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‘Time Series’ is not new. It has long been at the heart of commerce – most notably in the form of Financial market price ticks, but it is now equally widespread across industry and has evolved from minutes and seconds – to nano and milliseconds.
The connected world expands exponentially every year. Over the last ten years, we have experienced growth in the volume and velocity of data from ever-expanding cloud infrastructure, the emergence of 5G, and the rapid adoption of AI and machine learning across market trading platforms, smart meters, machine tools, and a myriad of IoT applications.
Fortunately, the constant stream of human and machine-generated data can now be captured and analyzed faster than ever before.
Businesses need to understand how to efficiently store and query the data and evaluate what platforms and analytic tools can help deliver the best-informed insights. More importantly, there’s an opportunity to use time-series data to make better-informed decisions and drive real business benefits.
“When it comes to time series data, you want to be using a purpose-built time series platform to handle your data streams. You need infinite scalability, particularly if you’re deploying on the cloud”.
Watch as Kathy Schnieder and James Corcoran from KX join Mike Matchett from TruthinIT in a discussion about time series data and the KX tools that accelerate adoption.
They can deliver significant improvements in storage space and performance over general-purpose databases and have the following key features:
Time Series databases support improved, and faster decision-making based on what is occurring now, what happened in the past, and what is predicted to occur in the future.
A time series database can help:
Many businesses look at their big data applications purely through an “architecture” lens and miss the vital point that the unifying factor across all data is its dependency on time. For big data applications, the ability to capture and factor in “time” is the key to unlocking value.
Data exists and changes – in real-time, earlier today or further in the past. Time series databases are highly performant, scalable, and usable ways to handle exploding data sizes. They offer specialized features not supported in standard SQL and no-SQL languages and can enable your team to capture and perform the analysis required to support ML and AI models.
Time series datasets, databases and analytics can deliver a meaningful snapshot of your business – from any specific point in time.
Each piece of data exists and changes in real-time, earlier today or further in the past. However, unless they are all linked together in a way that firms can analyze, there is no way of providing a meaningful overview of the business at any specific point in time.
Some firms have struggled to introduce this due to the limitations of their technology. Some architectures don’t allow for time-based analysis and struggle with recording and querying it in a highly efficient way. It’s a common issue resulting from opting for a seemingly safe technology choice. It’s also one of the reasons why over the past 24 months that time-series databases (TSDBs) have emerged from software developer usage patterns as one of the fastest-growing categories of databases.
Kdb+, which underpins all KX solutions, is the world’s fastest and most efficient time series database, designed from the outset for high-performance, high-volume data processing. The columnar design of kdb+ and use of the same in-memory architecture for both real-time and historical data means it offers greater speed and efficiency than typical relational databases – read about our latest STAC-M3 Financial Services Benchmarking here. And its native support for time-series operations vastly improves both the speed and performance of queries, aggregation, and analysis of structured data.
Kdb+ is also different from other popular databases because it:
For applications and critical decisions that require continuous and contextual intelligence, kdb+ accelerates your speed to business value. To experience the kdb+ difference, watch the demo, try the 12-month free kdb+ Personal Edition, or speak with our sales colleagues today.
Combining multi-layered solutions using different tools adds complexity and cost – and falls short on scale and usability. In many cases, the datasets will live on various platforms – such as a traditional database for historical data, in-memory for recent data, and event processing software for real-time data. Traditional databases cannot span multiple domains effectively with acceptable performance.
All data is, in some way, time-dependent. By unifying the underlying technology, businesses can move beyond simply storing and analyzing a subset of data to making insightful correlations across the company – and delivering significant cost-benefit.
Time really does mean money – so preserve your software and RDBMS technology investment while addressing the performance, scale, and cost challenges of working with high data volumes. Being able to implement alongside existing systems and processes is one reason why KX is the go-to time series database and analytics engine for the most ambitious organizations on the planet.
STAC-M3TM represents a truly independent set of benchmarks for the financial services industry and bypasses all proprietary benchmarking tooling used by technology vendor. It is the industry standard testing solution for high-speed analytics on tick-by-tick market data.
“STAC” and all STAC names are trademarks or registered trademarks of the Securities Technology Analysis Center, LLC.”
Imperial College London undertook a database scenario for storing and querying data from stock and cryptocurrency exchanges, running benchmarks across trade data, order book, complex query, writing efficiency – persistant storage, and storage efficiency.
According to Ruijie Xiong, Department of Computing, Imperial College London, “kdb+ is able to fast ingest data in bulk, with a good compression ratio for storage efficiency. Kdb+ has stable low query latency for all benchmarks including read queries, computational intensive queries, and complex queries”.
Check out the full benchmark here.
DBOps is a public benchmark comparing the performance of a number of open-source database tools and technologies. It defines a set of reproducible tests to measure their performance executing typical queries that a data scientist would run.
The tests include mathematical/statistical calculations, group-by operations and joins over a range of data sets to show performance under different conditions.
The Time Series Benchmark Suite (TSBS) is a collection of Go programs originally developed by InfluxData that are used to generate datasets and then benchmark read and write performance of various time-series databases. The intent is to make the TSBS extensible so that a variety of use cases (e.g., devops, IoT, finance, etc.), query types, and databases can be included and benchmarked.
What data are the tests based on?
Constant connectivity and business digitization have supercharged data growth, and it’s not showing signs of slowing down. Meanwhile, the window of opportunity to act on data-driven insights is continuously narrowing, and the ability to make faster, better informed business decisions has become a competitive imperative.