Decodable

Generative AI has gotten a lot of attention for its potential to change the ways we live and work. Being the engine of that car, LLMs are at the core of most GenAI applications and power everything from helping non-technical employees query data to auto-generating important meeting notes and action items for your current business roadmap. But like any engine, what you get out of it depends on what you put in. The quality of the output always boils down to the freshness of the data feeding the model. If your model is only trained on public, static data, it will fall short where it matters most, leaving you with: 🧨Stale, inaccurate responses prone to hallucination 🧨Lack of business-specific context 🧨Limited real-time awareness Without mechanisms to incorporate real-time data, even the most advanced models produce outdated or inaccurate responses. That makes them unreliable in fast-changing environments, especially in use cases that demand timely, context-aware outputs. There are many examples of where pre-trained LLMs fail for context-dependent use cases, including: 💥 E-Commerce personalization 💥 Customer support automation 💥 Fraud detection 💥 AI-powered search Building real-time AI pipelines doesn't have to mean spinning up complex infrastructure or managing low-level streaming tools. Decodable offers a fully managed platform that helps teams ingest, transform, and route data in motion, so your AI systems can operate with the most current and reliable context possible. We can help you to: 🎯 Provide your teams with a reliable stateful stream processing platform 🎯 Execute AI-ready data pipelines developed in SQL, Java, or Python 🎯 Deliver scalable, cost-efficient, and secure real-time data AI models need more than raw data, they need real-time context. Decodable provides stateful stream processing to power LLMs and other AI systems so they can detect patterns across streams, maintain state across events, and respond to real-time business signals with precision. https://dcdbl.co/4iRFlKU

Discover how Drata went from prototype to a production AI application in just two months with the help of Vellum and Decodable. Watch the on-demand tech talk for the full story 👉 https://dcdbl.co/3Y1ZchM

As industries demand more intelligent, autonomous decision-making, AI is evolving beyond general-purpose models to agentic AI. These systems combine real-time data with domain-specific context to drive continuous, adaptive learning. Our latest guide dives deep into how integrating real-time data can elevate agentic AI systems to new levels of autonomy, intelligence, and efficiency. Download now and learn how to: 🎯 Harness Real-Time Data: Unlock the full potential of AI by integrating continuous data streams for smarter, faster decision-making. 🎯 Drive Autonomous Decision-Making: Empower AI agents to optimize and act independently, reducing the need for manual intervention. 🎯 Overcome Common AI Challenges: Explore how to manage data pipelines, ensure high-performance infrastructure, and make the leap to next-gen AI maturity. https://dcdbl.co/4jc55RQ

Join Kevin, Anita, and Sharon to learn how Drata is delivering real-time context with streaming data to enhance AI accuracy. Watch the on-demand tech talk for the full story 👉 https://dcdbl.co/3Y1ZchM

It's time to transform how you handle real-time data. Join us for a virtual hands-on lab and we'll teach you how to: 💡 Stream Postgres changes to OpenSearch. 💡 Denormalize data for faster insights. 💡 Perform real-time joins with streaming SQL. Sign up here: https://dcdbl.co/3Wj9JnX

Discover how Drata went from prototype to a production AI application in just two months with the help of Vellum and Decodable. Watch the on-demand tech talk for the full story 👉 https://dcdbl.co/3Y1ZchM

AI doesn’t just need data—it needs the right context to make intelligent, informed decisions. A generic large language model (LLM) understands the world broadly, but that alone is not enough to create a useful AI system. For AI to be truly effective, it must be equipped with domain-specific knowledge, such as company policies, product details, and service guidelines. It also needs user-specific data, including purchases, support interactions, and behavioral patterns. Most critically, AI must be aware of the real-time state of things—current transactions, inventory levels, and other dynamic variables that affect the decision it needs to make at that moment. There are a wealth of solid options for building the foundation of useful AI applications, which start with a foundational model, trained on vast amounts of general data. This provides a broad understanding of language and common knowledge, but it lacks the ability to personalize responses or make situationally aware decisions. To actually make the responses relevant, businesses must layer in organization-specific data: information about the company, its products, policies, and how it operates. This retrieval-augmented generation (RAG) approach allows AI to move beyond generic responses and begin functioning as a domain expert, able to answer questions and perform tasks within a specific environment. But that’s just the first additional layer of context required. The next critical layer is customer-specific history. Your agentic AI solutions must be able to recognize and adapt to individual users based on their past interactions. What has this customer bought before? Have they had support issues in the past? What are their preferences, concerns, or ongoing engagements? This information allows AI to provide personalized and relevant assistance instead of treating every user like a complete stranger. Without this, even an advanced AI is nothing more than a chatbot that repeatedly asks for the same information, frustrating users rather than helping them. Finally, the most crucial piece is real-time awareness. No matter how much historical data and business context an AI has, it cannot make informed decisions and offer intelligent responses without knowing what’s happening in the moment. A customer might be attempting to complete a purchase, disputing a charge, or needing immediate support for a failed transaction. AI must be able to access and process real-time information—what the customer just tried to do, what system errors occurred, or whether their credit card was just declined. Without this layer, the AI is responding based on outdated assumptions, leading to responses that are not just unhelpful, but often actively misleading. Read the full article to learn more, including a practical path to success. https://dcdbl.co/4c3vyi8

The DataStream API is one of Flink's core APIs, offering access to low-level capabilities with explicit control over various stream processing concepts and aspects such as data transformations, state management, and time handling. It is well-suited for developers who require fine-grained control over data flows by implementing custom partitioning, complex windowing, or custom stateful processing. Written in Java or Python, DataStream API code is a good choice for applications demanding high-performance, event-driven data processing. For those who prefer a more structured, relational approach to stream processing, the Table API provides a high-level abstraction that represents data streams as dynamic tables. It enables users to perform common operations like filtering, joining, and aggregating data by means of a domain-specific language (DSL) which resembles the convenience of SQL. The Table API is often used when developers need to process structured data while maintaining the ability to integrate with lower-level APIs when required. It also supports both Java and Python, and bridges the gap between the imperative nature of the DataStream API and the declarative nature of SQL. In fact, SQL can also be used within Table API code for more conciseness whenever reasonable. And if you are already deeply familiar with SQL and prefer a purely declarative approach, Flink jobs can be expressed in pure SQL using ANSI compliant syntax which makes it very approachable to work with both real-time streaming data and historical batch data in the same way. Under the covers, Apache Calcite is used for query planning and optimization. Flink SQL is particularly beneficial for data engineers and analysts alike who want to interact with streaming data without the need for writing a single line of Java or Python code and can therefore be considered an excellent choice for building streaming or batch-based ETL pipelines. These APIs represent a comprehensive offering that enables developers to build stream processing applications at varying levels of abstraction and ease of use. Whether you need fine-grained control, structured transformations, SQL-based analytics, or stateful event-driven applications, Flink has got you covered. Ultimately, the API choice depends on the specific use case, the complexity of the application, and the developer’s familiarity with stream processing concepts and languages. Read more for a tangible example of combining the strengths of Flink’s DataStream API and Flink SQL to create a multi-stage, real-time data pipeline. https://dcdbl.co/4l2PjKC

Medidata moved thousands of siloed batch pipelines to a centralized, scalable platform powered by Flink. Watch the on-demand tech talk for the full story 👉 https://dcdbl.co/4gDouJX

As Eric Kavanagh put it in our Predictions for 2025 tech talk, we are at an incredible moment of transformation: a double inflection point for streaming and AI. While AI often grabs the spotlight, Eric makes a bold prediction: by 2025, streaming will have a greater impact. Why? ✅ Streaming is more mature. ✅ Its use cases are clear and transformative. ✅ A greenfield streaming architecture is like "Valhalla to the Vikings"—a once-in-a-lifetime opportunity to solve problems and unlock innovation. 💡 Ready to explore why streaming might outshine AI in the coming years? Don’t miss the on-demand replay of this insightful session 👉 https://dcdbl.co/3ZGZSth