Success in today's economy depends on the ability to make informed decisions in real-time based on huge volumes of diverse data.
Innovative organizations were quick to make sense of semi and unstructured data by
adopting Artificial Intelligence (AI) and Machine Learning (ML) tool chains. These enable faster, more accurate decisions and forecasts to be made in cost-effective ways.
The greatest benefits of AI/ML are only realized when data is stored and made available to query as quickly as possible. Incoming data may be structured, unstructured, or binary and might be organized by primary key, as a time-series, or in geospatial format.
The challenge of managing diverse data
Traditional databases are suited to storage of finite data sets with well-defined schemas. Real-time applications demand different approaches more suited to the streaming,
less-structured nature of their data.
One way of handling this complexity is to adopt multiple specialized databases. Time-series data might be stored in a time-series database, JSON, or semi-structured objects require a document database. Embeddings for Vector Similarity searches might need a vector database.
While this approach improves the ability to ingest data at scale, it creates data silos. Combining document, time-series, and vector data in a query requires applications to read from three separate databases each having different data models, query languages, and APIs. This complexity leads to inefficient queries, makes aggregations across data types difficult, and adds operational overheads.
Application development velocity suffers when data is siloed. Developers must become proficient in different query languages, libraries, and tools for each database. Aggregations across multiple stores must be performed by application code, incurring runtime and code complexity penalties. This increases the cost of development, integration, and time to market.
Embracing multi-model databases
These architectural limitations can be overcome by adopting a multi-model database designed to support real-time AI. When structured and semi-structured data coexist with time-series and geospatial data on a unified platform, challenges associated with managing multiple specialized data silos are replaced with opportunities to innovate and lower costs.
When evaluating a multi-model database, ensure that it supports standards based search and query interfaces. The use of familiar syntax and protocols eases adoption, enables developer productivity, and ensures out of the box integration with large ecosystems of drivers, tools, and libraries. Choose a database designed to handle high volumes of data and rapid rates of ingestion associated with real-time event streams.
CrateDB is ideal for today's real-time AI solutions
CrateDB is an open source, multi-model and distributed database offering high performance, scalability, and flexibility. Combining the best of SQL and NoSQL databases with the full-text search capabilities of Apache Lucene, CrateDB ingests, stores, and indexes huge volumes of data in real-time.
CrateDB’s flexible storage handles semi-structured and evolving data schemas with ease. The adoption of the familiar standards-based ANSI SQL query interface and PostgreSQL Wire Protocol increase developer productivity. This focus on standards enables integration with a wide ecosystem of drivers, tools, and libraries including popular AI frameworks such as LangChain and machine learning lifecycle platform MLflow.
CrateDB clusters seamlessly scale across multiple nodes using commodity hardware on premise, in the cloud, or as a hybrid deployment. Data is ingested from multiple sources
including real-time event streams from Kafka or MQTT. CrateDB easily handles the receipt of millions of events per second. Distributed writes and indexing across a cluster ensure that new data can be queried in real-time.
ABB, the world technology leader in electrification and automation, uses CrateDB to optimize and manage the operation and availability of industrial assets by analyzing vast amounts of data in real-time. They ingest a million events per second whilst reading data at a rate of 30-120,000 events per second. ABB leverages CrateDB’s scalability, multi-model data storage, and advanced aggregation capabilities with its AI/ML models to predict equipment failures before they occur. Read the full story here.
CrateDB’s flexible deployment options include a managed Cloud service on AWS, Azure, or GCP, or self-deployed with multiple support options. AWS, Azure, and GCP customers can deploy CrateDB directly from their Marketplace. For additional resilience and distribution, CrateDB supports multi-cloud and hybrid deployments.
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