AI Database - Open source and SQL

Webinar

Angsuman Kar from Axcess.io showing their Agent 1952 demo

From Data to Decisions: How GenAI Supports Time-Series Monitoring and Manual Interventions

Manufacturing with IoT (Industrial IoT or IIoT) faces several challenges when it comes to costs of poor quality and reducing time to action—the time it takes for data to be collected, analysed, and acted upon. Join CrateDB and Axcess.io in this webinar to explore how “Agent 1952” solutions that enable GenAI integration can enhance operational efficiency by real-time data and data-driven insights.

Demo

Harnessing CrateDB’s Multi-Model Capabilities for AI-Powered Applications

In this video, we explore the integration of CrateDB and PyCaret to detect anomalies in machine data, crucial for identifying potential failures or inefficiencies in technological systems. CrateDB's capability for handling large-scale data with ease pairs seamlessly with PyCaret's low-code approach to machine learning, offering a streamlined path to uncovering insights within vast datasets.

Webinar

Digital-Twins-and-Generative-AI-Digital-Innovation-Summit-2024

Digital Twins & Gen AI on Azure

Explore how TGW, a global leader in logistics automation, digitally transformed warehouse operations using Azure. This session delves into the creation of automated warehouses and LLM-based internal Q&A system, answering general questions of employees, providing deep insights based on technical documentation and support tickets, and streamlining sales support.

Webinar

The Transformative Effects of Real-Time AI

Businesses face challenges with diverse and vast data, siloed information, complex database technologies, and high costs, hindering AI insights. However, large language models and generative AI are transforming data analysis. This keynote will discuss how modern multi-model databases tackle these data management issues.

Webinar

Stefan Asanin from CrateDB talking about AI strategies and technologies for IoT

Harnessing AI for IoT: Strategies and Technologies for Data Empowerment

Discover how a modern and sustainable approach to AI and IoT, built on principles of context, unification, and flexibility, can empower your business. Learn how companies are leveraging cutting-edge technologies to manage and analyze massive IoT datasets in real time, unlocking intelligent decision-making and operational efficiencies.

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RAG Pipelines Explained

Retrieval Augmented Generation (RAG) pipelines are a crucial component of generative AI, enhancing the model's ability to generate accurate and contextually relevant content. RAG pipelines operate through a streamlined process involving data preparation, data retrieval, and response generation.

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Architecture of AI Knowledge Assistants

The overall architecture of a knowledge assistant usually consists of four parts: Context Data, LLM Gateway, Chatbot, as well as Monitoring and Reporting.

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Understanding Generative AI

Generative AI refers to a collection of artificial intelligence techniques capable of creating new content derived from the training data they have been fed with, combined with additional context provided by users. This content can encompass text, code, images, audio, and video.

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Vector data with CrateDB

CrateDB maximizes the potential of vector data with a single, scalable database that can be queried with SQL and streamlines data management, significantly reducing development time and total cost of ownership.

Documentation

KNN_MATCH function

The knn_match function uses a k-nearest neighbour (kNN) search algorithm to find vectors that are similar to a query vector.

Documentation

FLOAT_VECTOR data type

A float_vector type allows to store dense vectors of float values of fixed length.

It supports KNN_MATCH for k-nearest neighbour search. This allows you to find vectors in a dataset which are similar to a query vector.

Documentation

Machine Learning applications and frameworks to use with CrateDB

Learn how to integrate CrateDB with machine learning frameworks and tools, for MLOps and Vector database operations.

Vector storage

With CrateDB's vector store, you can easily store and retrieve embeddings generated by AI and ML models, seamlessly integrating vectorized data with your existing datasets. It allows you to enrich your existing data with semantics, providing context that aligns with your data and enhancing explainability.

Advanced search capabilities

CrateDB offers advanced search capabilities through its similarity search and flexible filtering, combining full-text and vector search. Similarity search allows users to find similarities across any data represented as vectors, while the combination of full-text and vector search improves the search precision by enhancing semantic similarity and keyword matching. These features facilitate enhanced recommendations, anomaly detection, and other AI/ML use cases.

Ingestion

CrateDB can ingest, process and analyze multi-structured data and dynamic schemas – relational tables, complex objects and arrays, time-series, geospatial, and textual data. It can easily ingest millions of records per second, through data streaming or batches, and from various data sources, such as Amazon S3 and Kafka.

Native SQL support

CrateDB is a distributed database that implements native SQL and the PostgreSQL Wire Protocol. With CrateDB, you can easily query even complex and dynamic schemas in a familiar SQL format, without the need to learn custom languages. The massive parallel execution of queries ensures fast response times, making it ideal for handling ad-hoc queries across large datasets, including those commonly encountered in AI/ML applications.

Ecosystem

CrateDB seamlessly integrates with your AI/ML stack (LangChain, MLflow, PyCaret ...) and analytics stack (Tableau, PowerBI ...) by leveraging the support of the PostgreSQL Wire Protocol.

View a sample list of integrations >

Reduced TCO

CrateDB offers a low Total Cost of Ownership (TCO) by eliminating the need to manage multiple systems. It seamlessly integrates your data, keeping your (meta-)data and vector representations aligned without the complexity of data synchronization processes. With its use of native SQL, CrateDB simplifies development and ensures compatibility with existing systems.

Manufacturing

ABB Ability™ Genix optimizes operations and increases asset availability in industrial use cases by analyzing vast amounts of data in real-time. They use CrateDB to unlock the value of industrial data, working with advanced data analysis and data management capabilities. With a data ingestion rate of an 1 million values per second and event retrieval ranging from 30,000 to 120,000 events per second, ABB optimizes industrial efficiency and productivity.

"Working with CrateDB brings positive outcomes. The ingestion and throughput have very good performance, with 1 million values/sec, the horizontal scalability where we can add as many nodes as we need and the automatic query distribution across the whole cluster"

Marko Sommarberg
Lead, Digital Strategy and Business Development at ABB

Software platform

Qualtrics is the leader in experience management software. They use CrateDB to process open text survey responses and enable machine learning algorithms to be applied to the data to make it easier to gain insights from thousands of pieces of feedback per hour.

"CrateDB gives us ease of SQL combined with easy scaling, and real-time querying of full-text data."

A database for AI and ML is a specialized storage system designed to store, manage, and retrieve machine learning models and large datasets used in AI applications. These databases are optimized for high performance and scalability, enabling efficient handling of the vast amounts of data and computational requirements needed for AI and machine learning tasks. They often support advanced features such as vector store support, advanced search capabilities, and integration with an ecosystem of machine learning frameworks and tools.

Some examples of databases for AI/ML include CrateDB, Pinecone, Zilliz, Weaviate, and DataStax. CrateDB is an open-source distributed database designed for AI/ML use cases, efficiently managing diverse data types and ensuring real-time data accessibility for continuous model training and prediction. Watch to learn how CrateDB's multi-model database approach enables real-time AI insights >

You should use an AI/ML database when you're working with large datasets, complex machine learning models, or applications that require real-time data analysis and prediction. These databases are useful when you need to perform frequent training and updating of models, ensuring that your AI solutions remain accurate and effective over time. With vector store and similarity search features, you can use CrateDB to perform complex data analytics and pattern recognition.

To choose the right AI/ML database for your needs, consider the size of your data, the required processing speed, and the scalability necessary for future growth. Assess the complexity of your machine learning models and ensure the database integrates well with your existing tools and technologies. Moreover, evaluate the costs of licensing, storage, and computational resources. CrateDB is an ideal choice for diverse needs due to its support for vector search and similarity search functionality, seamless integration with AI/ML stacks, and reduced total cost of ownership (TCO).

Unlike traditional databases, AI/ML databases are optimized for handling large volumes of diverse data types and performing high-speed analytical queries required for complex AI/ML tasks. CrateDB offers high scalability and integrates seamlessly with various AI and ML frameworks and tools.

AI/ML databases provide several benefits:

High Speed: They are optimized for rapid data ingestion, processing, and retrieval, which is crucial for time-sensitive AI applications. CrateDB delivers millisecond response times for complex queries, providing real-time insights and responsiveness.
Scalability: Designed to scale horizontally or vertically, they can handle growing data volumes and increased computational loads without significant performance degradation. With its distributed shared-nothing architecture, CrateDB provides seamless scalability for hundreds of nodes with minimal operational effort.
Efficient Storage: They offer advanced data compression and storage optimization techniques, reducing storage costs and improving data access speed. CrateDB optimizes data storage by partitioning and sharding large tables, ensuring balanced data distribution and improved query performance.
Integrated Analytics: Many AI/ML databases support in-database analytics, allowing data scientists to build, train, and deploy models directly within the database environment, streamlining the workflow and reducing latency. CrateDB enhances this capability by providing advanced search functionalities and real-time querying, which enable data scientists to perform complex analyses and derive insights quickly within the same environment.

AI/ML Database

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