A comparative performance evaluation of Kinetica 7.2.3.2 and ClickHouse 25.10.1 on the TPC-DS SF-100
benchmark, run on identical hardware. Kinetica completed 100% of the 99 queries while ClickHouse
completed 62–66%, and ran 2.5×–16× faster across single- and two-node configurations — while
ClickHouse exhibited negative scaling. This study highlights why workload completeness and
distributed execution efficiency matter as much as raw scan speed.
Today we’re excited to share a new integration that brings the speed and flexibility of DuckDB
together with the real-time analytical power of Kinetica. By leveraging DuckDB’s Postgres
protocol extension alongside Kinetica’s support for the PostgreSQL wire protocol, you can now
query live Kinetica datasets directly from DuckDB — mixing real-time operational analytics with
local, interactive SQL. This unlocks an incredibly productive developer experience: use DuckDB as
your local analytics workbench, join Kinetica data with Parquet/CSV files, prototype queries
against live tables, and orchestrate workflows without writing custom connectors or glue code. Why
This MattersTools like DuckDB have transformed local, interactive analytics. Analysts and
engineers can explore datasets stored in many formats right from their laptop — fast and without
infrastructure overhead. But until now, extending that same ergonomics to live analytics
databases required someone to build and maintain a custom connector layer. Now, because
Kinetica natively speaks the Postgres wire protocol and DuckDB supports attaching
Postgres-compatible endpoints, you can treat Kinetica just like another data source in
DuckDB — no translation layer, […]
Logistics optimization is becoming more complex: multi-hop routing, diverse transportation modes,
SKU-level requirements, service constraints, and constantly shifting supply-demand conditions —
leaving many organizations stitching together multiple tools, custom code, and external solvers to
try to keep up. We think there’s an easier route… Kinetica’s Multiple Supply Demand
Optimization (MSDO) solver is a native, SQL-driven engine that computes optimal routing and
allocation across complex, multi-modal supply chains — matching supplies to demands while honoring
constraints such as transport modes, capacities, penalties, priorities, and detailed item
specifications. Classic MSDO focused on solving the optimal path from supply to demand in a single
step. In this article we’ll discuss recent updates including multi-step
optimization and specification-aware matching, that bring Kinetica’s hybrid
OLAP/Graph closer to becoming a complete, end-to-end logistics solver — capable of
modeling and executing complex multi-hop logistics flows through a single, concise SQL statement
inside the database. No more separate optimization engines. No external orchestration layers. No
custom Python pipelines — routing, constraints, specification matching, and even multi-step
optimization — all created/executed in a single platform. MSDO’s multi-modal […]
Can we use LLMs to interrogate massive amounts of real world data? We built a Model Context
Protocol (MCP) server for Kinetica, hooked it up to Claude and asked questions about a
104-million-row Foursquare Places dataset to test this. This post walks through what worked,
what didn’t, and why Kinetica + MCP might be one of the cleanest ways to build AI-native
interfaces for large data. Why You Should Use MCP + Kinetica Let’s first break down why you
should use Kinetica for your AI applications. MCP is engine-agnostic, but its usefulness depends
on the backend. Kinetica offers real-time, multimodal analytics that make MCP much more powerful
in practice. You can see our latest benchmarks for a more detailed performance comparison. We’re
proud of how Kinetica stacks up against ClickHouse, BigQuery, and SingleStore, especially when it
comes to complex SQL workloads on massive data. What we Built We built the Kinetica MCP server to
let LLMs like Claude interact directly with live Kinetica data. No plugins, wrappers, or […]
The rise of IoT has led to an explosion of real-time data—from logistics fleets to power grids to
factory floors. Sensors are constantly emitting data. This telemetry unlocks immense value:
smarter routing, predictive maintenance, and responsive infrastructure. But here’s the catch: this
data often arrives as deeply nested JSON, emitted either directly by the devices or through
aggregation gateways. Traditional databases choke on this complexity. They force teams to flatten
data upfront via ETL pipelines—slow, brittle, and ill-suited for real-time demands. ✨ Kinetica:
Real-Time Analytics on Raw Sensor Data Kinetica eliminates the need for external ETL. It ingests
raw nested JSON directly, stores it in native JSON columns, and lets you query it with standard
SQL—all at sub-second speeds. Let’s walk through how this works, using some sample sensor JSON
data. Sample Nested JSON Payload This one JSON object includes: This is a common telemetry
structure—rich, flexible, and hard to wrangle using traditional SQL. Step 1: Ingest the raw JSON
directly into Kinetica With Kinetica, you skip the […]
Introduction: The Limits of Embeddings and Graphs in Isolation Graph databases are powerful tools
for modeling relationships, but the connections between the nodes do not necessarily follow a
semantic intuition, or language rules (LLM). Meanwhile, embedding models – that transform general
knowledge graphs into vector embeddings using algorithms like word2vec or our recently published
concept[1] which computes and flattens many graph predicates over vector spaces–fail to accurately
capture both local and remote affinities represented by infinite-dimension graphs as
limited-dimension vectors. The main reason is that the relations are mere ad-hoc connections that
do not necessarily follow a pattern — however, the best pattern for query accuracy is the graph
itself. Hence, the reverse process, i.e., injecting vector similarities as connections to
the graph where its schema (ontology) is purpose built further improves graph’s abilities with far
reaching potentials – hops away relations are by definition much more reliably accurate as they
are rectified by connections from nodes that are proven to be similar. What if you could
combine […]
Introduction Most analytics platforms struggle to support the full range of operations required
for complex real-world problems. Spatial, graph, time series, and vector search capabilities are
often siloed into separate tools, forcing users to stitch together workflows across multiple
systems—creating inefficiencies, bottlenecks, and integration challenges. Kinetica eliminates this
friction by unifying these advanced analytics within a relational framework. Users can perform
deep analytical operations—such as computing isochrones, solving shortest paths, and aggregating
spatial zones—within a single SQL query, streamlining decision-making that would otherwise demand
multiple systems and custom engineering. This blog demonstrates how Kinetica’s built-in
capabilities enable high-speed reachability and coverage analysis with remarkable efficiency.
Figure 1 : The animation is created from the agglomerated isochrones from 500+ fire stations in
the Seattle area within 1,2,3,4,5 and 10 minutes successively by applying the SQL statement shown
in Figure 3. How long does it take for a fire truck to reach a certain location from all available
stations is an important question for many interested parties; for the home owner […]
A key challenge for any database, whether distributed or not, is the constant movement of data
between a hard disk and system memory (RAM). This data transfer is often the source of significant
performance overhead, as the speed difference between these two types of storage can be dramatic.
In an ideal scenario, all operational data would reside in memory, eliminating the need to read
from or write to slower hard disks. Unfortunately, system memory (RAM) is substantially more
expensive than disk storage, making it impractical to store all data in-memory, especially for
large datasets. So, how do we get the best performance out of this limited resource? The answer is
simple: use a database that optimizes its memory use intelligently. Prioritizing Hot and Warm Data
Kinetica takes a memory-first approach by utilizing a tiered storage strategy that prioritizes
high-speed VRAM (the memory co-located with GPUs) and RAM for the data that is most frequently
accessed, often referred to as “hot” and “warm” data. This approach significantly reduces the […]
With Rockset being sunsetted by September 30th, many of its customers are left in the lurch,
seeking a reliable alternative for their real-time data analytics needs. Kinetica stands out as
the optimal choice, engineered specifically for real-time data analytics. Why Choose Kinetica for
Your Rockset Migration? Kinetica leverages GPU acceleration to ingest millions of records and
execute complex OLAP, spatial, time series, graph analytics and vector search using SQL. Our
platform is designed to match or outperform Rockset in handling real-time data analytics and
similarity search, providing a seamless transition with enhanced performance. Start Free With
Kinetica Cloud in Just Minutes Create a staging S3 bucket Export Your Rockset Data to AWS S3 Load
Data From AWS S3 Into Kinetica Migrate Your Queries Need Help?
The release of ChatGPT marked a significant shift in how people interact with technology by
introducing a conversational mode of inquiry using natural language to surface insights. This
trend is now extending to enterprise analytics, as evidenced by OpenAI’s acquisition of Rockset.
The trend is clear: traditional BI tools and data science languages are giving way to natural
language and conversational interfaces. Real-time Multimodal Capabilities for AI Copilots
Kinetica’s database engine is uniquely suited for AI copilots because it excels in two critical
areas: analytical range and low-latency responses. Conversational inquiries can lead to
unpredictable and diverse types of queries. Kinetica supports a wide array of analytical tasks,
including spatial, OLAP, graph, time series, and vector search, ensuring comprehensive analytical
coverage required to support a conversation mode of inquiry. Additionally, when dealing with
enterprise-scale data, maintaining a conversational flow requires fast query responses. Kinetica’s
architecture, leveraging modern CPUs and GPUs, guarantees high-speed processing, allowing for
quick and seamless transitions from language to insight. Furthermore, the speed with which
[…]
Imagine waking up one morning to find that your local gas station has run dry, leaving you
stranded without fuel. This was the harsh reality for millions of Americans in May 2021, when the
Colonial Pipeline ransomware attack disrupted the fuel supply across the Eastern United States.
DarkSide, a group of hackers, infiltrated the pipeline's systems, causing widespread panic and
fuel shortages. This incident highlights the growing vulnerability of our energy infrastructure to
cyberattacks.
Kinetica, the GPU-powered RAG (Retrieval Augmented Generation) engine, is now integrating deeply
with NVIDIA Inference Microservices for embedding generation and LLM inference. This integration
allows users to invoke embedding and inferencing models provided by NIM directly within Kinetica,
simplifying the development of production-ready generative AI applications that can converse with
and extract insights from enterprise data. NIM packages AI services into containers, facilitating
their deployment across various infrastructures while giving enterprises full control over their
data and security. By combining these services with Kinetica’s robust compute and vector search
capabilities, developers can easily build data copilots that meet rigorous performance and
security requirements. The queries below show how you can connect Kinetica to NIM with just a few
SQL statements. A cybersecurity chatbot We are developing a cybersecurity chatbot using this
stack. The chatbot processes two types of sources: Given a user prompt, the chatbot interfaces
with tabular data sources and text-based knowledge from documents to provide an appropriate
response. The diagram below outlines the architecture of the […]
Until recently, pure vector databases like Pinecone, Milvus, and Zilliz were all the rage. These
databases emerged to meet a critical need: Large Language Models (LLMs) often require information
beyond their training data to answer user questions accurately. This process, known as Retrieval
Augmented Generation (RAG), addresses that need by fetching relevant text based information using
a vector similarity search. RAG powers applications like ChatGPT, enabling them to act on
information beyond their training, such as summarizing news and answering questions about private
documents. By storing and retrieving relevant information to augment an LLM’s knowledge, vector
databases played a crucial role in the first phase of Generative AI, facilitating the adoption of
tools like ChatGPT. So why are vector databases being replaced??? While some vector
databases will continue to provide value, they are increasingly being replaced by a new breed of
multimodal retrieval engines that offer more than just vector search. This shift is driven by the
limitations of vector databases in two critical areas: 1. Inability to […]
Every moment, trillions of entities—vehicles, stock prices, drones, weather events, and beyond—are
in constant motion. Imagine the vast opportunities and insights we could uncover by monitoring
these objects and detecting pivotal events as they unfold, in real time. Such a task demands an
analytical engine that can ingest high velocity data streams, execute sophisticated queries to
pinpoint critical events and insights, and deliver instantaneous results to be acted upon. This is
precisely the challenge you can address with Kinetica and Confluent. Kinetica is a GPU accelerated
database that excels in complex real-time analysis at scale, while Confluent, built upon Apache
Kafka, provides robust data streaming capabilities. Together, they forge a powerful architecture
that unlocks the full potential of streaming data. My aim with this blog is to demonstrate the
power of Kinetica and Confluent in action in three simple steps. You can try all of this on
your own by uploading this workbook into your free Kinetica instance. All of the data is open for
access. You will […]
We are thrilled to announce that Kinetica has now joined the Connect with Confluent Partner
program. This collaboration merges the unparalleled speed of Kinetica’s GPU-accelerated database
with the data streaming capabilities of Confluent Cloud, delivering insights on high-velocity data
streams in mere seconds. Why This Partnership Matters Confluent is at the forefront of streaming
data technology, offering best-in-class capabilities that make it an industry leader. Kinetica
enhances this proposition by ingesting these high velocity data streams and fusing them with
contextual data, enabling the execution of complex SQL queries – all in real time. This unlocks
opportunities for advanced analytics on real-time data feeds, setting a new standard for
immediate, data-driven insights. Fast Ingest Kinetica’s multi-head ingest is designed to handle
the volume and velocity of Kafka topics effortlessly. Its lockless architecture allows query
execution while data is being streamed in. Both of these features together slash data latency
significantly. Contextual Insights Together, Kinetica and Confluent create an ecosystem where data
is not just collected but is swiftly […]
You’ve seen how Kinetica enables generative AI to create working SQL queries from natural-language
questions, using data set up for the demonstration by Kinetica engineers. What about your
data? How can you make Kinetica respond to real SQL queries about data that belongs to you,
that you work with today, using conversational, natural-language questions, right now? You’re
about to see how Kinetica SQL-GPT enables you to have a conversation with your own data. Not
ours, but yours. With the built-in SQL-GPT demos, the data is already imported, and the
contexts that help make that data more associative with natural language, already entered.
When your goal is to make your own data as responsive as the data in our SQL-GPT demos, there are
steps you need to take first. This page shows you how to do the following: STEP 1: Import
your Data into Kinetica Kinetica recognizes data files stored in the following formats: delimited
text files (CSV, TSV), Apache Parquet, shapefiles, JSON, and GeoJSON [Details]. For Kinetica
to […]
I think one of the most important challenges for organizations today is to use the data they
already have more effectively, in order to better understand their current situation, risks, and
opportunities. Modern organizations accumulate vast amounts of data, but they often fail to
take full advantage of it because they struggle finding the right skilled resources to analyze it
that would unlock critical insights. Kinetica provides a single platform that can perform complex
and fast analysis on large amounts of data with a wide variety of analysis tools. This, I
believe, makes Kinetica well-positioned for data analytics. However, many analysis tools are
only available to users who possess the requisite programming skills. Among these, SQL is
one of the most powerful and yet it can be a bottleneck for executives and analysts who find
themselves relying on their technical teams to write the queries and process the reports. Given
these challenges Nima Neghaban and I saw an opportunity for AI models to generate SQL based on
natural […]
Prior to the emergence of machine learning, and particularly “deep learning,” I was an ML
skeptic. Judging from what I saw from the state of the art at the time, I’d say there was no
way to program a CPU or a GPU — each of which, after all, is just a sophisticated instance of a
Turing machine — to make it exhibit behaviors that could pass for human intelligence. It seemed
like a sensible enough stance to take, given that I spent the bulk of a typical work week
translating ambiguous requirements from customers into unambiguous instructions a computer could
execute. Algorithmic neural networks had been around since the 1950s, yet most AI algorithms
had been designed to follow a fixed set of steps with no concept of training. Algorithms are
sets of recursive steps that programs should follow to attain a discrete result. While machine
learning does involve algorithms at a deep level, what the computer appears to learn from ML
typically does not follow any […]
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