SingleStore

In this discussion, Kannan D.R., Chief Architect at LiveRamp, explains to Sarung Tripathi, SingleStore's VP of Worldwide Field Engineering, how LiveRamp utilizes SingleStore to enhance their operations: ?? Unify data for the first time ever in a single database, providing LiveRamp's customers with seamless access to all relevant data in one place ?? Augment data stored in Snowflake, Databricks, Google BigQuery and all sources as an operational data store that delivers major performance and TCO advantages ?? Drive his team's zero-copy read objective by eliminating unnecessary data movement and analyzing data in place ?? Support LiveRamp's corporate initiatives around clean energy and reducing its carbon footprint This demonstrates how LiveRamp leverages SingleStore to drive efficiency, sustainability, and innovation in data management. Watch the full video here ?? https://bit.ly/4gYuhLj #SingleStore #LiveRamp #BigQuery

2?? prominent solutions in this field, Elastic and SingleStore, offer distinct approaches to integrating AI into search. Elastic’s Elasticsearch Relevance Engine (ESRE) offers zero-shot search, delivering AI-powered relevance without extra training. In contrast, SingleStore focuses on flexibility, enabling organizations to customize AI models for their specific needs. Dive into this blog to explore these approaches, examining their benefits, limitations, and ideal use cases ?? https://bit.ly/4dHukbD #SingleStore #ZeroShot #AI

Website refreshes and CMO shelf lives are famously short-lived but customer feedback like this outlives everything. Thank you ?? Jack Ellis and the Fathom team. #marketing #growth SingleStore

Generative AI is taking the world by storm, but as SingleStore's CEO, Raj Verma, puts it, if AI is the car, data is the engine. And that engine only runs smoothly when data is used in the right context and accessed instantly. That's where SingleStore comes in – the only platform that lets you transact, analyze, and search data in real-time, unlocking the full potential of your data-driven future. ?? ?? Check out the full story in a fireside chat with Shradha Sharma, Founder and CEO, YourStory ?? https://bit.ly/4gZgnsi via YourStory Media. #SingleStore #YourStory #AI #DataPlatform

SingleStore seamlessly supports generative AI by enabling data reads and writes for both training and real-time tasks—without the complexity or data movement across multiple tools. Large Language Models (LLMs) can be tough on many systems due to their size, but SingleStore boosts LLM performance to meet real-world needs. ? In this demo, our Technical Evangelist Akmal Chaudri loads tick data into SingleStore using Kafka, runs queries via LangChain & Natural Language, and even integrates voice interaction with a chatbot that responds in speech. ?? Check it out, with a bonus section in the Notebook! ?? Watch the full demo here ?? https://bit.ly/3XVMSPs #SingleStore #LLM #GenAI

SingleStore is set to unveil a groundbreaking integration of our real-time data and analytical engine within #watsonxdata ?? This powerful collaboration will elevate the IBM lakehouse ecosystem with a feature-rich database infrastructure, giving you a unique competitive edge over other lakehouses. Imagine unparalleled access to real-time data streams seamlessly integrated with your entire lakehouse repository! ? What does this mean for you? ?? Enhanced Performance: Experience unmatched scalability in handling data at scale. ?? Reduced Query Latency: Get faster insights when you need them most. ?? Heightened Concurrency: Support more users without breaking a sweat. ?? Expedited Data Ingestion: Bring data in faster and more efficiently than ever! ?? Register today: https://ibm.co/4dJ5VT3 and swing by booth # 862. See you soon! ??

?? Imagine an intelligent application that harnesses petabytes of data from your lakehouse or warehouse, blending it with real-time data for actionable insights. Think of it as a stock trading app or a cybersecurity alert system that accelerates decision-making. ?? Discover how you can leverage SingleStore's Native App within Snowpark Container Services for real-time analytics without ever moving your data. This blog post will show you how to achieve zero-ETL ingestion with Iceberg tables and seamlessly connect Kafka streams for real-time insights—all while maintaining full ownership of your data. ?? ?? Read more to explore how this integration can transform your analytics https://bit.ly/4eHqJf6 #SingleStore #RealTimeData #Kafka #Snowflake

You don't need a specialized database to build powerful #RAG apps. The database you're already using is enough if it supports both data types [vector data and knowledge graph data]. RAG can be implemented using either a database that supports vectors and semantic search or a knowledge graph. In particular, vector databases facilitate efficient search and similarity functions, enhancing the RAG process by enabling quick setup for applications involving unstructured and structured data. Each offers distinct advantages and methodologies for information retrieval and response generation. The goal of both approaches remains the same: to retrieve contextually relevant data/information for the user’s query. RAG with a vector database involves converting input queries into vector representations/embeddings, and performing vector searches to retrieve relevant data based on their semantic similarity. The retrieved documents go through an LLM to generate the responses. This approach efficiently handles large-scale unstructured data and excels in contexts where the relationships between data points are not explicitly defined. In contrast, RAG with a knowledge graph uses structured relationships and entities within the graph to retrieve relevant information. The input query searches within the knowledge graph, extracting relevant entities and their relationships. This structured data is then utilized to generate a response. Knowledge graphs are handy for applications requiring a deep understanding of the interconnections between data points, making them ideal for domains where the relationships between entities are crucial. Both approaches can be integrated with a SingleStore database; you can use it as a vector database and also for knowledge graphs. Here is my step-by-step practical guide on how to enhance your RAG applications using Knowledge Graphs: https://lnkd.in/gk34DvAs

Learn how to integrate Hasura and SingleStore to build powerful data access APIs along with best practices for ensuring security and scalability in API development next week. In this session, we’ll explore how you can leverage Hasura's GraphQL engine and SingleStore's high-performance data processing capabilities to create robust and scalable APIs. Learn best practices for securing your APIs, optimizing for performance, and handling complex queries at scale. You’ll learn: ?? How to integrate Hasura and SingleStore to build powerful data access APIs. ?? Best practices for ensuring security and scalability in API development. ?? How to optimize your APIs for real-time data processing. ?? A live demonstration of creating secure, scalable APIs for modern applications. Register today ?? https://bit.ly/3NcciDi

Ready to elevate your career? ?? Become part of the expanding community of data professionals who have discovered new opportunities through SingleStore Certification! If you're aiming to boost your skills, build your credibility, or advance your career in the dynamic field of data management, our certification program is designed for you. ?? What you’ll gain: ? In-depth knowledge of SingleStore ? Hands-on experience with real-world applications ? A prestigious certification to boost your resume Sign up today and take the next step toward your professional growth. ?? Learn more and get certified: https://bit.ly/3BtdGz2 #SingleStore #CertificationProgram #RealTimeDatabase