data processing. Flink has continued to innovate since its release, pushing the boundaries of what stream and batch processing systems can achieve. With its powerful abstractions and robust scalability, Flink has empowered organizations to process large-scale data across every business sector. Over the years, Flink has undergone a fantastic evolution as a leading stream processing framework. Let’s dive into some history with that intro out of the way.

1. Introduction of Stateful Stream Processing

One of Apache Flink’s foundational updates was the introduction of stateful stream processing, which set it apart from traditional stream processing systems. Flink’s ability to maintain application state across events unlocked new possibilities, such as implementing complex event-driven applications and providing exactly-once state consistency guarantees.

This update addressed one of the biggest challenges in stream processing: ensuring that data remains consistent even during system failures. Flink’s robust state management capabilities have been critical for financial services, IoT applications, and fraud detection systems, where reliability is paramount.

2. Support for Event Time and Watermarks

Flink revolutionized stream processing by introducing event-time processing and the concept of watermarks. Unlike systems that rely on processing time (the time at which an event is processed by the system), Flink’s event-time model processes data based on the time when an event actually occurred. This feature enabled users to handle out-of-order data gracefully, a common challenge in real-world applications.

With watermarks, Flink can track the progress of event time and trigger computations once all relevant data has arrived. This feature has been a game-changer for building robust applications that rely on accurate, time-sensitive analytics, such as monitoring systems, real-time recommendation engines, and predictive analytics.

3. The Blink Planner Integration

In 2019, Flink introduced the modern planner (sometimes referred to as Blink), which significantly improved Flink’s SQL and Table API capabilities. Initially developed by Alibaba, the Blink planner was integrated into the Flink ecosystem to optimize query execution for both batch and streaming data. It offered enhanced performance, better support for ANSI SQL compliance, and more efficient execution plans.

This integration was a turning point for Flink’s usability, making it accessible to a broader audience, including data engineers and analysts who preferred working with SQL instead of Java or Scala APIs. It also established Flink as a strong contender in the world of streaming SQL, competing with other frameworks like Apache Kafka Streams and Apache Beam.

4. Kubernetes Native Deployment

With the rise of container orchestration systems like Kubernetes, Flink adapted to modern infrastructure needs by introducing native Kubernetes support in version 1.10, released in 2022. This update allowed users to seamlessly deploy and manage Flink clusters on Kubernetes, leveraging its scalability, resilience, and operational efficiency.

Flink’s Kubernetes integration simplified cluster management by enabling dynamic scaling, fault recovery, and resource optimization. This update also made it easier for organizations to integrate Flink into cloud-native environments, providing greater operational ability for companies adopting containerized workloads.

5. Savepoints and Checkpoints Enhancements

Over the years, Flink has consistently improved its checkpointing and savepoint mechanisms to enhance fault tolerance. Checkpoints allow Flink to create snapshots of application state during runtime, enabling automatic recovery in the event of failures. Conversely, savepoints are user-triggered, allowing for controlled application updates, upgrades, or redeployments.

Recent updates have focused on improving the efficiency and storage options for checkpoints and savepoints, including support for cloud-native storage systems like Amazon S3 and Google Cloud Storage. These enhancements have made it easier for enterprises to achieve high availability and reliability in mission-critical streaming applications.

6. Flink’s SQL and Table API Advancements

Flink’s SQL and Table API have evolved significantly over the years, making Flink more user-friendly for developers and analysts. Recent updates have introduced support for streaming joins, materialized views, and advanced windowing functions, enabling developers to implement complex queries with minimal effort.

Flink’s SQL advancements have also enabled seamless integration with popular BI tools like Apache Superset, Tableau, and Power BI, making it easier for organizations to generate real-time insights from their streaming data pipelines.

7. PyFlink: Python Support

To broaden its appeal to the growing data science community, Flink introduced PyFlink, its Python API, as part of version 1.9, released in 2019. This update has been particularly impactful as Python remains the go-to language for data science and machine learning. With PyFlink, developers can write Flink applications in Python, access Flink’s powerful stream processing capabilities, and integrate machine learning models directly into their pipelines.

PyFlink has helped Flink bridge the gap between stream processing and machine learning, enabling use cases like real-time anomaly detection, fraud prevention, and personalized recommendations.

8. Flink Stateful Functions (StateFun)

Another transformative update was the introduction of Flink Stateful Functions (StateFun). StateFun extends Flink’s stateful processing capabilities by providing a framework for building distributed, event-driven applications with strong state consistency. This addition made Flink a natural fit for microservices architectures, enabling developers to build scalable, event-driven applications with minimal effort.

Conclusion

Since its inception, Apache Flink has continually evolved to meet the demands of modern data processing. From its innovative stateful stream processing to powerful integrations with SQL, Python, and Kubernetes, Flink has redefined what’s possible in real-time analytics. As organizations embrace real-time data-driven decision-making, Flink’s ongoing innovations ensure it remains at the forefront of stream processing technologies. With a strong community, enterprise adoption, and cutting-edge features, Flink’s future looks brighter than ever.

Every second, billions of data points pulse through the digital arteries of modern business. A credit card swipe, a sensor reading from a wind farm, or stock trades on Wall Street  – each signal holds potential value, but only if you can catch it at the right moment. Stream processing frameworks enable organizations to process and analyze massive streams of data with low latency. This blog explores some of the most popular stream processing frameworks available today, highlighting their features, advantages, and use cases. These frameworks form the backbone of many real-time applications, enabling businesses to derive meaningful insights from ever-flowing torrents of data.

What is Stream Processing?

Stream processing refers to the practice of processing data incrementally as it is generated rather than waiting for the entire dataset to be collected. This allows systems to respond to events or changes in real-time, making it invaluable for time-sensitive applications.
For example:
Fraud detection in banking: Transactions can be analyzed in real-time for suspicious activity.
E-commerce recommendations: Streaming data from user interactions can be used to offer instant product recommendations.
IoT monitoring: Data from IoT devices can be processed continuously for system updates or alerts.
Stream processing frameworks enable developers to build, deploy, and scale real-time applications. Let’s examine some of the most popular ones.

Apache Kafka Streams

Overview:

Apache Kafka Streams, an extension of Apache Kafka, is a lightweight library for building applications and microservices. It provides a robust API for processing data streams directly from Kafka topics and writing the results back to other Kafka topics or external systems. The API only supports JVM languages, including Java and Scala.

Key Features:

• It is fully integrated with Apache Kafka, making it a seamless choice for Kafka users.
• Provides stateful processing with the ability to maintain in-memory state stores.
• Scalable and fault-tolerant architecture.
• Built-in support for windowing operations and event-time processing.

Use Cases:

• Real-time event monitoring and processing.
• Building distributed stream processing applications.
• Log aggregation and analytics.

• Kafka Streams is ideal for developers already using Kafka for message brokering, as it eliminates the need for additional stream processing infrastructure.

Apache Flink

Overview:
Apache Flink is a highly versatile and scalable stream processing framework that excels at handling unbounded data streams. It offers powerful features for stateful processing, event-time semantics, and exactly-once guarantees.

Key Features:

• Support for both batch and stream processing in a unified architecture.
• Event-time processing: Handles out-of-order events using watermarks.
• High fault tolerance with distributed state management.
• Integration with popular tools such as Apache Kafka, Apache Cassandra, and HDFS.

Use Cases:

• Complex event processing in IoT applications.
• Fraud detection and risk assessment in finance.
• Real-time analytics for social media platforms.

Apache Flink is particularly suited for applications requiring low-latency processing, high throughput, and robust state management.

Apache Spark Streaming

Overview:
Apache Spark Streaming extends Apache Spark’s batch processing capabilities to real-time data streams. Its micro-batch architecture processes streaming data in small, fixed intervals, making it easy to build real-time applications.

Key Features:

• Micro-batch processing: Processes streams in discrete intervals for near-real-time results.
• High integration with the larger Spark ecosystem, including MLlib, GraphX, and Spark SQL.
• Scalable and fault-tolerant architecture.
• Compatible with popular data sources like Kafka, HDFS, and Amazon S3.

Use Cases:

• Live dashboards and analytics.
• Real-time sentiment analysis for social media.
• Log processing and monitoring for large-scale systems.

While its micro-batch approach results in slightly higher latency compared to true stream processing frameworks like Flink, Spark Streaming is still a popular choice due to its ease of use and integration with the Spark ecosystem.

Apache Storm

Overview:
Apache Storm is one of the pioneers in the field of distributed stream processing. Known for its simplicity and low latency, Storm is a reliable choice for real-time processing of high-velocity data streams.

Key Features:

• Tuple-based processing: Processes data streams as tuples in real time.
• High fault tolerance with automatic recovery of failed components.
• Horizontal scalability and support for a wide range of programming languages.
• Simple architecture with “spouts” (data sources) and “bolts” (data processors).

Use Cases:

• Real-time event processing for online gaming.
• Fraud detection in financial transactions.
• Processing sensor data in IoT systems.

Although Apache Storm has been largely overtaken by newer frameworks like Flink and Kafka Streams, it remains an option for applications where low latency and simplicity are key priorities. It is being actively maintained and updated, with version 2.7.1 released in November 2024.

Google Dataflow

Overview:
Google Dataflow is a fully managed, cloud-based stream processing service. It is built on the Apache Beam model, which provides a unified API for batch and stream processing and enables portability across different execution engines.

Key Features:

• Unified programming model for batch and stream processing.
• Integration with Google Cloud services like BigQuery, Pub/Sub, and Cloud Storage.
• Automatic scaling and resource management.
• Support for windowing and event-time processing.

Use Cases:

• Real-time analytics pipelines in cloud-native applications.
• Data enrichment and transformation for machine learning workflows.
• Monitoring and alerting systems.

Google Dataflow is best for businesses already operating in the Google Cloud ecosystem.

Amazon Kinesis

Overview:
Amazon Kinesis is a cloud-native stream processing platform provided by AWS. It simplifies streaming data ingestion, processing, and analysis in real-time.

Key Features:

• Fully managed service with automatic scaling.
• Supports custom application development using the Kinesis Data Streams API.
• Integration with AWS services such as Lambda, S3, and Redshift.
• Built-in analytics capabilities with Kinesis Data Analytics.

Use Cases:

• Real-time clickstream analysis for e-commerce platforms.
• IoT telemetry data processing.
• Monitoring application logs and metrics.

Amazon Kinesis can be the most sensible option for a company already using AWS services, as it offers a quick way to start. 

Choosing the Right Stream Processing Framework

The choice of a stream processing framework depends on your specific requirements, such as latency tolerance, scalability needs, ease of integration, and existing technology stack. For example:

• If you’re heavily invested in Kafka, Kafka Streams is a likely fit.
• Apache Flink is an excellent choice for low-latency, high-throughput applications and works with a wide array of data repository types.
• Organizations with expertise in the cloud can benefit from managed services like Google Dataflow or Amazon Kinesis.

Conclusion

Stream processing frameworks are essential for extracting real-time insights from dynamic data streams. The frameworks mentioned above – Apache Kafka Streams, Flink, Spark Streaming, Storm, Google Dataflow, and Amazon Kinesis, each have unique strengths and ideal use cases. By selecting the right tool for your needs, you can unlock the full potential of real-time data processing, powering next-generation applications and services.