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Introducing The Data Timehouse: Bringing Data Science to Data

Time data is primal. It has always been thus for modern computing, mathematics and, now, artificial intelligence.

From the founding grandparents of modern computing and AI – Ada Lovelace’s “analytical engine,” Alan Turing’s Enigma and Turing Machine-infused work on “Rounding-off Errors in Matrix Processing”, the “Grace Hopper nanosecond,” – elements of time, analytics and data inform modern computing.

For modern AI specifically, the core foundations were expressed through, for example, time and state-infused notions of dynamic programming, mathematical optimization and computing methods popularized by Richard Bellman. In turn, this influenced Geoff Hinton et al’s work on back-propagation which so informed neural networks and, infused by modern compute power and storage, underpins the inordinate scale and scope of the current generative AI epoch.

You, dear reader, are probably like I, neither a computing pioneer nor a neural network genius, just a humble individual working on use cases that benefit our organizations and the markets at large. For example:

  • Which stocks and/or derivatives can I use as proxies, in my trading or risk and hedging strategies?
  • How can my lending team diagnose credit risk from data-sets beyond risk-ratings where risk ratings may be incomplete or inadequate, say rural areas of India?
  • Where and why did retail customer adoption just increase? Which customers became happy? How can more customers be happy?
  • How do we best combine market data, internal data-sets and fraud patterns to identify spoofing transactions?
  • What trading, investment or risk strategy should I deploy amidst a possible credit default despite a recent bullish trading history?
  • How will the recent extreme weather impact supply chains and thus retail and food & beverage equities pricing, and commodity prices at large? Should I make FX hedges?
  • What churn trends happened because of unhappy customers in this month’s 2,000,000 online customer conversations?

All these use cases use data, often different types of data, sometimes simulated and generated data. All leverage prediction, much automated but mostly requiring human insight and intelligence for optimal action. All center on TIME: when and how and into what do I adjust my portfolio? how important is it I hedge today for FX risk? Can I lend based on on more complete data-sets incorporating alternative sources?  Time, time, time, prediction, decide, time, time, time.

And thus, we introduce the data timehouse which brings termporal-infused AI, the defining technology of our time, to the forefront of the data ecosystem.

A data timehouse is a data science-centric environment where the temporal, “time” – orchestrates prediction and information from a “house” of intertwined models and data.

Traditional Data Warehouse Before Data Lakes Vs. Data Timehouse - KX

Not that it needs introducing, since many organizations in financial services, particularly in capital markets, already keep a data timehouse.

For them, the storage provided by a data lake and a data warehouse is only a part of their data story. Their priority is the intelligence, insight and outcome. Anything that prioritizes storage – the lakehouse, mesh, fabric – is insufficient. The data timehouse infuses data fuel with an analytics engine organized around time to create gold. Think of it as a temporal data warehouse, with added analytics able to engage data immediately in an ultra-efficient way.

Paraphrasing a CEO of a major financial exchange who has practised a data timehouse culture for years:

There are no limits to all conceivable data in the universe that we could capture, in real-time to record a ‘continuous stream of truth, and ask any question of any dataset at any time, and get the answer instantaneously.”

“The only limit is our imagination and in the questions we can think of to ask such as why did something happen? Or why did something happen the way it happened? Or what were the small events that led to the big event? And which factors influenced each event? If you know the answer to the question ‘why’, you can do a lot with that information.

In the following sections of the article, we deconstruct some traditional views around so-called data categories. We then construct the data timehouse from the constituent parts, exploring how time, time-series, models, analytics, and vector mathematics reinforce each other, finally concluding with some real-use cases with particular reference to data science workflows.


Section 1: From Data Warehouse to Data Timehouse: The Traditional Storage-Narrative

Data warehouses have a substantial history in decision support and business intelligence applications. Since their inception in the late 1980s, massively parallel processing (MPP) architectures facilitated the handling of larger data sizes, provided they were structured. Unstructured and semi-structured data were however troublesome, and its core paradigm proved time and cost inefficient for data with high variety, velocity, and volume.

Thus about 15 years ago, architects envisioned the data lake, a single system housing data for many different analytic products – repositories for raw data in a variety of formats. While great for storing data, data lakes neither supported transactions nor enforced data quality. Also, their lack of consistency and isolation made it hard to mix appends and reads, and unwieldy for batch and virtually irrelevant for streaming jobs. It also had the misfortune to be built on top of a bewildering Hadoop ecosystem that has not aged well, a software equivalent of the Star Wars “A Phantom Menace,” a wrong cinematic turn and the ultra-annoying jar jar binks. Yes, I have a Data is the New Bacon T-shirt tucked away in my old t-shirt draw courtesy of my favorite Hadoop services provider, AND I also happened to be at a premiere of the Phantom Menace, both guilty pleasures.

The promises and hype of initial data lakes did not materialize, nor with Hadoop 2.0, nor numerous relaunches, and resulted, some say, in the loss of many warehouse benefits.

While data warehouse versus data lake divided opinions like pineapple on pizza, organizations used the cards they were dealt and got on with their jobs. While the data nerds got their Hadoop t-shirts and geeky tech, businesses continued to expand SQL analytics, real-time monitoring, and add new layers of data science and machine learning technologies to which I will return. Most firms evolved a polyglot of concurrent systems serving a variety of use case and key applications – perhaps a data lake, several data warehouses, SQL Server, MySQL or similar setups, and, depending on focus, specialized streaming systems, graph, and image databases. The result: poor responsiveness, and cost (and carbon footprint) inefficiencies across complex systems and a data stack not in harmony with the emerging analytics and model development universes inspired by the Pythonic movement.

Step forward Databricks, Dremio and others with key Apache projects like Spark and Iceberg. They promoted the useful lakehouse concept, more open and inclusive, allowing for sensible co-existence of lake and warehouse, with low cost cloud storage, object stores, and open formats to enable moderate interoperability. It has also, conveniently, enveloped hyperscaler cloud services into a convenient data lake story.

Yet even a “governed lakehouse” promotes and essentializes cheap storage at the expense of useful analytics, continuing to separate compute and storage, bringing the two together reluctantly, with latency, data transfer costs and inefficient queries in suboptimal localities. Key applications, such as real-time streaming analytics, are very far from front of mind yet a use case dynamic that thousands across the world participate in. The chaotic mesh and centralized lakehouse coexist somewhat, moderated sometimes by process-oriented fabrics. However, analytics is not at their heart. Skeptics might indeed argue that the lakehouse is simply a nice semantic to explain and circumvent the organizational lake-hack gloop the real world has ended up with.

Enter the data timehouse, a temporal data warehouse but with so much more.


Section 2: The Data Timehouse Constituent Parts


Temporal Data & Time-Series, The Basics

“It’s really clear that the most precious resource we all have is time.” — Steve Jobs

Okay, I perhaps take Jobs’ quote out of context, but let’s take a step back, get into the mindset of the data scientist and AI domain expert and explore their notion of time.

Most data, particularly machine generated data, has a temporal structure. That’s what data scientists analyse, whether it comes from sensors (machines, in factories) or human events (shopping events, stock markets, criminal activities, etc)

Knowing how to model time series is an essential skill within data science, as there are structures specific to this type of data that can be explored in all situations. Don’t get caught up, yet, in terms of time scales like seconds or months, just think of units of time as periods.


Structure of a Time Series

A time series is a series of data points indexed in time order, like this (with apologies for formatting):


Measurement Time     Temp     Pressure     Wind Speed

18-Dec-2015   08:03:05    37.3    30.1     13.4

18-Dec-2015   10:03:17    39.1    30.03    6.5

18-Dec-2015   12:03:13    42.3    29.9      7.3


The timestamp represents a focal point of the moment in time when an event – a weather phenomenon – was registered. The other column designates a value delivered by this phenomenon at that moment. More recorded values mean multivariable time-series.

We might want to synchronize the weather data to regular times with an hourly time step, and adjust the data to new terms using an interpolation method. Thus the data has now undergone some simple time-series analysis looking like this (with apologies for formatting)


Measurement Time      Temp      Pressure      Wind Speed

18-Dec-2015 08:00:00      37.254      30.102      13.577

18-Dec-2015 09:00:00      38.152      30.067      10.133

18-Dec-2015 10:00:00      39.051      30.032      6.6885

18-Dec-2015 11:00:00      40.613      29.969      6.8783

18-Dec-2015 12:00:00      42.214      29.903      7.2785

18-Dec-2015 13:00:00      43.815      29.838      7.6788


Time series can be regular time series, a record at each uniform moment in time, say a daily series of temperatures, or an hourly series like the modelled data above. Other time series can be irregular, for example, like the initial dataset above of weather observations, or a log of accesses to a website. It could also be a time when a chess move is made in a match. In capital markets, both types are used interchangeably, for example stock prices. Published stock prices by might reflect a given or modelled price over specific time frequencies, every second for example. However, “ticks” can occur at any moment in time, a trade, a quote, etc. At certain occasions, e.g., market open, there are many ticks – a lot happens. Market-makers, for example, watch the individual ticks closely, where as the retail broker systems us amateur traders deploy when we feel like going in the market tend to work with aggregated data-sets, for example 15 second price updates.

In all these cases, understanding the temporal dynamics of the weather data, the sequences of chess moves and the “ticks” on the stock price inform our ability to determine information about future paths – the prediction – of future events.


Models & Analytics: An Example

 Model-speak alert. If you’re not a quant modeller, I try to keep this section a high level but salient points appear in the “Why it Matters” section.

I’m going to explore the model concept through the lens of one of the most influential algorithms readers may not have heard of – singular value decomposition (SVD). Obscure, perhaps, but the very essence and foundation of AI as you know it. It is intertwined closely with the dynamic programming and backpropagation techniques discussed at the very start of this paper.


What is Singular Value Decomposition (SVD)?

Singular Value Decomposition (SVD) matrix factorization (or decomposition) is a workhorse of modern numerical computing and fundamental to quantiative finance and econometrics. Its use is found in statistics, signal processing, econometrics, information retrieval, recommender systems, natural language processing, image processing, computer vision, engineering, control systems theory, machine learning (heavily used in tensor/multi-linear-algebra learning, multi-task learning, and multi-view learning), deep learning (DL), plus more. Most of these mathematical use cases center on time. Indeed, for time-series, SVD is a crucial tool to discover hidden factors in multiple time series data, and it is used in many data mining applications including dimensionality reduction and principal component analysis (PCA) that get used in real-time statistical inference and MLOps workflows, for example in recommender systems or fraud detection use cases.

The factorization of a big matrix with the standard SVD is compute and memory-intesnsive. Think of a retail dataset matrix of [transactions x products]. A supermarket sales matrix data with, say, a weekly transaction set of 500K for 40K products available, is a sizeable dataset for the standard SVD. This matrix size [500K x 40K] can take hours/days (depending on the computing infrastructure if SVD is parallelized or not) to factorize/decompose if you don’t run into an ‘out of memory’ error already, by using, say, ‘numpy.linalg.svd’ or ‘scipy.linalg.svd’ in Python.


What on earth did you just say? Why does it matter?

Here are four reasons why what I just said above matters, with a little less deep matrix algebra jargon.

  • The supermarket data-set is likely a time-series, as most are in financial services.
    • Decomposition makes sense of the data-set and puts it in a form useful for statistical and machine learning, It’s memory – and compute – hungry. The data-set is completely intertwined with the algorithm, and that’s before we’ve even gotten to the “sexy” predictor.
  • Its organization is conducted via vectors, or matrixes, with the process of revising loop-based, scalar-oriented code to use vector or matrix operations called vectorization. Vectorizing your code is worthwhile for several reasons:
    • Vectorized mathematical code appears more like the mathematical expressions found in textbooks, making the code easier to understand. The SVD process outlined above is a foundational element of matrix algebra or linear algebra. Vectors commonly represent events over time.
    • Less Error Prone: Without loops, common to most code conventions, vectorized code is often shorter. Fewer lines of code mean fewer opportunities to introduce programming errors. In big data and compute-intensive processes, this matters.
    • Vectorized code often runs much faster than the corresponding code containing loops. In big data and compute-intensive processes, this matters.
  • Everything described in above involves data and an operation on that data. Data on its own is ineffectual
  • The programming language alluded to above is Python. That’s a completely different world to the data store.

So, bringing all this together – core operations – requires the co-existence of Azure blobs (or other data stores) and Python (or similar) code-sets. That means compute combines with storage for analytics, deep in the data as with SVD, and at the higher level to abstract meaning, e.g. the machine learning algorithm.

Taking the SVD routine a stage further into the world of AI and key machine learning use cases that it underpins – a common machine learning set of analysis is dimensionality reduction. This matters, as reducing the number of features in a data-set while preserving as much information as possible drives effective model training into a trained model and subsequent live model inference. That helps deliver a live insightful model, for example one that doesn’t just observe, say, quotes and prices in real-time but one that predicts and delivers trends, as ticks proceed.

That workflow commonly centers on the Python programming language as this is the language of data science model development, though as workloads – data and model – go from research to production, attributes like speed, resilience and efficiency matter so production implementations, likely cloud-centric and MLOps, matter. Python drives, but production tooling downstream looks different.


What Do I Do then with a Data Timehouse?

Stop stuffing temporal data in your data warehouse sock drawer and do as others already do well. Deploy temporal data into your data timehouse, get it used in a convenient form by your Python programmers, data scientists and data analysts. In this way, put data science at the centre of your workflow. Be more productive. Be more useful. Leverage the technology trend that defines our modern age, AI.

A data timehouse is shown to be 100X faster and 1/10th the cost for their time series data than queries running on common data warehouse, lake and lakehouse platforms, because the data timehouse efficiently captures the data and predictor through the common denominator of time series.

On the other hand, compared to data science and machine learning platforms used, for example “pure” Pythonic code running in-memory, the data efficiency of the data timehouse – in-memory in an optimized way – is similarly 100X faster.

Thus the data timehouse nicely blends data science with data through the common denominator of time. It works "natively" with both data and model sides of the house.

The data timehouse most likely interoperates with a warehouse, where storage of a governed golden source data set matters, as it does the model environment, but it carries some of the core workloads – the workloads that matter – and runs them so much more efficiently. 


What does a data timehouse look like?

The time series nature of finance entails mapping and aggregating sequences of events - trades, quotes, and other market activities  and augmented data - benchmarks, trader processes and styles for example - to better observe market conditions. With this, capital markets can understand trade, risk and compliance patterns to build an optimized map from the temporal sequence of activities, market-facing and, say, internal data-sets (for compliance, replay).

That means working with a range of data sets centered around time, such as:


Time Data                               Update Frequency       Enriched With

Market Data:                            Tick                              Historical Trends

Internal Order Requests          Intraday                        Trader Styles

Simulation Data                       e.g. Real-Time Testing  Bursty Metadata

Benchmark Data                      Variable                        Metadata


In putting the temporal at the center of data warehouse and analytics architectures, teams in financial services organizations can consolidate and guide analytical queries and statistical and machine learning models, core to their derived business value – the insight. They can leverage their data timehouse’s ability to perfectly blend data and analytic, memory and compute, to best effect, to deliver blindingly fast queries and analytics at a mere fraction of the cost of alternative paradigms. It looks like this.


Data Timehouse Wokrflow - KX



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