By analyzing this data, insights can be gleaned about vehicle performance, predictive maintenance, energy efficiency, and even real-time traffic conditions, leading to the development of intelligent features like adaptive cruise control, predictive maintenance alerts, and advanced driver-assistance systems (ADAS). Moreover, the potential extends beyond the vehicle itself, as this data can be leveraged to create new services, such as usage-based insurance, fleet management solutions, and smart city initiatives that rely on traffic and environmental data from connected vehicles. The proliferation of sensors in automobiles has given rise to a data-driven automotive ecosystem that promises to revolutionize how we interact with and benefit from our vehicles.

Vehicle sensor data is characterized by its sheer volume and fast-moving nature, making it a dynamic and high-frequency data source. It comprises a continuous stream of readings, each associated with a timestamp and geospatial coordinates in the form of longitude and latitude. The timestamp provides a chronological context, allowing for the tracking of events over time, while the geographical coordinates offer critical location-based insights. This combination of time and location data is essential for understanding not only what is happening with a vehicle but also where and when it is occurring. 

For example, consider this abstracted view of a suspension reading. The camber of the left front tire is one of many suspension readings, along with caster, toe, and thrust angle. The measurement changes and is recorded every second, along with the changing location of the vehicle. A simple use case on this data would be to detect potholes, their depth, and the range of speeds in which the potholes were hit by drivers. Such information could be used to avoid these hazards and provide municipalities with insights to help with prioritizing road maintenance.  

Yet, many auto manufacturers are currently storing and analyzing vehicle telemetry data using tools that primarily focus on time-series data, often lacking advanced spatio-temporal capabilities such as InfluxDb or Spark. InfluxDB, a time-series database, has traditionally been used by some auto manufacturers despite disclaimers that the “geo package is experimental and subject to change at any time” and “risky.” Spark currently offers only rudimentary spatio-temporal capabilities, and efforts within the Apache ecosystem, such as GeoMesa and Sedona, aimed at addressing these limitations have not yet delivered comprehensive solutions. 

Using a time-series database for vehicle telematics data that is inherently both time-series and spatial in nature can result in a range of significant pitfalls. One of the most glaring issues is the loss of crucial information related to the location of vehicles. Telematics data often includes GPS coordinates, which are essential for tracking the precise whereabouts of vehicles, monitoring routes, and ensuring driver safety. In a time-series-only database, this spatial information is either completely discarded or treated in a primitive manner, reducing the ability to gain insights into the geospatial aspects of vehicle operations. This can severely limit the effectiveness of telematics applications, such as route optimization, geofencing, and real-time monitoring, all of which heavily rely on spatial data.

Furthermore, relying solely on a time-series database for telematics neglects the holistic nature of the data. Failing to incorporate the spatial dimension of the data means that critical insights, such as identifying the exact location of accidents or the proximity of vehicles to certain landmarks or destinations, are compromised. This can have far-reaching implications for fleet management, insurance assessments, and regulatory compliance, as these functions often require a nuanced understanding of the interplay between time and location. To harness the full potential of vehicle telematics data, a database should effectively capture and integrate both time-series and spatial elements, ensuring that no critical information is overlooked or underutilized.

Context matters, and when it comes to vehicle telemetry data, contextual data is fused not on primary keys but on spatial and temporal dimensions. Supporting temporal and geo-joins with telemetry data is crucial for unlocking a deeper understanding of complex systems and driving data-driven insights. For instance, combining vehicle telemetry data with road network information enables precise route optimization, real-time traffic management, and accident analysis. Similarly, integrating telemetry data with weather data empowers applications like dynamic weather-influenced routing, enhancing safety and efficiency. These temporal and geo-joins allow for a comprehensive analysis of data, facilitating the development of intelligent solutions that can significantly impact transportation, logistics, and safety-related decision-making.

Ford has emerged as a leader in the realm of Advanced Driver Assistance Systems (ADAS) through its groundbreaking BlueCruise system. A recent Consumer Reports article ranked Ford #1 in the industry (including Tesla that comes as a shock to many). While there are many facets contributing to this achievement, one standout reason is Ford’s innovative approach and unwavering dedication to constructing a cutting-edge data platform that excels in real-time location analytics. By harnessing both the temporal and spatial nature of vehicle data in a comprehensive and intelligent manner, Ford has managed to create a pioneering ADAS system that has set new standards in the automotive industry, making roads safer and driving experiences more comfortable for their customers.

Kinetica was designed from the ground up to excel at spatio-temporal analytics. The database platform offers a comprehensive set of tools that cater to the intricacies of vehicle telemetry data analysis. Kinetica is also at the forefront of leveraging Generative AI (Gen AI) for vehicle telematics by harnessing large language models to generate complex analytic code in plain english (aka NL2SQL) tuned for vehicle telemetry questions. Kinetica is also enabling real-time vector search on vehicle telematics to uncover previously hidden patterns leading to enhancements in safety, efficiency, and various other aspects of automotive technology.

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For decades, the realm of big data analytics has been a daunting landscape for most lay business users, navigable only by a team of specialists, comprising analysts, engineers and developers. They have been the gatekeepers. Setting up analytical queries and crafting data pipelines capable of wrestling out insights from immense volumes of data. Yet, the advent of Large Language Models (LLMs) has started to democratize this space, extending an invitation to a broader spectrum of users who lack these specialized skills.

At Kinetica, we’re not just building a database—we’re scripting the lexicon for an entirely new dialect of big data analytics. A dialect empowered by Large Language Models (LLM), designed to catalyze transformative digital dialogues with your data—regardless of its size.

In this article, I’ll unpack the salient features that make our approach unique and transformative.

A pioneering approach to the Language-to-SQL paradigm

There are several databases that have implemented a Language-to-SQL feature that takes a prompt written in natural language and converts it to SQL. But these implementations are not deeply integrated with the core database and they require the user to manage the interaction with the LLM.  

We have taken a different approach at Kinetica. An approach that is rooted in our belief that the future is one where the primary mode for analyzing big data will be conversational – powered by LLMs and an engine that can execute ad hoc queries on massive amounts of data. This requires that the SQL generated by the LLM is tightly coupled to the database objects that exist in the user’s environment without compromising security. 

To realize this vision, we have baked constructs directly into our database engine that allow users to interact more natively with an LLM. Through our SQL API users can effortlessly define context objects, provide few-shot training samples and execute LLM output directly.

-- A template for specifying context objects
CREATE CONTEXT [<schema name>.]<context name>
(
    -- table definition clause template
    (
        TABLE = <table name>
        [COMMENT = '<table comment>']
        -- column annotations
        [COMMENTS = (
            <column name> = '<column comment>',
            ...
            <column name> = '<column comment>'
            )
        ]
        -- rules and guidelines
        [RULES = (
            '<rule 1>',
            ...
            '<rule n>'
            )
        ]
    ),
    <table definition clause 2>,
    ...
    <table definition clause n>,
    -- few shot training samples
    SAMPLES = (
            '<question>' = '<SQL answer>',
            ...
            '<question>' = '<SQL answer>'
         )
)

-- A template for a generate SQL request
GENERATE SQL FOR '<question>'
WITH OPTIONS (
    context_name = '<context object>',
    [ai_api_provider = '<sqlgpt|sqlassist>',]
    [ai_api_url = '<API URL>']
);

A user can specify multiple cascading context objects that provide the LLM the referential context it needs to generate accurate and executable SQL. Context objects are first class citizens in our database, right beside tables and data sources. They function as a managerial layer between the user and the LLM, optimizing query results and performance. The existence of context objects creates a simplified operational workflow that ensures both speed and accuracy.

Hosted and On-Prem LLM services: Your choice, our delivery

At Kinetica, we believe that our users should have the freedom to choose. While we offer a private on-prem LLM service that is fine tuned to generate queries that use Kinetica functions, we have decoupled the LLM entirely from the database. And developers can use a  standard SQL interface with any LLM they want. This framework allows swapping LLMs based on your preference. This flexibility empowers businesses to select the service model that aligns with their requirements. You can therefore use Kinetica’s native LLM service or opt for a third party LLM like the GPT models from OpenAI.

That being said, to generate accurate and executable SQL queries, we still need to provide additional guidelines to an LLM to reduce its tendency to hallucinate. Using our SQL API developers can specify rules and guidelines that are sent to the LLM as part of the context. This helps the LLM generate queries that are performant and use the Kinetica variant of SQL where necessary.

Enterprise grade security

Kinetica is an enterprise grade database that takes data security and privacy seriously. A user interacting with an LLM can glean information using the context. We have therefore made context objects permissioned, giving database administrators full control over who gets to access and use context objects.

However, if you are using an externally hosted service like OpenAI, your context data might still be vulnerable. Customers who need an additional layer of security can use our on-prem native LLM service to address this concern. With Kinetica LLM your data and context will never leave your database cluster and you still get the full benefit of using an LLM that powers a conversation with your data.

Performance: It’s Not Just About Accurate SQL

Conversations require speed. You cannot have a smooth analytical conversation with your data if it takes the engine minutes or hours to return the answer to your question. This is our competitive edge. Kinetica is a fully vectorized database that can return queries in seconds. Queries that other databases can’t even execute. 

Apart from the obvious benefit of keeping conversation flowing by returning ad hoc queries generated by an LLM really fast. Speed also has a less obvious benefit – it helps to improve the accuracy of the SQL generated by the LLM. 

Because we are really fast, we can maintain logical views that capture joins across large tables or other complex analytical operations easily. This allows ‘context’ objects to feed semantically simplified yet equivalent representations of data to the LLM. This approach ensures that even the most intricate data models are interpreted accurately, thereby boosting the reliability of query results.

A database you can build on

Kinetica is a database for developers. Our vision is to enable conversations with your data – no matter the scale. We want developers to build the next generation of applications that harness the amazing potential of generative AI and the raw speed and performance of Kinetica and help us realize this vision.

Our native APIs cover – C++, C#, Java, JavaScript, Node.js, Python, and REST. You can use these to build tools that help users analyze data at scale using just natural language.

Conclusion

The future of conversational analytics rests on two pillars: the capability to convert natural language into precise SQL and the speed at which these ad-hoc queries can be executed. Kinetica addresses both, setting the stage for truly democratized big data analytics, where anyone can participate. 

Experience this right now with Kinetica.

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Data engineering, in this context, takes on various forms such as denormalization, indexing, partitioning, pre-joining, summarizing, and more. Put another way, data engineering exists to overcome the performance limitations of traditional databases. These techniques are employed to make data retrieval and analysis more efficient for anticipated queries. However, this approach falls short of the genuine ad-hoc flexibility that many users desire.

Over time, users of data have adapted to a model where their expectations have been managed to follow a somewhat linear process. This process typically involves documenting their data requirements, which are then prioritized among numerous other demands. Users may have grown accustomed to waiting for their specific set of questions to be answered by IT. However, the ever-evolving nature of business and the dynamic data landscape often means that users have moved on to new questions by the time their previous inquiries are addressed. This model, while once a standard approach, has become increasingly mismatched with the pace of today’s data-driven world, where insights need to be dynamic, immediate, and adaptable to rapidly changing needs. 

The core issue with this traditional approach lies in its rigidity. When new, unanticipated questions arise, which is quite common in dynamic business environments, users face significant obstacles. In such cases, the process of re-engineering the data to accommodate the new questions can be time-consuming, resource-intensive, and disruptive to ongoing operations. 

True ad-hoc capabilities should empower users to interact with data in a more natural, exploratory manner. This requires databases that can dynamically adapt to user inquiries without the need for extensive preparation. 

The immense power of modern Graphics Processing Units (GPUs) has ushered in a new era of data analytics by enabling data-level parallelism. Unlike traditional approaches that necessitate pre-engineering and indexing of data, GPUs excel at scanning through massive datasets swiftly and efficiently. By harnessing their parallel processing capabilities, GPUs can simultaneously perform operations on multiple data points, avoiding the need for extensive data restructuring. This not only accelerates query performance but also empowers users to engage in genuine ad-hoc analysis, enabling dynamic exploration of data without constraints.

Moreover, significant advancements in generative AI are revolutionizing the landscape of ad-hoc, novel queries by enabling natural language interfaces such as Kinetica’s SQL-GPT. These interfaces leverage cutting-edge language models to facilitate spontaneous and intuitive data interactions. Instead of adhering to rigid query structures, users can simply ask questions in natural language, promoting a more dynamic and exploratory approach to data analysis. By fostering a more inclusive environment, these interfaces are democratizing data exploration, and driving more demand for ad-hoc vs canned queries.  

While the idea of ad-hoc querying remains a common buzzword in the database industry, true ad-hoc capabilities are far from being the standard. Overcoming the limitations of traditional data engineering is crucial for empowering users to explore data freely and discover valuable insights without being confined to predetermined queries and structures.

Learn more about how you can do more with less with Kinetica. 

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Comparison: Kinetica compared to PostGIS, GeoMesa, Apache Sedona

Background

Geographic Information Systems (GIS) platforms have undergone a number of technological changes, from mainframe to SaaS and from hierarchical network structures to relational databases. The core technology designed around points, lines, and polygons has adapted to new underlying technologies making them faster and more flexible. However, the major GIS systems are still fundamentally built on the same designs from the 1980s and 1990s. While new tools and interfaces have taken advantage of new innovations, GIS continues to focus on building, managing, and analyzing data with a geographic component.

Beyond the borders of the GIS market, rapid growth in hardware, databases, and development tools offers new and fresh approaches to fundamental issues that have plagued GIS. One of the core issues is the extremely large sizes of geospatial data. From the complex curves of natural features to streaming location data over time from a moving device, GIS technology is not designed to efficiently handle such data. To address the needs of the most critical users of this data, specialty systems are offered. Kinetica is one such system. Built initially for the government to track high-value assets, it is now available to GIS users.

By leveraging the power of tools such as WMS, the power of big data viewing, query, and analysis is now available to GIS users. While many databases are adding spatial data types and functions, they are still based on underlying technology that was designed for non-spatial types. When making a technology decision, it is critical to carefully examine the fundamental designs of the systems. You will want a system that’s flexible, fast, scalable, and easy to use. Our guide to the different options can provide you with insight into the various choices.

The reach of GIS is being expanded through the development of new technologies and the integration of GIS with other systems. This is enabling GIS to be used to analyze and visualize larger and more complex datasets, and to provide decision-makers with the information they need to make better decisions.

Learn more about Kinetica with ArcGIS

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Numerous vendors have pledged this functionality, only to disappoint in terms of performance, accuracy, and smooth integration with current systems. Over-hyped solutions turned out to be painfully slow, causing frustration among users who expected conversational interactions with their data. To overcome performance issues, many vendors demo questions known in advance, which is antithetical to the free form agility enabled by generative ai.

Accuracy issues have been just as vexing, with wild SQL hallucinations producing bizarre results or syntax errors leading to answers that are completely incorrect. Enterprises and government agencies are explicitly banning the use of public LLMs like OpenAI that expose their data. And when it comes to integration, cumbersome processes introduce significant complexity and security risks.

Kinetica has achieved a remarkable feat by fine-tuning a native Large Language Model (LLM) to be fully aware of Kinetica’s syntax and the conventional industry Data Definition Languages (DDLs). By integrating these two realms of syntax, Kinetica’s SQL-GPT functionality has a deep understanding of the nuances specific to Kinetica’s ecosystem, as well as the broader context of how data structures are defined in the industry.

Accuracy

Kinetica’s model’s unique advantage lies in its training on proprietary syntax, enabling it to harness specialized analytic functions such as time-series, graph, geospatial, and vector search. This means that we can readily address complex queries such as “Show me all the aircraft over Missouri.” To execute this query, our model performs a sophisticated geospatial join, uniting the shape of a state with the precise locations of aircraft. Subsequently, it facilitates the visualization of these results on a map. This capability underscores our ability to handle intricate tasks beyond standard ANSI SQL efficiently, offering users valuable insights and visualizations for enhanced decision-making.

With Kinetica’s native LLM for SQL creation, we ensure unwavering consistency. While responses from OpenAI and other public LLMs may have previously functioned, they can unpredictably cease to work without clear explanations. In contrast, Kinetica provides a reliable guarantee that the responses we generate will remain functional over time, offering businesses and users the peace of mind that their SQL queries will consistently deliver the desired results.

In our approach to inferencing, we prioritize the optimization of SQL generation. OpenAI and other public LLMs are primarily tuned for creativity, often employing sampling for inferencing, which can lead to diverse but less predictable responses. To enhance the consistency of our SQL generation, we disable sampling. Instead, our model employs a more robust “beam search” technique, which meticulously explores multiple potential paths before selecting the most suitable response. This deliberate approach ensures that our SQL queries are not only consistent but also effectively tailored to the specific task at hand, delivering accurate and optimized results.

Performance

Vectorization and the utilization of powerful Graphics Processing Units (GPUs) play a pivotal role in the unparalleled performance achieved by Kinetica’s SQL-GPT. By harnessing the principles of vectorization, which involves performing operations on multiple data elements simultaneously, SQL-GPT can process and analyze data at an extraordinary speed. This compute paradigm allows Kinetica to quickly tackle complex SQL queries with multi-way joins, which is often required for answering novel questions. As a result, Kinetica SQL-GPT becomes an invaluable tool in data analysis, providing rapid insights and solutions to even the most intricate analytical challenges. Kinetica crushes the competition on independently verified TPC- DS benchmarks.

The speed achieved through GPU acceleration and vectorization in Kinetica is transformative in its implications. One of the key benefits is that it eliminates the need for extensive data pre-engineering to answer anticipated questions. Traditional databases often rely on indexes, denormalization, summaries, and materialized views to optimize query performance, but with Kinetica’s capabilities, these steps become unnecessary. The true allure of generative AI technologies lies in their capacity to enable users to explore uncharted territory by asking entirely new questions that might not have been considered before. This freedom to inquire and discover without the constraints of pre-engineering is a game-changer in the world of data analytics and insights, offering unprecedented opportunities for innovation and exploration.

Security

One of Kinetica SQL-GPT’s most compelling advantages is the ability to safeguard customer data. We provide the option for inferencing to take place within our organization or on the customer’s premises or cloud perimeter. This feature sets us apart, particularly in the context of large organizations where data security is paramount. Many such entities have implemented stringent security policies that have led to the prohibition of public LLM usage. Unlike with public LLMs, no external API call
is required, and data never leaves the customer’s environment. By offering a secure environment for inferencing, we empower organizations to harness the benefits of generative AI without compromising their data integrity or falling afoul of their security protocols, making us a trusted and preferred choice in the realm of AI solutions.

Smooth Integration

Kinetica’s data management strategies are designed to minimize data movement and maximize flexibility. One approach it employs is the use of external tables, which results in zero data movement. When a query is executed, it is pushed down directly into the database where the data resides, eliminating the need to transfer large datasets, thereby enhancing query speed and efficiency. Additionally, organizations can further optimize performance by caching data through Kinetica’s change data capture (CDC), a technique that boosts processing speed while removing complexity and minimizing data movement. Kinetica offers the convenience of pre-built connectors for a vast array of data sources, including Snowflake, Databricks, Salesforce, Kafka, BigQuery, Redshift, and many others.

Kinetica’s easy-to-use API for SQL-GPT makes it easy to build LLM apps. This API simplifies and streamlines the development of AI-driven solutions on internal enterprise data. Simply pass your question in natural language to the API and get the resulting answer back.

Give analysts and SQL laymen what they’ve always wanted: a platform to get answers at the speed of thought. Try with SQL-GPT today at www.kinetica.com/try.

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The Rise of GPUs in AI

GPUs, originally designed for rendering images and graphics in video games, found a new purpose when AI researchers realized their parallel processing capabilities could be harnessed for training complex neural networks. This marked the dawn of the AI revolution, as GPUs sped up training times from weeks to mere hours, making it feasible to train large-scale models on massive datasets. Deep learning and generative AI, both subsets of AI, rapidly became synonymous with GPU utilization due to the technology’s ability to handle the intensive matrix calculations involved in neural network training.

Beyond Model Training: A Paradigm Shift in Data Analytics

While GPUs revolutionized AI model training, their potential was not confined to this singular role. The parallel processing architecture that made GPUs so efficient for matrix computations in neural networks turned out to be highly adaptable to a variety of data analytic tasks. Turns out time-series, geospatial, and pretty much any analytic that involves aggregations benefit from matrix computations rather than serial computations. This realization marked a paradigm shift in data analytics, as organizations began to explore the broader capabilities of GPUs beyond the boundaries of deep learning.

GPUs enable vectorization – a computing technique that processes multiple data elements simultaneously. Instead of processing one data element at a time, vectorized instructions operate on arrays or vectors of data, executing the same operation on each element in parallel. This approach enhances processing efficiency by minimizing the need for iterative or scalar computations, resulting in significant performance gains for tasks such as mathematical calculations and data manipulations.

The Diverse Landscape of Data Analytic Workloads

As businesses recognized the efficiency and speed benefits of GPUs, they started applying them to a wide range of data analytic workloads. These workloads span various industries and use cases:

Real-Time Analytics

In today’s fast-paced business environment, real-time insights hold immense value. However, traditional CPU-based analytics often struggle to meet the demand for instantaneous results, leaving decision-makers lagging behind. This is where GPUs emerge to close the gap. With their exceptional parallel processing capabilities, GPUs are a force-multiplier on top of traditional distributed processing, enabling organizations to swiftly analyze data in real time. GPUs transcend the traditional trade-off between speed and metric sophistication; empowering organizations to achieve near-real-time insights even for highly complex calculations (figure 1). This advancement enables businesses to make better decisions faster, leading to enhanced outcomes and a deeper understanding of intricate data patterns that were previously challenging to analyze swiftly. An example of an early adopter is NORAD – The US Air Force who needed detect increasingly hard to discover threats in North American airspaces as fast as possible.  

Geospatial Analytics

The proliferation of spatial data has surged due to the abundance of location-enriched sensor readings, driving a transformative shift in how industries harness and analyze geospatial information. Industries reliant on geospatial data, such as logistics, telecommunications, automotive, defense, urban planning, and agriculture, face the complex challenge of analyzing vast datasets with intricate spatial relationships. Traditional CPU-based methods struggle to handle the complex joins between points and polygons, as well as polygons to polygons, often leading to incredibly sluggish performance. 

GPUs not only fuse massive geospatial datasets with remarkable speed but also excel at intricate spatial calculations, such as proximity, intersections, and spatial aggregations. GPU-based architectures gracefully handle spatial joins and complex geometry functions due to their ability to perform operations on entire arrays or sequences of data elements simultaneously. Spatial joins involve comparing and combining large sets of spatial data, a task perfectly suited for GPUs’ simultaneous processing of multiple data points. Additionally, GPUs’ high memory bandwidth efficiently handles the data movement required in geometry functions, enabling faster manipulation of complex spatial structures

An early adopter was T-Mobile, who said, “We had geospatial workloads supporting network gap analysis that could take months or even years to complete on the previous data stack.”

Time-Series Analytics

Time-series analytics, a cornerstone of industries like finance, healthcare, and manufacturing, finds a new dimension with the integration of Graphics Processing Units (GPUs). GPUs catapult time-series analysis to unprecedented levels of speed and sophistication, adeptly handling the high cardinality data that is typical in these scenarios. 

High cardinality refers to a situation where a dataset has a large number of distinct values in a particular column or attribute. In time-series analysis, high cardinality often arises because of timestamp or identifier fields, where each entry represents a unique time point or event. CPUs struggle with high cardinality joins because traditional CPU architectures process data sequentially, leading to significant overhead when performing operations involving large sets of distinct values. Joining datasets with high cardinality on CPUs requires extensive memory access and complex processing, which can result in performance bottlenecks and slower query execution times.

By swiftly crunching through intricate temporal data, GPUs uncover hidden patterns, empower accurate forecasting, and provide real-time insights on constantly changing readings. Early adopters like Citibank have been using GPUs to create more advanced and timely metrics used in transaction cost analysis as part of their real-time trading platform.

Language to SQL

Language-to-SQL (Structured Query Language) models are revolutionizing database interactions by enabling users to formulate complex database queries using natural language. These Language Model-based systems (LLMs) leverage advanced AI techniques to understand and translate human language into precise SQL commands. GPUs are not only used to build these LLMs, but they also play a pivotal role in accelerating the model’s training and inference processes. The parallel processing power of GPUs allows these LLMs to process large amounts of data more quickly, making the language-to-SQL conversion faster and more responsive, thus improving the user experience and expanding the range of practical applications for these systems.

While this capability is still new, leading healthcare, automotive, and airline companies are starting to pilot this capability on their proprietary data.

Kinetica: Harnessing GPU Power for Diverse Workloads

Kinetica was designed and built from the ground up to leverage NVIDIA GPUs to accelerate this wide range of analytics. Kinetica’s GPU-accelerated analytics not only enhance the speed of analysis but also enable users to derive insights from data in ways that were previously infeasible due to computational limitations.

By leveraging GPUs as the original design principle, Kinetica accelerates complex computations, enabling organizations to process and analyze massive datasets with unprecedented velocity. What sets Kinetica apart is not only native use of GPU acceleration but also its comprehensive suite of enterprise-grade features. Unlike other GPU databases, Kinetica stands out as a fully distributed platform that seamlessly integrates critical features such as high availability, cell level security, tiered storage, Postgres wireline compatibility, and advanced query optimization. This unique combination positions Kinetica as the go-to solution for enterprises seeking unparalleled speed, scalability, and reliability in their data analytics initiatives. The platform’s ability to seamlessly integrate with existing data ecosystems means organizations can leverage their existing data investments fully and minimize data movement. 

Kinetica achieved a pioneering milestone by being the first analytic database to integrate a Language Model-based system for SQL, seamlessly blending the power of AI-driven language understanding with robust data analytics capabilities. 

The evolution of GPUs from AI model training to a versatile data analytics workhorse is changing the landscape of industries worldwide. As organizations continue to explore the potential of GPUs, we can expect to see innovative solutions that solve complex problems in ways we never imagined. The journey of GPUs from AI model training to a cornerstone of data analytics is a testament to their adaptability and potential. 

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Compute Efficiency Through Vectorization

One of the primary ways Kinetica addresses environmental concerns is through its groundbreaking approach to compute efficiency. Traditional computing methodologies often require a vast number of processors to handle complex data analytics tasks, resulting in significant energy consumption and heat generation. Kinetica’s revolutionary vectorization technology, however, is turning this paradigm on its head.

Kinetica was designed from the ground up to leverage parallel compute capabilities of NVIDIA GPUs and modern ‘vectorized’ CPUs. Vectorization is a computing technique that processes multiple data elements simultaneously, enhancing performance by exploiting parallelism within modern processors. This approach is significantly more efficient than traditional methods that process data one element at a time, as it minimizes the need for repetitive instructions and better utilizes the hardware’s capabilities, leading to faster computations and reduced energy consumption.

This innovative compute paradigm accelerates query speeds by orders of magnitude (see latest TPC-DS benchmarks), which in turn enables a material reduction in the number of nodes required.  A top 5 bank was able to reduce their node counts from 700 nodes of Spark to 16 nodes of Kinetica.  A top 3 retailer went from 100 nodes of Cassandra to 8 nodes of Kinetica. A top 5 pharmaceutical company was able to retire 88 nodes of Impala with only 6 nodes of Kinetica. 

By optimizing the processing of data through vectorization, Kinetica radically reduces the number of processors needed to perform intricate analytical tasks. This not only translates to impressive speed and performance gains but also leads to substantial energy savings. With fewer processors in operation, the energy required for power and cooling is significantly reduced, resulting in a notable reduction in the company’s carbon footprint.

Kinetica’s Role in Transforming Real Estate and Energy Sectors

Kinetica is actively contributing to energy optimization through advanced analytics tailored for energy and real-estate companies. The integration of sensors into energy exploration and real estate management has revolutionized these fields by instrumenting every facet of operations. The convergence of data analytics and energy management has allowed Kinetica to develop powerful tools that help these industries make smarter decisions for resource utilization and energy consumption.

In the energy sector, Kinetica’s advanced analytics provide invaluable insights into patterns of energy demand, enabling companies to optimize energy distribution, minimize waste, and enhance overall operational efficiency. These analytics help energy providers shift towards greener and more sustainable energy sources, thus aligning with the global shift towards renewable energy solutions.

Similarly, Kinetica’s analytics solutions are transforming the real estate industry. By analyzing data related to building usage, occupancy patterns, and environmental conditions, Kinetica’s technology empowers real estate companies to optimize building performance, reduce energy consumption, and create more eco-friendly spaces. This not only benefits the environment but also enhances the value proposition for sustainable and energy-efficient properties.

Kinetica’s commitment to environmental sustainability goes beyond mere lip service. Through our efficient vectorization technology and advanced analytics solutions, Kinetica is playing a pivotal role in reducing energy consumption, optimizing resource utilization, and helping industries transition towards more sustainable practices.

The post Kinetica’s Contribution to Environmental Sustainability: Pioneering Energy Efficiency in the Data Center and in the Field appeared first on Kinetica - The Real-Time Database.

However, as the world embraces the era of the Internet of Things (IoT) and data becomes more accessible across time and space, traditional GIS systems like ArcGIS encounter challenges in accessing and managing these voluminous datasets. This is where innovative databases like Kinetica step in.

Kinetica is purpose-built to handle large streaming and historical databases efficiently, while also offering built-in spatial capabilities, making it an ideal solution for geospatial big data challenges. The seamless integration of Kinetica with ArcGIS empowers users to bridge the world of GIS with the immense potential of big data. This marks a significant leap forward for organizations seeking to derive meaningful insights from the vast amounts of data they collect.

Imagine the power of connecting Kinetica with ArcGIS. These two systems come together to handle real-time workloads on massive, streaming datasets without any data aggregation or duplication. By setting it up yourself, you bypass the need for expensive professional services to build a custom solution. Plus, it works seamlessly on both CPU and GPU-based systems.

Once connected, you’ll dive into a comprehensive visual analytics environment within your ArcGIS system. Effortlessly process, analyze, and display spatial data with precision. Kinetica’s unique lightning-fast visualization leverages WMS layers that seamlessly integrate with ArcGIS. Picture the ability to visualize millions, or even billions, of points on a map in real time. These active layers dynamically update to reflect the ever-changing data streams in Kinetica. Enhance your analysis by replaying historical events.

Unleash the potential when Kinetica and ArcGIS join forces. Experience the transformative capabilities of these systems working in harmony.

Renowned organizations like the FAA, Ford, T-Mobile, and others embrace the power of Kinetica to conquer complex spatial and time-series challenges. With its cutting-edge design, Kinetica effortlessly handles massive, streaming data sets, even with billions of records. Let’s dive into its key strengths:

• Extensive support for fast geo-joins like contain, within-a-distance, intersect, overlap, and more
• Advanced geospatial analytic functions like st_*, stxy_*, entity tracks, and more
• Graph matching and solving capabilities
• Native Kafka integration and distributed ingest for real-time processing
• Web Mapping Service (WMS) and Vector Tile Support for server-side rendering of heatmaps, contours, tracks, unique value, and classbreaks on billions of records

The opportunities to bring new insights and value to your organization by combining traditional GIS with spatiotemporal big data services are virtually limitless. These often siloed datasets, together enable data enrichment, analysis, and aggregations.

Below are some examples:

Telecommunication: The vast quantity of spatiotemporal data available to telecommunication organizations can be used to gain valuable insights into customer behavior, network performance, and market trends. By examining where and when customers use their services, these companies can better anticipate demand, tailor offerings to customer needs, and optimize network infrastructure. This utilization of spatiotemporal data can also aid in identifying potential areas of expansion and in strategizing effective marketing campaigns.

Insurance: Tracking extreme events in real-time to allocate resources quicker, and accurately track usage parameters for better rates, faster claim processing, fraud detection, and risk assessment and prediction.

Supply Chain: Demand forecasting requires analyzing large data set across time and location including historical sales data, market trends, and competition to better predict future demand. Data on asset locations, such as warehouses, distribution centers, and retail stores, over time helps optimize logistics, and make real-time decisions to meet changing demands or potential disruptions.

Traffic and Transportation Management: Utilizing spatiotemporal big data enables real-time traffic monitoring, which includes identifying congestion areas and optimizing transportation routes. This helps in effectively optimizing traffic systems, leading to improved efficiency and smoother traffic flow.

Oil & Gas: Combining historical data with live sensor data, petroleum companies are leveraging spatiotemporal analytics for oil exploration, drilling/well analytics, pipeline monitoring and maintenance, supply chain optimization and predictive maintenance of critical assets.

Retail: The retail industry is utilizing spatiotemporal big data to improve operations, enhance the customer experience, and increase sales by analyzing foot traffic patterns, and customer behaviors. Store locations can be optimized, selected, or closed based on complete customer behavior data and deeper demographic trends while optimizing inventory management, and leveraging geolocation-based marketing.

Environmental Monitoring and Analysis: Integrating spatial and temporal data allows scientists and researchers to study environmental factors like air quality, water pollution, climate patterns, and natural disasters, in order to track changes and identify trends.

Smart Cities and Urban Planners: Leveraging spatiotemporal big data, city planners can see population movements by time of day and combine it with land use, weather, energy consumption, and more, allowing them to optimize city resources. As patterns change due to climate change, cities can quickly adjust to ensure livability for their citizens.

Precision Agriculture: The combination of spatial data (e.g., satellite imagery, soil composition maps) and temporal data (e.g., crop growth, weather conditions) quickly enables farmers to optimize irrigation, fertilizer usage, and farming practices, maximizing crop yield while minimizing environmental impact.

Emergency Response and Disaster Management: Integrating spatial and temporal data aids response teams in assessing the severity of disasters, identifying affected areas, and allocating resources swiftly and effectively.

Electric Network Management: With spatiotemporal big data, managing distribution networks becomes more efficient by analyzing consumption patterns, adjusting systems to load changes, identifying potential failures or inefficiencies, and improving reliability.

Public Health Monitoring: Spatiotemporal analysis plays a pivotal role in tracking the spread of infectious diseases, identifying high-risk areas, and strategizing targeted interventions. It equips health organizations to respond promptly and efficiently to outbreaks and epidemics.

These examples give a hint of the tremendous power of integrating spatial and temporal data to unlock valuable insights, facilitate proactive decision-making, and drive progress across various fields.

Let us show you how you can work with your own large datasets in ArcGIS today. Book a Demo

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Primary Purpose:

Databricks is primarily known for its unified analytics platform that integrates Apache Spark for big data processing and machine learning tasks. While Databricks does support a plug-in for GPU-accelerated processing for certain machine and deep learning workloads, its design center is focused on CPU based data engineering and data science on large-scale data processing tasks. Databricks supports SQL through Spark SQL, and has streaming capabilities, but neither of which is optimized for the GPU. Typically, the GPU use case for Databricks involves the creation of a model in batch. Think, building a model to predict customer churn or build a new large language model (LLM).

Kinetica is specifically designed as a high-performance GPU-accelerated database platform. Its primary focus is on delivering real-time data analytics and insights through the power of NVIDIA GPUs. Kinetica’s main use cases often involve interactive contextual analytics on streams of sensor and machine data utilizing time-series, geospatial, and graph analysis, especially for applications where low-latency access to data is crucial. Every part of Kinetica is natively optimized for the GPU, including ANSI SQL, stream processing, and hundreds of analytic primitives for graph, spatial, time-series, and vector search that leverage the power of the GPU. Think, detecting threats in our airspace, spotting buying or selling stock trading opportunities in real-time, optimizing a 5g network, energy management and exploration, or re-routing a fleet of vehicles to achieve on-time delivery.

Core Technology and Architecture:

Databricks heavily relies on Apache Spark, an open-source distributed computing framework, which allows users to process and analyze large-scale datasets in parallel. While Databricks does provide some support for running Spark workloads on GPUs, it is not fully optimized for GPU-centric processing as Kinetica. As with any plug-in approach, Databricks’ GPU integration can have performance overhead, compatibility issues, security risks, lack full functionality, require extra integration effort, and receive more limited support and maintenance.

Kinetica is built from the ground up to fully harness the power of GPUs. Kinetica’s seamless integration with NVIDIA GPUs incorporates CUDA. Its database architecture is specifically designed to take advantage of GPU parallelism, allowing it to perform complex data operations significantly faster than traditional CPU-based databases. See independently verified benchmarks.

Community:

Databricks has a significant user base and a robust ecosystem due to its integration with Apache Spark and support for various programming languages like Python, Scala, and SQL. It benefits from a broader community and extensive library support for machine learning models.

While Kinetica may not have as large of a user community as Databricks, it has built a strong presence in certain industries and domains that require high-performance real-time analytics on constantly changing data, such as finance, telecommunications, Department of Defense, and IoT (Internet of Things) applications such as Citibank, TD Bank, T-Mobile, Verizon, NORAD, FAA, Lockheed Martin, and Ford.

In sum, Databricks and Kinetica both leverage the GPU for advanced analytics but in different ways that address different use cases.

Check out Kinetica on NVIDIA GPUs on AWS, Azure, or Kinetica Cloud.

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Time stamped data.

This is the domain of spreadsheets and traditional database layouts.  Sometimes, these relational tables use temporal SQL. But to be clear, temporal tables are not time-series data. 

Times series data  

An example of one array record is:

07/02/23, 0.110000077, -0.2663, 0.0408,0.9785, -0.0004, 0.0058, 0.120000084, -0.2631, 0.03749, 0.973359, -0.0004, 0.0058, 0.130000127, -.24411.017982, 0.940882, -0.0028, 0.0021, 

+200 more measurements.

Reformatted it might look like this table:

Time series data can fill entire memory banks with measurements and time-stamp separators. After the first few records, the human eye says “I’m leaving” –but the computer loves it!  Time-series data often arrives in 10s or 100s of terabytes.  Today, with cloud computing, anyone can grab 20 or 100 compute servers for 30 minutes.  The power to number-crunch 500 terabytes of data is at our fingertips. Big data indeed! 

Big data from the 2010s is small compared to what programmers are wrestling with in the 2020s. Volumes are up as are real-time use cases. Consider Boeing and Airbus. They collect petabytes of airplane sensor data annually. Every industry is analyzing bigger data.  According to G2 analysts[i] “In 2023, we will generate nearly 3 times the volume of data generated in 2019. By 2025, people will create more than 181 ZB of data. That’s 181, followed by 21 zeros.”  Data is arriving faster too.  Confluent’s State of Data in Motion Report found that “97% of companies around the world are using streaming data… More than half of this group (56%) reported revenue growth higher… than their competitors” [ii]

Time Series Analysis Use Cases

Time series analysis began long ago with stock markets and risk analysis. Today it is vital for the Internet of Things, fraud detection, and dozens of other workloads. While many of these use cases may sound familiar, the data and algorithms are far from it. 

Wrestling with Complexity

Time series analysis involves sophisticated mathematics applied to huge arrays of data. Hence, there are dozens of time series algorithms from universities, GitHub, and vendors.  Kinetica was an early adopter of “bring your own algorithm” integration. Today its simple to run your favorite algorithms in parallel. Here are a few examples of bring-your-own-algorithms (BYOA):

ARIMA: The Auto Regressive Integrated Moving Average can summarize trend lines. It then can predict highs and lows that might occur. Its flexible and outstanding at forecasting sales trends, manufacturing yields, or stock prices. This is a popular algorithm to get started. 

Shapelets are a category of algorithms that smooth out the sawtooth pattern measurements.  There are many times when a 0.03% value change is irrelevant – especially with sensor data.  Shapelets make it easier to compare and correlate data streams. 

Multivariate time series classification algorithms detect correlations from different measurement scales. We can’t compare wind speed and propeller temperature numbers on a small airplane.  But we can see correlations between the graphs which the multivariate classification detects.

Vector similarity search is the latest breakthrough in time-series analysis.  Similarity-based searches do rapid pattern matching and data analysis.  This accelerates cyber-security, recommendation engines, image, and video searches. A key benefit is comparing time-series data points that have no relationships keys or name-identifiers.  Matches can be similar instead of identical data.  Vector similarity search enables proactive monitoring, predictive maintenance, and real-time anomaly detection.

Kinetica’s Best Fit with Time-Series Analysis

Kinetica is a time-series database with several capabilities that accelerate time-series analytics. These are:

Window functions: Built-in sliding windows help correlate events that occur in close time-proximity.   It’s easy to set the start and width of the window. Within the window, numerous algorithms can find important events or results.  For example, moving averages can detect which four or more events are causing price surges or machine failures. The time-period can be minutes, weeks, even months. We can then compare the sliding average to different points throughout the year.

Data loading: Kinetica set out to speed-load data was a necessity for their first few customers. Some of them required Kafka data streams loaded and analyzed within 10 seconds. This was foundational to Kinetica’s earliest product development.  Kinetica has three features for faster data loading:

• Kinetica database ingests direct into the cluster.  Data doesn’t pass through a central point of control. This scales better, has no single point of failure, and new nodes can be added without disruption.
• Kinetica’s native Kafka integration accesses Kafka topics using Kafka Connect.  Once a stream begins, there are minimal latencies for Kafka writing streams to disk. 
•  Kinetica’s lockless architecture avoids the heavy overhead of conflict resolution in many database engines.  We use versioning and optimistic concurrency control to ensure consistency and isolation. This enables faster processing and less contention for resources in analytical workloads. 

High Cardinality Joins: Time-series data often involves joining dozens of data streams with unique values together.  Multi-server parallel clusters are essential to handle big data high cardinality joins. What is high cardinality? Cardinality is high when a table column has thousands or millions of unique values. A table column containing only “Yes” or “no” has a cardinality of two. The receipt total for the 37 million who shop at Walmart every day is near 37M cardinality.   Joining two tables on high cardinality columns is computationally intensive.  It’s often an intolerable meltdown. Every value in the first table must be compared to every value in the other table for a match.  Imagine table-A has 100,000 unique values and Table B has 50,000,000 unique values. The computer must then match 100,000 * 50,000,000 memory cells –a mere five trillion comparisons.[iii] 

Why are high cardinality joins common in time-series data?  Start with time itself which is often stored in hours-minutes-seconds-and-milliseconds.  There is enough drift in clock ticks to make most every time value unique for months.  Other data with wildly unique values includes temperatures, identification numbers, stock prices, airflow, ocean currents, traffic patterns, and heart monitors.  Bigdata joins are inescapable.  Kinetica’s super-power comes to the rescue.   

GPUs and Intel AVX performance boosts: Vector processing in GPUs or Intel CPUs is perfectly suited for high cardinality joins.  Kinetica leverages hardware vectorization in GPU devices or Intel AVX instructions.  Start up a few cloud instances of NVidia A10s to exploit 9,200 optimized CUDA cores. It’s a clear competitive advantage for you and Kinetica. You might have time to run down the hall for a coffee refill. Performance of this intensity accelerates your productivity.  It also enables jobs to be rerun to improve analytic accuracy.

Kinetica finishes complex joins long before programming languages and other software tools.  You will want Kinetica software to do that for you. A table join of 20 terabytes to 50 terabytes doesn’t have to mean waiting hours for results.  The alternative is slow performance and many days of manual work-arounds.

Running Your Code in Parallel: Another foundational experience taught Kinetica engineers that customers wanted to use any algorithms. The data scientists had compelling reasons. Kinetica developers made it easy to bring-your-own-algorithm.  Your algorithms becomes a User Defined Function (UDF).  Kinetica software runs your UDF code in parallel, a benefit not found in many DBMS.  There are UDFs available on Github for data cleansing, data transformations, parsing, aggregations, and –best of all– data science.  There are many good UDF tutorials on Kinetica web pages.  Most developers can integrate their favorite algorithms easily.  Many years of Kinetica enhancements are accessible here.

Bonus:  The human eye is a powerful analytic tool.  People see graphical data patterns in an instant.  So Kinetica renders gigabytes of graphical data fast and condensed.  We then send it to visualization tools like Tableau or PowerBI.  That means Kinetica won’t overload the business intelligence servers or the network.  The user can quickly spot anomalies and start drilling into the discovery. 

Summary

Kinetica fits well into time-series data preparation and analysis. It helps with understanding the dataset and its context.  It accelerates data exploration, data munging, analytics, and feature engineering.  Each of these tasks requires multiple processing steps. These tasks involve detailed work and are often time consuming. Time-series data also drives the data analyst to make a lot of complex decisions.  This includes selecting the right tools and methods. When Kinetica provides these functions, the entire data preparation is done in situ.  There’s much less data movement –a time consuming low value task.  Furthermore, Kinetica functions enable preparation of big data time-series analysis. Bring your own algorithms!  Kinetica speeds all these things up. Including you.

[i] https://www.g2.com/articles/big-data-statistics
[ii] https://www.confluent.io/data-in-motion-report/
[iii] https://www.kinetica.com/blog/dealing-with-extreme-cardinality-joins/

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