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Vertical-Specific

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### Trading Analytics
Users generally praise Kx's market surveillance and financial analytics capabilities. When it comes to trading analytics, reviewers highlight the platform's strength in handling vast datasets from the US Equity Options market, enabling efficient data ingestion, analytics, and calibration. The ability to merge historical and real-time data for trade and quote analysis is also commended for providing a detailed view of financial markets at a granular level. Additionally, users appreciate the platform's support in identifying alpha in equity and FX markets, enhancing electronic trading, market making, and client profitability analysis.

### Time Series Analysis
Users consistently praise KX for its robust time series analysis capabilities, emphasizing its ability to handle intricate transformations on large datasets with ease. The platform's unique temporal analytics features set it apart from other solutions, enabling users to derive valuable insights from historical and real-time data efficiently. Additionally, KX's integration with AWS further enhances its analytical power, particularly in the realm of financial services where real-time data analysis is paramount.

### Historical Data Analysis
Some users highlight KX's historical data analysis as a key strength, emphasizing its ability to provide valuable insights for business processes by combining real-time and historical data effectively. On the other hand, there are users who express challenges in leveraging the historical data analysis features of KX, suggesting a learning curve for maximizing its full potential. This mixed sentiment indicates that while KX offers robust historical data analysis capabilities, users' experiences and opinions on its usability may vary.

### High-Performance Computing
Users consistently highlight KX's high-performance computing capabilities as a key strength, emphasizing its speed, efficiency, and ability to handle complex operations with minimal code. The integration with AWS further enhances its performance, particularly in real-time analytics for financial services. Despite a small developer community, users find KX's technology to be powerful, flexible, and well-suited for high-speed data processing tasks.

### Query Optimization
Opinions on kdb+'s query efficiency vary among users. While some praise its highly efficient vector-based database technology and super-fast query performance, others express concerns about its single-threaded nature limiting concurrent querying capabilities. The consensus seems to be that kdb+ excels in handling large volumes of data with its built-in q language for complex logic, but the restriction to one user querying an exposed port at a time poses a limitation in certain scenarios.

### Performance Enhancements
Some users express optimism about the upcoming improvements in multithreading, anticipating significant benefits in the upcoming v4.1 release. However, concerns linger regarding the proprietary nature of Kx, hindering widespread adoption and integration with other tools. Despite these challenges, seasoned industry professionals appreciate the continuous evolution and enhancements in kdb+, recognizing its core technology and ongoing updates as valuable assets.

Lack of Simple Features: Some users have expressed dissatisfaction with the lack of simple features related to IPC, which they feel hinders their experience with the product. They believe that the product could benefit from additional features and functionalities in this area.

Learning Curve of q language: The learning curve of the programming language q has been mentioned as a potential downside by several reviewers. They feel that it can be challenging to grasp initially, although some users believe this concern is overstated and can be overcome with practice and available resources.

Limited Standard Tooling: Users have mentioned that the limited standard tooling provided by the product has led them to build more than necessary, which could have been done more efficiently with other systems. This limitation has caused frustration among some users who would prefer a wider range of pre-built tools and functionalities.

Comprehensive Analytics Solution: Users find KX's streaming analytics, data and analytics capabilities in a single solution to be unique and comprehensive. This has been mentioned by several reviewers who appreciate the convenience of having all their analytical needs met in one platform.
Ease of Use: The solution is praised for its ease of use, with users stating that analysis is simple, fast, and easy to share. Many reviewers have found the platform user-friendly and intuitive, allowing them to quickly perform complex operations without much effort.
Efficient Handling of Time Series Data: Users appreciate KX's ability to handle intricate time series transformations on large amounts of data efficiently. Several reviewers have specifically highlighted this feature as a strong point of the platform, enabling them to work with time series data effectively.
Overall, reviewers have consistently praised KX for its comprehensive analytics capabilities, ease of use, and efficient handling of time series data.

Users of KX software have found it invaluable for analyzing fast-moving data with historical context, enabling faster decision-making and providing greater insight for business processes. This has been particularly useful for post and pre-trade analysis, allowing users to work with large datasets from the US Equity Options market data feeds. The software has also been crucial for processing massive amounts of real-time data and deriving real-time insights in the fields of equity and FX. 

The ability to analyze vast volumes of data in real-time as well as historically has aided in a wide range of applications, including electronic trading, market making, client profitability analysis, trading analytics, TIC data analytics, and market abuse surveillance. KX software has been relied upon for its timeseries data handling capabilities, as well as its data quality, cleansing, and enrichment features. It provides high speed and scale for loading and enrichment workloads. 

In addition to these use cases, KX has been used to observe real-time trends in client-facing businesses and has proven invaluable for debugging large datasets. It has addressed challenges related to time series data storage, management, and analysis. The platform has also been utilized for building multi-asset trading analytics platforms and for financial data reporting and analytics. Furthermore, KX has facilitated the quick building and running of large simulations.

kdb+ is a time series database from kx headquartered in Palo Alto, California, a division of First Derivatives.

kdb+

Time Series Databases (TSDB) are designed to store and analyze event data, time series, or time-stamped data, often streamed from IoT devices, and enables graphing, monitoring and analyzing changes over time.

Fintan Quill: Intro to kdb+ and demo

Glenn Wright & Rebecca Kelly: Kx in the Public Cloud (AWS demo at 6 minutes, 25 seconds)

Kevin Holsgrove: Query Routing Demo

Nataraj Dasgupta: Patient ML app demo using Python, R & kdb+

kdb+ trader dashboards demo

Racing Drone Rolling Launch Control || IPC Implementation Demo (q/kdb+ EmbedPy and ML Toolkit demo)

Likelihood to Recommend

Likelihood to Renew

Usability

Support Rating

Implementation Rating

Kx Systems has a high-performance database, kdb+, with a built-in programming language, q, that sets the standard for time-series analytics. Acclaimed for its speed and performance in streaming, real-time and historical analysis of “Big Data” in the financial services, utility and pharmaceutical industries, Kx technology is increasingly being used by any business looking for a scalable computing solution.

Key words:"time-series programming language," NoSQL, NewSQL, "high-performance database", MapReduce, "Big Data analytics", "functional programming"; data management, "relational database."

Amazon Relational Database Service (RDS)

DataRobot

MATLAB

Microsoft Excel

kdb+ 2022-03-29 11:41:27

SingleStore

kdb+ 2016-01-08 08:37:54

OpenText Vertica

Apache Hadoop

Estimating Edge

Oracle Berkeley DB

Spotfire Streaming

Quant Strategist

kdb+ 2016-01-05 11:06:49

MySQL

NetBeans

kdb+ 2015-12-08 11:39:43

Bizview

kdb+ 2015-12-11 07:13:14

TrustRadius is a technology and business research firm based in Austin, Texas. The company is known as a review platform for verified B2B technology and software reviews through a proprietary algorithm and human verification.

TrustRadius

New users

Ability to write

Backwards compatibility

Complex logic

Difficult to maintain

Hard to read

License cost

Difficult to learn

Run time

Learning curve

Time series

File based

Easy to maintain

Processing power

Series data

Time series data

Extremely fast

Home

We use it for large-scale data collection, storage, analysis, and modeling. It has been game-changing in our ability to conduct research on large-scale datasets and has sped up our research pipeline and ability to share code across the team. This is for basic queries as well as more sophisticated AI work.

Time series analysis.
Large dataset storage.
Query re-use.

Run time error message readability, particularly for new users.
Backwards compatibility between versions.

Ability to get insights into a large production system.
Ability to handle data volumes greater than more basic databases / query languages.

Amazon Relational Database Service (RDS) and MATLAB

I work in the equities area, where we use kdb for most of our real time tick data requirements. Tick data in our case ranges from market data, real time analytics, live PNL and orders data etc.

It is really fast if chosen for the right problem.
Large logic can be shrunk into a tiny snippet.
It provides a column oriented database, where each column in a table is a vector. Thus you can perform very fast analytics using its vector processing power.

It is sometimes painful to accept the fact that KDB+ is not fully multithreaded.
The ability to write shorter code for a complex logic is really good. But it makes it really cryptic. Cryptic codes are very difficult to maintain and extend.
For some small institutions license cost is little high.

It perfectly solves most of our real time tick data needs.
Finding good kdb resources is slightly difficult. Also new people trying to learn kdb experience a relatively longer learning curve.

I've been using kdb+ in the context of storing and generating analytic from large financial data set for over 10 years. IMHO, no other product provides the performance and flexibility remotely close to kdb+. The build-in language q is very terse and expressive. In one of my former jobs, traders were creating new quantitative models almost "in the speed of thoughts" with kdb+. That really gave us the edge of "time to market".

Time series analysis. The built-in vector operations are extremely fast. Also with the q language you can code up any customized analytical ideas quickly.
The database are all file based, very easy to maintain.
Very solid and fast interface to websocket, so you can interface with javascript easily.

The learning curve is a little steep in the beginning.

Fast turn around on delivering new ideas and products.

Oracle Berkeley DB, TIBCO StreamBase, Hadoop

We use kdb to store and analyze real time market data for transaction cost analysis.

Process large amounts of time series data
Perform quick calculations without use of cursor
Use of window joins and as of joins

Hard to read
Hard to find knowledgeable developers
Lack of good IDE

Long development time
Long hiring cycle
Hard to error check

time series data analysis
live tick trading
trading analysis

We have not had a very positive experience with the solutions KX has provided to our issues.

time series calcutions
easily access and manipulate records without a cursor
ability to create functions withing a given query

indexing multiple columns
readability of the language
debugging

kdb+ is very useful in the trading world as it allows analysts to look at huge amounts of data quickly and somewhat easily. Speed was a huge factor.

Efficient computing.
Code interpretation is fast.
Designed with finance in mind.

The language is difficult to learn.
Better solutions are needed for breaking loops without resetting servers.
Include basic templates for fields such as finance, medicine, etc.

Increases the speed of the research process.
Allows for quick analysis and results.
Rapid implementation of new ideas.

kdb+

Overview

What is kdb+?