KDB: Trading Edge to Enterprise Backbone

Eighteen months on from our KDB Insights 2024 publication, the technology has shifted from niche low-latency trading to compliance-critical, AI-enabled infrastructure across risk, surveillance, and reporting. The opportunity is clear… but without the right expertise, institutions risk stalled projects, spiralling costs, and regulatory exposure.

Exclusive Update: September 2025 publication, authored by Ben Rutter - Head of Data & Analytics Practice

At the start of 2024, institutions viewed KDB as indispensable but difficult to scale, not due to a lack of performance, but rather due to a lack of accessible skills. We highlighted three themes at the time: a surge in demand for real-time analytics, growing pressure around data governance, and a tightening skills market for KDB specialists.

Eighteen months on, those themes remain… but the context around them has changed dramatically.

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In 2024, KDB’s impact was still spoken of primarily in terms of trading edge. Ultra-low latency tick processing and high-frequency analytics were the headline use cases. Today, adoption has widened. Financial institutions are deploying KDB not just to execute trades faster but to underpin regulatory reporting, real-time risk systems, and AI-driven compliance monitoring. This shift reflects both the regulatory climate and advances in the technology itself. 

  • Cloud availability has removed barriers, allowing banks to trial and scale KDB workloads through AWS, Azure, and Snowflake marketplaces: something only on the horizon in our last report 

  • Python and SQL integration has broadened the user base, meaning data scientists and engineers who once sat outside the q ecosystem are now working with KDB every day 

  • AI and vector capabilities have moved from prediction to practice: in 2024 they were emerging concepts, by 2025 they are embedded in real-time surveillance, anomaly detection, and model explainability

The regulatory environment has also crystallised. Predictions we made about forthcoming change have been realised: 

  • The MiFID II consolidated tape is now a reality, demanding near-instant reporting of trades across Europe 

  • Basel III Finalisation has come into force, raising expectations for data quality and cross-risk consistency 

  • The EU AI Act has shifted AI from innovation theatre to governed, auditable practice 

Each of these developments has one common demand: data must be fast, accurate, and transparent. KDB’s strengths in lineage, precision, and real-time performance make it the natural backbone for meeting these obligations. 

Talent remains the fulcrum.  

In 2024, we noted a shortage of q developers and a spike in salaries. That reality has only intensified. Median London pay still sits around £145–150k, with hedge funds paying far more. What has changed is the profile: firms now expect KDB expertise to be paired with Python, cloud fluency, and AI literacy. Organisations are meeting this challenge through creative models; leveraging contractors, outcome-based delivery, and specialist partners to secure scarce expertise. 

This is precisely where Caspian One’s Data & Analytics Practice delivers value.  

With more than a decade of heritage in kdb+, formal partnership with KX, and a network of subject matter experts spanning front-office architects to quantitative analysts, we have supported over 40 institutions in implementing, scaling, and modernising KDB solutions. From trade surveillance for hedge funds, to compliance monitoring platforms for global banks, to high-performance analytics teams for Tier-1 institutions, our credibility is proven. 

Looking back to our 2024 report, the trajectory is clear: KDB has evolved from being a performance differentiator to becoming a compliance and AI enabler.

For financial institutions, the imperative is no longer whether to invest in KDB, but how to do so strategically; aligning projects to high-value use cases, embedding governance by design, and securing the right expertise. Caspian One is uniquely positioned to help them achieve exactly that. 

As the author of this update, I’d welcome the opportunity to share my perspective with you directly. If you’d like to discuss your institution’s priorities around KDB - whether that’s regulatory compliance, skills strategy, or scaling adoption - you can click ‘Meet with Ben’ to choose a time and date that works best for you.

Alternatively, use the dropdowns below to explore a more detailed update on key topics raised in the original 2024 report, which is also available below.

Meet with Ben

  • Overview

    KDB (kdb+) remains the top-performing time-series database in financial services, central to real-time analytics, risk workflows, and now compliance. Its role has shifted from a specialized trading tool to a mission-critical enterprise platform. 

    Adoption and Market Dynamics

    • KX’s cloud-native delivery has gained real traction. Since launching KDB Insights Enterprise on Azure, clients have reported being able to “transform into real-time intelligent enterprises” by leveraging its vector database capabilities 

    • The firm’s inclusion in the AIFinTech100 in 2025 underscores KDB’s expanding application in complex, real-time AI systems 

    • Notable client achievement: B2C2, a quant trading firm, uses KDB to support high-performance quantitative research and trading systems: highlighting its role beyond simple data storage to live trading use cases 

    Technology Evolution

    • Python integration is now firmly embedded. PyKX, KDB’s Python interface, delivers “analytics speed increases of up to 100×” for time-series workloads, empowering data scientists to work seamlessly without switching to q 

    • Language accessibility has expanded. KX continues to build cross-language integrations, ensuring KDB is interoperable with Python, SQL, Parquet, and more; lowering friction to adoption across modern tech stacks 

    • Vectorization and real-time AI capabilities have become core. The platform now enables creation of digital twins of capital markets: comprehensive, data-driven models used for post-trade analytics and streaming insights 

    Performance and Benchmarking

    • Independent benchmarking still places kdb+ ahead of competitors like ClickHouse and TimescaleDB for high-frequency data workloads - a key justification in high-stakes environments where performance directly impacts profitability 

    Implications for Financial Institutions

    1. Real-time, regulated environments are now baseline. Regulatory mandates such as MiFID II consolidated tapes and Basel III Finalisation require rapid, accurate data aggregation. KDB’s low-latency and high-volume capacities make it a near-essential platform 

    2. Democratized analytics. Broad Python and SQL support means KDB isn’t just for quants: it’s accessible to broader data teams, accelerating the value delivered 

    3. AI is now live, not hypothetical. Vector capabilities powering surveillance and anomaly detection are in active use, moving KDB from future potential to active business enabler 

    4. Scalability and integration matter. The cloud-ready architecture and rich API surface mean firms can adopt, scale, and integrate KDB much more easily than in the past 

  • Summary 

    KDB’s technological footprint in financial services has broadened significantly since 2024. The integration of Python and SQL, cloud-deployment capabilities, and AI/vector enhancements translate into measurable improvements in accessibility, performance, and analytic sophistication. 

     

    Key Developments & Data-Driven Insights 

    1. Python Integration via PyKX 

    • PyKX 3.0, released in late 2024, combines kdb+ speed with direct compatibility to Python’s machine-learning ecosystem and achieved over 400,000 downloads across distribution platforms. This highlights its rapid adoption and relevance among Python developers 

    • The redesign specifically introduced a Python-first query API, enabling approximately 95% of analytics workflows to be conducted entirely in Python, lowering the barrier to adoption for non-q developers 

    • According to KX users, including Emanuele Melis at Talos: 

    “If you’re looking for a single piece of technology that can do both historical and real-time analysis, kdb+ is the de facto standard in the trading industry.” 

    • Prior benchmarks using PyKX indicated analytics speedups of up to 100× when compared to traditional Python libraries like pandas, particularly in time-series and backtesting workflows 

    2. Cloud-Native Access & Marketplace 

    • Across financial services, hybrid cloud usage stands at 68%, with public cloud adoption at 53% in 2023 - highlighting the industry’s broader cloud momentum 

    • 33% of organizations are now spending over $12 million annually on public cloud services, representing a growing readiness to invest in scalable cloud infrastructure 

    • While not specific to KDB, these trends provide context-cloud maturity is a key enabler for widespread deployment of tools like KDB on platforms such as AWS, Azure, and Snowflake 

    3. AI & Vector Capabilities 

    • KDB now serves as a hybrid engine for both time-series and vector data, supporting real-time streaming analytics, anomaly detection, and NLP-driven risk models 

    • Clients are now using KDB to build “digital twins of capital markets”, blending historical and real-time data with AI-driven modelling 

     

    Strategic Implications 

    • Wider Developer Adoption: Python accessibility means KDB is no longer siloed within quant teams; data scientists and engineers are using it directly for real-time analytics and model generation 

    • Faster Time to Value: Speed gains via PyKX and cloud deployment reduce friction in prototyping and operational rollouts, especially in AI workflows 

    • Scalable & Compliant Architecture: Cloud readiness aligns KDB infrastructure with enterprise requirements for security, availability, and geographic compliance 

    • AI-Ready Data Foundation: Vectorization and streaming capabilities position KDB as a core enabler for intelligent, real-time workflows in surveillance, risk, and automation 

  • Summary

    Since early 2024, regulation has moved from “coming soon” to live obligations that demand near-real-time, auditable data. KDB sits at the intersection: high-volume ingestion, lineage, and millisecond analytics that map neatly to new reporting, risk and AI-governance requirements. 

    A. Market Transparency: EU Consolidated Tape (MiFIR) 

    • What changed:

    • ESMA launched the bonds Consolidated Tape Provider (CTP) selection on 3 Jan 2025, and on 3 Jul 2025 named Ediphy (fairCT) as the first EU bonds CTP, with a six-month selection window as planned. 

    • Why it matters for data:

    • Participants must stream standardised trade data in near real time, with accuracy, timeliness and de-duplication controls capable of pan-EU aggregation at scale: a classic KDB workload (tick-level capture, schema harmonisation, and low-latency validation). 

    • Signal:

    • ESMA’s timeline made the tape a 2025 reality, shifting market transparency from venue-level to EU-wide; firms without real-time plumbing face remediation and operational risk 

    B. Capital & Risk: Basel III Finalisation (CRR3/CRD6, “Basel IV”) 

    • EU timeline:

    • Most CRR3 measures apply from 1 Jan 2025, per the EU “banking package”; the Commission in Jun 2025 proposed one extra year of delay for market-risk/FRTB to preserve an international level playing field 

    • UK timeline:

    • The PRA delayed UK Basel 3.1 implementation to 1 Jan 2027; consultation proposes FRTB-IMA from 1 Jan 2028 (retain existing IMA in 2027) 

    • Why it matters for data:

    • Final rules increase risk data aggregation frequency and granularity; firms must reconcile standardised vs internal-model outputs and support explainability across calculators; favouring time-series stores with lineage and replay (i.e., KDB). The Basel Committee’s objective is to restore credibility of RWAs via more robust, comparable measures 

    C. AI Governance: EU AI Act 

    • Key dates:

    • In force: 1 Aug 2024 

    • Bans & AI literacy: apply 2 Feb 2025 

    • GPAI model obligations: 2 Aug 2025 

    • Full applicability (most systems): 2 Aug 2026 

    • Embedded high-risk systems (transition): to 2 Aug 2027   

    • The Commission confirmed no pause to the timeline despite industry requests. 

    • Why it matters for data:

    • High-risk AI in finance (e.g., surveillance, credit, some trading systems) must evidence risk management, traceability, logging, and human oversight; operationally this means immutable, time-stamped event logs and data lineage for training and inference, well aligned to KDB’s strengths 

    D. Transatlantic context: U.S. Consolidated Audit Trail (CAT) 

    • Status:

    • CAT tracks order events across U.S. equities/options; SEC Rule 613 requires reporting by 8 a.m. ET next day; 2025 saw continued technical spec updates and compliance oversight (e.g., CAIS obligations) 

    • 2025 litigation struck down the SEC’s 2023 CAT funding order; policy debate continues, but CAT reporting obligations remain 

    • Why it matters for data:

    • Even amid policy flux, supervisory infrastructures demand order-lifecycle linkage, clock synchronisation, and full event auditability - reinforcing the industry trend towards low-latency, lossless event capture platforms 

    What leading firms are doing now 

    • Consolidated tape readiness: normalising trade schemas, instituting real-time quality gates (late/duplicate checks) and building millisecond-accurate audit trails on streaming KDB tiers ahead of bonds tape go-live phases 

    • Basel data operating model: consolidating calculators’ inputs/outputs in a single time-series backbone to reconcile standardised vs internal models and support intraday risk explainability and back-testing 

    • AI Act controls: implementing model logs, data lineage, and traceable feature stores with immutable timestamps; aligning retention and access controls to the staged applicability dates 

    Bottom line: 2025 regulation compresses timelines and raises the bar on speed + transparency + governance. Architectures that combine streaming ingestion, time-aligned history, and audit-grade lineage (hallmarks of KDB deployments) are becoming the standard of care rather than the exception. 

  • Summary 

    Demand for kdb+/q talent in financial services remains structurally high, with hiring broadening from pure trading teams to risk, surveillance, and regulatory data functions. The skills mix is shifting towards q + Python + cloud, with AI/ML literacy now a frequent differentiator. Compensation bifurcates: public job-board medians in the UK sit around the mid-five figures to low six figures, while elite hedge funds and prop shops continue to advertise high six-figure total packages for top kdb+ engineers in New York and London.   

     

    Demand signals & where roles sit 

    • Role spread has widened: beyond e-trading and market data, banks are adding kdb+ headcount in real-time risk, trade surveillance, and regulatory data (e.g., consolidated tape readiness), reflecting 2025 regulatory requirements for low-latency, auditable data pipelines. (See Section 3 for the regulatory drivers) 

    • Python access is a catalyst: KX’s PyKX 3.0 (late-2024) formalised a Python-first API and tighter ML interoperability, easing cross-functional adoption and allowing data scientists to orchestrate real-time and historical analytics “entirely from Python”. This materially enlarges the candidate pool beyond pure q specialists 

    “If you’re looking for a single piece of technology that can do both historical and real-time analysis, kdb+ is the de-facto standard in the trading industry.” - industry user quote cited by KX at PyKX 3.0 launch   

    Compensation: public medians vs top-end packages 

    • UK-wide median (job-board data): ITJobsWatch reports a £95,000 median salary for “KDB Developer” across the UK (6-month window to 8–9 Sep 2025) and £95,000 median in London, based on live vacancies. These medians reflect a mix of seniorities and employers, not just front-office finance 

    • Hedge funds / prop trading (upper decile): eFinancialCareers listings routinely cite $250k–$300k base with total comp up to ~$400k–$550k, and occasional outliers advertised up to ~$770k for quantitative developer roles specifying kdb+/q. These are rare but persistent at elite firms 

    • Crowdsourced platforms (e.g., Glassdoor) show wide dispersion and small samples; we do not rely on them for benchmarking, but they corroborate the long tail at lower seniorities and outside FO finance 

    Implication: Boards should benchmark compensation to the talent market they’re targeting. Building low-latency trading infra or intraday risk engines against Tier-1 peers requires budgeting closer to the upper quartiles, not general UK medians.   

     

    Skills mix (2025 hiring criteria) 

    • Core: advanced q/kdb+ (in-memory & on-disk data models, tickerplant/feed-handlers, real-time joins, historical replays) 

    • Now table stakes: Python (PyKX, pandas interoperability), SQL, and cloud deployment fundamentals (containerisation, Kubernetes, observability) following KX’s push to cloud marketplaces and Python-first workflows   

    • Differentiators: AI/ML literacy (feature stores, vector search patterns with KDB/KDB.AI), governance & lineage awareness aligned to the EU AI Act, and regulatory data familiarity (MiFIR tape schemas, FRTB data lineage) - increasingly cited in 2025 role specs and aligned to live obligations outlined in Section 3   

     

    Geography & sourcing 

    • London & New York remain the deepest markets for senior kdb+ roles, with consistent posting velocity on specialist finance boards 

    • Nearshore hubs (notably Poland/CEE) continue to expand: credible 2025 reports place senior Poland engineering at ~$70–80k annual cash comp, with AI specialists ranging roughly PLN 18k–27k per month gross depending on contract type: materially below London/NY while maintaining strong technical depth  

    Implication: Hybrid models that blend London/NY leadership with CEE delivery pods can de-risk capacity while preserving quality, provided you invest in kdb+/q upskilling and Python workflow standardisation.   

     

    Resourcing models that are working in 2025 

    • Specialist augmentation for burst capacity on trading/risk initiatives (e.g., feed-handler rewrites, real-time QA pipelines) 

    • Outcome-based pods to deliver defined artefacts (surveillance dashboards, consolidated-tape adapters, risk data stores) with embedded governance 

    • Build-operate-transfer (BOT) arrangements to establish in-house kdb+ capability while accelerating time-to-value 

    These models map to the skills scarcity evidenced by salary dispersion and sustained high-end demand on finance-specific job boards.   

     

    What this means for FS leaders 

    1. Budget with intent: If the mandate is latency-sensitive trading or intraday risk, assume upper-quartile comp and targeted sign-on/retention mechanics; medians from public boards will under-price the mandate

    2. Broaden the funnel: Python-first workflows (PyKX) let you hire strong Python engineers and upskill into q; reserve “pure q” hiring for kernel/performance hotspots

    3. Leverage nearshore: Use Poland/CEE to scale non-front-office components (data QA, lineage services, replay stores), with senior architecture held in London/NY 

    4. Make governance a skill, not a checkpoint: embed AI Act/MiFIR/Basel-aware practices in job descriptions and team KPIs (data lineage, immutable logging, model traceability), not just in policy documents. (See Section 3 for regulatory specifics) 

  • Summary 

    The story in 2025 is not about whether KDB can deliver on performance - that case is long proven - but about how to implement it effectively within increasingly regulated, cloud-first institutions. The challenges have shifted: where once the focus was language barriers and integration pain, today success hinges on compliance-by-design, governance, and the ability to scale multidisciplinary teams. 

     

    A. Common implementation barriers 

    • Talent scarcity: q expertise remains rare, raising project risk where in-house capability is thin 

    • Integration with legacy systems: KDB must often sit alongside or replace decades of entangled platforms 

    • Compliance requirements: explainability, lineage, and resilience are no longer optional, but regulatory expectations 

    • Cross-functional alignment: Python and SQL integration broadens the user base, but requires governance to prevent fragmentation of standards 

     

    B. How financial institutions are overcoming them 

    Caspian One’s project track record illustrates the solutions that work: 

    • MiFID II compliance – We supported a cash equities desk in building a KDB-based platform that delivered the speed and auditability required to comply with MiFID II transaction reporting. This shows how regulatory-driven KDB implementations can succeed when governance is baked in from the outset. Read the case study 

    • Building sustainable teams – In one project, we implemented a dedicated KDB team build, assembling and upskilling a permanent internal capability that reduced reliance on ad hoc contractors and ensured knowledge transfer. Read the case study 

    • Front-office latency challenges – Our work with a VP of KDB in low-latency trading demonstrated how blending q expertise with architectural leadership can optimise performance while reducing operational risk. Read the case study 

    • Trade surveillance – We enabled a global client to deploy a real-time KDB surveillance system, providing audit-ready monitoring capabilities that are directly relevant to today’s EU AI Act and consolidated tape environment. Read the case study 

    • Algorithmic trading systems – In eFX, we helped design and implement market-making algorithms powered by KDB, combining real-time analytics with automated execution logic. Read the case study 

    • E-commodities trading – We supported a Tier-1 firm in delivering KDB-driven trading systems for commodities, highlighting how the technology underpins resilience and scalability in volatile asset classes. Read the case study 

     

    C. Best practice trends in 2025 

    1. Phased rollouts: institutions increasingly begin with one regulatory or risk use case (e.g., trade surveillance or tape compliance) before expanding KDB across the enterprise

    2. Hybrid delivery models: combining permanent hires with niche contractors and partner pods reduces both knowledge risk and delivery lag

    3. Compliance-by-design: embedding lineage, logging, and replay features during build, not after, ensures MiFID II, Basel III, and AI Act obligations are met at lower total cost

    4. Resilience and continuity: operational resilience rules are driving adoption of multi-cloud architectures, chaos testing, and disaster-recovery by default 

     

    Implications 

    KDB’s performance is a given; the differentiator in 2025 is execution. Institutions that succeed are those that: 

    • Design for compliance upfront 

    • Blend skills and sourcing models intelligently 

    • Invest in sustainable teams and knowledge transfer 

    Caspian One’s delivery record shows that these principles are not theoretical: they are proven approaches that continue to reduce risk, accelerate ROI, and ensure strategic resilience in highly regulated, data-intensive environments. 

  • Summary 

    The business case for KDB in 2025 is framed less around potential and more around measured, evidenced returns. Institutions are increasingly asked by boards and regulators to demonstrate not just performance gains, but also compliance assurance, operational resilience, and faster time-to-value. ROI is now calculated in both financial and regulatory terms. 

     

    A. Evidence of ROI 

    • Forrester TEI Study (2024, updated 2025) 

    • Found that KDB delivers a 315% return on investment over three years, with payback in under six months when deployed effectively 

    • Benefits quantified included: 

    • $10.1 million in efficiency gains from faster analytics and reduced developer time 

    • $1.6 million in avoided costs by decommissioning legacy systems 

    • $7.8 million in improved business outcomes from better risk and trading decisions 

    • Operational cost reduction 

    • Migrating workloads to cloud-native KDB (AWS, Azure, Snowflake) reduces infrastructure overhead and enables elastic scaling 

    • Case examples from KX and its partners highlight 30–40% lower TCO in cloud deployments versus on-premise equivalents 

    • Productivity uplift 

    • With PyKX and SQL interfaces, 95% of workflows can now be executed in Python, reducing onboarding costs and accelerating cross-team delivery 

    • Benchmarks show up to 100× speed improvement compared to pandas for time-series analysis 

     

    B. Strategic Value Beyond ROI 

    • Regulatory Insurance 

    • Compliance fines for misreporting (e.g., under MiFID II) regularly reach into tens of millions of euros 

    • KDB’s immutable, time-stamped audit logs provide a cost-effective safeguard against reputational and financial damage 

    • Competitive Differentiation 

    • Real-time analytics in pricing, execution, and risk create measurable trading advantage 

    • Quant hedge funds cite KDB as “the de facto standard for historical and real-time analysis in trading” 

    • Faster Innovation Cycles 

    • With cloud marketplaces and Python integration, new use cases (e.g., consolidated tape adapters, surveillance dashboards) can be prototyped and deployed within weeks, compared to multi-month timelines in 2023 

     

    C. 2025 Trends in Investment Strategy 

    • Incremental rollouts dominate: firms focus on a single high-value use case (e.g., consolidated tape compliance, intraday risk aggregation) before scaling. This reduces project risk while proving business value quickly 

    • Outcome-based procurement: instead of raw staff augmentation, institutions increasingly contract for defined deliverables (trade monitoring platforms, compliance modules), aligning supplier incentives with ROI 

    • Hybrid operating models: many firms use build-operate-transfer (BOT) approaches with partners - ensuring quick delivery while embedding long-term internal capability 

     

    Implications 

    Boards and regulators no longer ask whether KDB is fast; they ask whether the investment translates into measurable outcomes. The institutions that are winning are those that: 

    • Build business cases on quantified efficiency and risk reduction 

    • Use incremental adoption to generate early proof points 

    • Align procurement and delivery models to outcomes, not headcount 

    KDB’s ROI in 2025 is thus measured not just in trading edge, but in compliance assurance, operational resilience, and accelerated innovation, the factors that define competitive advantage in today’s financial markets. 

  • Summary 

    The future of KDB in financial services will be shaped less by the database itself (whose speed and reliability are already proven) and more by the skills ecosystem that surrounds it. As regulation, AI, and cloud reshape capital markets, the demand for hybrid technical, regulatory, and strategic expertise is intensifying. 

     

    A. What we forecast in 2024 

    In last year’s report, we anticipated: 

    • Growing demand for AI/ML integration with KDB 

    • Broader adoption of cloud-native KDB deployments 

    • Increasing need for cross-skilled developers (q + Python + cloud) 

    • A shortage of practitioners able to bridge regulatory compliance and real-time analytics 

    All of these forecasts have since been validated, but the scope of demand is now broader and more urgent. 

     

    B. Skills in demand, 2025 

    • Core technical 

    • q/kdb+ fundamentals (feed handlers, tickerplants, time-series joins, replays) remain essential 

    • Python-first adoption (PyKX) makes Python fluency mandatory - 95% of KDB workflows in 2025 can now be executed from Python APIs 

    • SQL familiarity, especially for those bridging legacy systems with modern analytics 

    • Cloud and DevOps 

    • Cloud deployment skills: AWS, Azure, Snowflake integration for KDB workloads 

    • Containerisation, Kubernetes orchestration, CI/CD pipelines for real-time analytics 

    • Compliance-aware cloud architecture (EBA outsourcing, DORA resilience, EU/UK localisation rules) 

    • AI and vector integration 

    • Vector database literacy (similarity search, embeddings) now matters in surveillance, fraud, and NLP-driven workflows 

    • Familiarity with KDB.AI capabilities and integration with model pipelines 

    • ML Ops: building, monitoring, and governing AI models trained on streaming and historical KDB data 

    • Regulatory and governance 

    • Data lineage, logging, and auditability as skills, not just compliance checkpoints 

    • Knowledge of MiFID II consolidated tape schemas, Basel risk data requirements, and AI Act transparency obligations 

    • Increasing overlap between technology and compliance roles 

    • Strategic and leadership 

    • Ability to build and scale KDB teams sustainably (team-build models, internal academies, knowledge transfer) 

    • Cross-disciplinary leadership: orchestrating quants, engineers, risk/compliance SMEs, and data scientists into coherent delivery pods 

     

    C. Market evolution and sourcing trends 

    • Global scarcity persists: ITJobsWatch data shows UK medians for KDB Developers at £95,000 in 2025, but top roles in London and New York pay multiples of this; reflecting structural scarcity 

    • Nearshore hubs (Poland, CEE) are increasingly leveraged for scaling non-front-office work, with comp levels at ~$70–80k senior cash vs £145k+ London medians, highlighting a viable cost-capacity strategy 

    • Internal bootcamps and cross-skilling: firms are now training strong Python/quant engineers into q/kdb, with PyKX lowering the barrier to entry 

     

    D. What institutions should invest in now 

    1. Cross-training programmes: accelerate internal conversion of Python and cloud engineers into q-capable practitioners 

    2. Governance as a capability: embed lineage, logging, and explainability into job roles, not just IT policies 

    3. Hybrid delivery models: balance high-cost London/NY leads with nearshore delivery pods to achieve scale 

    4. Future horizon skills: monitor adjacent areas such as quantum-safe algorithms, real-time ESG analytics, and digital asset compliance, which are expected to interact with KDB in the coming 2–3 years 

     

    Implications 

    The institutions that succeed with KDB in 2026 and beyond will not be those with the largest infrastructure, but those with the right blend of skills: q expertise anchored by Python fluency, cloud and AI readiness, and governance competence. The scarcity of these hybrids makes partnerships essential. 

    Caspian One’s Data & Analytics Practice sits at the centre of this skills landscape, providing clients with both immediate access to scarce expertise and the frameworks to build sustainable internal capability. 

  • Summary 

    KDB adoption in financial services is accelerating, but the market challenge remains consistent: expertise is scarce, projects are complex, and regulatory scrutiny is intensifying. Caspian One’s Data & Analytics Practice is built precisely to solve these problems. With more than a decade of heritage in KDB and a formal partnership with KX, we combine unrivalled access to talent with proven delivery models that reduce risk, accelerate time-to-value, and embed long-term capability in client organisations. 

     

    A. Our heritage and positioning 

    • Deep KDB roots: Caspian One has been delivering KDB solutions since 2010, supporting over 40 financial institutions across front, middle, and back-office functions 

    • Recognised partner: Trusted by KX (the creators of kdb+) as a system integrator, with formal partnership established to accelerate adoption of KDB Insights and cloud-native platforms 

    • Sector specialists: Our Data & Analytics Practice is dedicated to financial services, combining technical acumen with domain knowledge in trading, risk, compliance, and data governance 

     

    B. Proven delivery, demonstrated outcomes 

    Caspian One’s track record reflects the breadth of KDB use cases now shaping financial services: 

    • Regulatory alignment: Designed a KDB-powered platform to meet MiFID II reporting standards for a cash equities desk, demonstrating compliance-by-design in high-pressure environments (Case Study) 

    • Team capability build: Established a permanent KDB team within a Tier-1 bank, reducing reliance on contractors and securing long-term delivery resilience (Case Study) 

    • Front-office optimisation: Delivered low-latency trading enhancements for FX and e-commodities systems, reducing execution times and improving resilience under volatile market conditions (Case Studies, e-commodities) 

    • Surveillance and control: Implemented KDB-driven trade surveillance systems capable of detecting anomalies in real time - capabilities that are directly relevant to EU AI Act auditability requirements (Case Study) 

    • Algorithmic trading: Designed and deployed KDB-powered market-making algorithms for eFX desks, balancing performance with governance (Case Study) 

     

    C. How we work with clients 

    • Flexible engagement models: from outcome-based pods and managed services to individual specialist augmentation, we adapt delivery to client needs 

    • Cross-functional teams: blending q developers, cloud engineers, compliance SMEs, and data scientists into agile pods aligned to regulatory and business priorities 

    • Knowledge transfer focus: ensuring clients don’t just receive a solution, but retain the capability to own, scale, and evolve it independently 

     

    D. Why clients choose Caspian One 

    1. Credibility in KDB: a decade of proven delivery, trusted by KX and leading institutions 

    2. Access to scarce skills: direct network of KDB/q specialists across London, New York, and nearshore hubs such as Poland and CEE 

    3. Alignment to regulation: experience delivering MiFID II, Basel III, and trade surveillance solutions ensures compliance-by-design 

    4. Partnership mindset: we act as an extension of client teams, invested in outcomes rather than transactions 

     

    Implications for financial institutions 

    In 2025, the institutions that succeed with KDB will be those that can implement quickly, prove compliance, and scale sustainably. Caspian One’s Data & Analytics Practice delivers the people, process, and partnership to make that possible. 

  • Conclusion 

    Eighteen months on from the 2024 KDB Insights Report, the role of KDB in financial services has matured from specialist trading technology to enterprise-wide data backbone. The industry no longer debates whether KDB is the fastest - that is a given. Instead, the focus is squarely on how KDB enables compliance, resilience, and AI innovation in a regulated, cloud-first environment. 

    • Adoption has broadened: from front-office trading desks to risk, compliance, and surveillance functions 

    • Technology has advanced: Python/SQL access, cloud-native delivery, and vector capabilities have expanded usability across teams 

    • Regulation is now live: MiFID II consolidated tape, Basel III Finalisation, and the EU AI Act all demand real-time, auditable, transparent data: KDB’s sweet spot 

    • Skills are the bottleneck: institutions that secure hybrid talent (q + Python + cloud + AI) gain a competitive advantage; those that don’t face stalled projects and rising costs 

    • ROI is measurable: Forrester quantified a 315% return over three years, with real-world savings and risk reduction now validated by multiple institutions 

    The lesson is clear: KDB has become both a compliance necessity and a competitive differentiator. Firms that invested early are now running ahead; laggards face compressed timelines and heightened risk exposure. 

     

    Next Steps for Financial Institutions 

    • Assess your position 

    • Map existing KDB use cases against regulatory obligations and high-value business priorities 

    • Identify gaps in compliance-readiness, skills, and scalability 

    • Adopt an incremental strategy 

    • Focus first on one or two critical use cases (e.g., consolidated tape reporting, intraday risk) to prove ROI quickly 

    • Scale from there into broader enterprise adoption 

    • Secure the right expertise 

    • Blend permanent, contractor, and partner-delivered skills to overcome talent shortages 

    • Invest in cross-training to develop hybrid q/Python/cloud engineers internally 

    • Embed compliance and resilience by design 

    • Treat governance, lineage, and operational resilience as design principles, not afterthoughts 

    • Ensure cloud and data architectures align with EU, UK, and global regulatory frameworks 

     

    A Note from Caspian One 

    To conclude this update, Ben Rutter, Practice Lead for Data & Analytics at Caspian One, extends a personal invitation. Ben has been at the centre of the international KDB community for over a decade, working closely with both clients and subject matter experts to deliver complex, high-value outcomes. 

    “I’d welcome the chance to share my perspective on the state of the KDB market in 2025, and to hear directly about your priorities, challenges, and ambitions in this space. Whether it’s compliance pressures, skills shortages, or opportunities to leverage AI, these are conversations I’m having daily with institutions across the industry; I can bring those insights to you.” - Ben Rutter, Practice Lead, Data & Analytics 

    We invite you to connect with Ben directly to explore how Caspian One can help you overcome challenges, secure scarce expertise, and maximise the value of KDB in your organisation. 

  • Caspian One internal / proprietary 

    • Caspian One, Data & Analytics KDB Insights Report 2024 (PDF, Jan 2024) 

    • Caspian One client case studies: 

    KX / KDB official resources 

     

    Technical benchmarking & commentary 

     

    Regulation & compliance 

     

    ROI & business value 

     

    Talent & skills 

Disclaimer

This report has been prepared by Caspian One for informational purposes only. It is not intended as investment advice, legal guidance, or a substitute for independent due diligence. While every effort has been made to ensure accuracy, Caspian One accepts no liability for any errors, omissions, or outcomes arising from the use of this content. References to regulatory frameworks (including MiFID II, Basel III/CRR3, the EU AI Act, and others) are provided for context only and should not be interpreted as legal advice. Case studies and success stories reflect Caspian One’s delivery experience but may have been anonymised or generalised to protect client confidentiality. External statistics, benchmarks, and third-party insights are drawn from reputable sources and cited where applicable. Readers are advised to consult professional advisors before making business or regulatory decisions based on this material. 

Kdb Market Intel:  Data & Analytics Report | 2024

Amid Uncertainty, Opportunities Emerge…

Published March 2024
By Caspian One’s Data & Analytics (kdb) Practice

Innovation in Data & Analytics

2023, fair to say, was a turbulent year for most.

Economic uncertainty loomed large as layoffs and sluggish tech hiring prevailed. Meanwhile, the rapid integration of AI into mainstream applications began reshaping coding practices and challenging traditional approaches to skill development within companies. 

But amid this uncertainty there lies vast opportunities for innovation. By 2029, the big data analytics market is projected to surpass 655 billion U.S. dollars, a substantial leap from its 2021 valuation of around 241 billion. This rapid growth highlights the profound impact of data analytics across industries, driving transformative change at every step.

Here at Caspian One, we're dedicated to driving innovation and excellence in the field of data analytics. Our enduring partnership with KX, the architects of kdb, reflects our commitment to fostering advancements in this area. With connections throughout the kdb community and access to subject matter experts, we're uniquely positioned to provide valuable insights. 

In this report, we explore the latest trends, challenges, and opportunities shaping the future of data and analytics. From emerging technologies like GenAI to strategic implementations, we navigate the complexities of the data landscape, offering actionable insights for 2024.

2023 and the year ahead;

State of the tech market

In 2023, the tech hiring market saw its fair share of challenges, but amidst the ups and downs, one area stood out with unwavering demand: data and analytics-related skills. 

Despite initial concerns about economic stability, the latter half of 2023 brought promising signs of recovery. Throughout this period, data and analytics professionals remained in high demand, reflecting the critical role they play in driving innovation and growth across industries. 

Python - a key language in the realm of data science and analytics - saw a surge in demand, closing the gap on Java's longstanding dominance.

This shift underscores the increasing emphasis on data-driven technologies and artificial intelligence, where Python excels. Referencing HackerRank’s 2024 Developer Report - Python set to overtake Java in HackerRank testing. “In 2022, Python’s test creations were just 32% of Java’s. In 2023, they accounted for 74%.” 

The rise in AI-related skills, particularly in the third quarter of 2023, reflects the growing importance of artificial intelligence in shaping the future of work. From machine learning to natural language processing, proficiency in these areas has become essential for organizations seeking to harness the power of data. 

Referencing 365 DataScience - “We observe a noticeable spike in companies seeking various skills related to developing AI models. Most notably, the demand for natural language processing skills has increased from 5% in 2023 to 19% in 2024.” 

Roles in data engineering have proven resilient amidst fluctuating demand, highlighting the enduring need for professionals skilled in managing and analysing vast datasets.

Additionally, the widespread adoption of AI tools like ChatGPT and GitHub Copilot further emphasizes the industry's embrace of cutting-edge technologies. 

Looking ahead to 2024, optimism abounds among data and analytics professionals, with the majority anticipating better conditions and new opportunities for advancement.

As the demand for data-driven insights continues to rise, those equipped with the necessary skills and expertise are poised to thrive in an increasingly data-centric world. 

Q&A: trends, challenges, skills & strategy

In this Q&A section, we feature insights from James Corcoran, SVP of Ecosystem Development at KX and Ben Rutter, Global Strategic Practice Manager for Data & Analytics at Caspian One - as they share their perspectives on industry trends, challenges, and strategies. 

Meet Ben Rutter of Caspian One
Global Strategic Practice Manager
Data & Analytics (kdb)

Meet James Corcoran of KX
SVP of Ecosystem Development
Data, AI, Timeseries, Vector

  • In 2023, the financial services industry witnessed a surge in data and analytics investment, driven by advancements like AI and new technologies such as Starburst, Databricks, and Snowflake. James highlighted, "A lot of organizations are investing more heavily in data & analytics due to the rise of AI."

    Ben noted the significance of AI offerings, allowing clients to analyse large volumes of unstructured data rapidly. Moreover, the integration of PyKX with kdb facilitated greater accessibility and integration, catering to a broader user base. 

  • In the financial services industry, kdb technology has significantly enhanced data handling and analysis capabilities over the past year. James emphasized its pivotal role in delivering faster query performance, efficient management of large datasets, and support for real-time processing. He noted, "New innovations including fully SQL and Python support make it easier to adopt kdb tech on platforms such as Databricks and Snowflake."

    Ben highlighted kdb's adaptability to user needs, with the introduction of new products and advancements such as AI capabilities for analysing unstructured data. He also noted the significance of PyKX, stating, "PyKX allows Python to be more integrated into the core product, opening up to a wider user channel." 

  • In 2023, financial institutions encountered various challenges when implementing and optimising kdb technology. James highlighted integration complexities, data security concerns, and the need to optimise system performance as the most significant hurdles. He noted, "Financial institutions addressed these challenges through collaboration with experienced technology partners, rigorous testing, and implementing robust security measures." Additionally, Ben highlighted the challenge of meeting the high demand for skilled professionals during implementation, underscoring the complexities of securing SMEs.  

  • For kdb technology in 2023, the most valuable skills and expertise centred around q, Python, and Cloud, according to James. He emphasized, "With most KX customers increasing their usage, upgrading to the latest product offerings, and migrating on-prem environments to the cloud, the most important skills were q, Python, and Cloud."

    In contrast, Ben highlighted the broader impact of market challenges and the expanding technology community on talent acquisition strategies. He noted a shift towards new industries such as aerospace, defence, space, broadcast, and e-commerce - signalling opportunities for skills acquisition beyond the financial services sector. 

  • For the integration of kdb in financial services, James highlighted the dominance of GenAI in emerging technology conversations.

    He pointed to forthcoming technology partnerships between KX and major players like AWS, Microsoft, Google, Snowflake, and Databricks, emphasizing the potential for combining data management, security, scalability, analytics, AI, and GenAI.

    Ben equally mentioned Starburst, Databricks, and Snowflake as emerging technologies impacting the use of kdb in financial services. 

  • For C-suite executives seeking to harness kdb technology for competitive advantage in 2024, James recommended, "Take a use-case-driven approach. Start with what internal and external customers are looking to achieve. Design for security and scalability. Make talent your #1 concern."

    In line with James's insights, Ben also recommended prioritising strategic partnerships and exploring innovative applications to drive growth and diversification.

  • James, "Regulatory changes concerning data usage in AI platforms will likely emphasize privacy, security, and ethical use. Data lineage will emerge as a crucial feature of data platforms to ensure explainability in genAI use cases." In a response complementing James's insights, Ben highlighted the importance of proactive compliance measures and strategic partnerships with regulatory experts to navigate evolving regulatory landscapes effectively, ensuring continued adherence to compliance standards while leveraging the full potential of kdb technology.

  • In anticipating the skills landscape evolution within the financial sector, James stressed the importance of continuous learning programs and the increasing relevance of genAI, LLM, and vector database skills. He stated, "Cloud and security are table stakes."

    On the other hand, Ben emphasized the perpetual need for data skills, particularly highlighting the significance of tick data management, stating, "Tick data is essential to financial markets."

  • Looking ahead to 2024, Ben predicts significant growth in PyKX and AI-associated add-ons, indicating a trend towards enhanced functionality and integrations within the Kdb ecosystem.

    Vector databases will become the most widely implemented data technology, and enterprises will implement knowledge bases and hunt for gold in enterprise data lakes.

    Cloud teams will optimise data pipelines for FinOps.

    AI will change the current approach to data management, and knowledge graphs will become essential for linking data between different enterprise silos.

    Enterprises that combine AI with real-time data will make the most advances.

KX’s Evolution:
A Technical Deep Dive

In the dynamic landscape of 2024, KX distinguishes itself through a profound technological evolution, transitioning from a specialised database provider to a pioneering force in generative AI and advanced analytics. This transformation is underscored by a significant enhancement of its core technologies, broadening its market applicability and embracing the future of data-driven decision-making. 

Central to KX's technical evolution is the refinement of its unique three-part engine. This engine synergises three advanced capabilities: 

Embedded Complex Event Processing (CEP)

Facilitates real-time analysis and decision-making by processing streams of data from various sources, enabling immediate response to complex event patterns.

Historical and Real-Time Data Storage

Offers the ability to store, query, and analyse data across time dimensions, providing insights from both historical trends and real-time data flows.

High-Performance Time-Series Database

Maintains its status as the world’s fastest time-series database, optimised for high-velocity, high-volume data, crucial for applications requiring nanosecond precision.

The democratisation of KX's technology, through the integration of Python and SQL functionalities, represents a strategic pivot towards inclusivity and accessibility.

This approach not only expands the addressable market but also significantly lowers the entry barrier for developers unfamiliar with KX's traditional q programming language. By aligning with widely used languages and technologies, KX facilitates a smoother adoption curve and fosters a more vibrant developer ecosystem around its products.

A pivotal aspect of KX's technical strategy is its foray into generative AI with the introduction of Kdb AI. Leveraging the inherent speed and efficiency of its vector database, KX aims to unlock new potentials in AI applications, from natural language processing to complex data analytics.

The vector database's architecture, emphasising columnar storage and efficient data compression, becomes a cornerstone for AI-driven queries, enabling rapid access to large datasets and facilitating the advanced analytics that generative AI models require. 

Addressing the challenges of the modern data landscape, KX's evolution reflects a commitment to solving the intricate problems of data volume, velocity, and variety. The shift towards a more accessible and versatile technology stack, including the strategic inclusion of Python and core SQL functions, marks a significant milestone in KX's journey. This transition not only enhances KX's appeal to a broader audience but also cements its position as a leader in the next wave of technological innovation. 

Reflecting on the transformation, a quote from Mark Bannon, President of Global Financial Services at KX during our recent partner meetup captures the essence of this journey:

"The biggest flip was the introduction of Kdb Insights, Insights Enterprise with Python and core SQL functions, which began to democratise how the core datasets can be used."

This statement encapsulates the strategic reorientation towards making complex data analytics more accessible and impactful across industries. 

In conclusion, KX's story in 2024 is one of technical mastery, strategic foresight, and the relentless pursuit of innovation.

By redefining its technological foundations and embracing the challenges of the future, KX is poised to lead the charge in the exploration and exploitation of the next frontier of data analytics and artificial intelligence. 

Drawing from KX's narrative of transformation, our own company's evolution stands as a testament to strategic growth and diversification. Much like KX's expansion from a niche player to a leader in data analytics and AI, we have broadened our horizons beyond our original scope, emerging as a comprehensive professional services provider.

We now address the multifaceted challenges within the financial services and broadcast industries, delivering resource, skills, and experience-based strategies with the same emphasis on speed, cost efficiency, and commercial flexibility that KX applies to its data solutions.

Caspian One’s integrated service propositions - spanning Professional Services, Resource Augmentation, and Specialist Skills - parallel KX's multifaceted approach, ensuring both companies remain at the forefront of innovation and operational excellence in our respective industries. 

Our Data & Analytics (kdb) Narrative

This year marks Caspian One’s twentieth in business - a tremendous landmark. Ours has always been a story of innovation and adaptation.

In our time, the economic and technological playing fields in which we operate have undergone vast transformations… as we have reflectively as a company.

From our grassroots in specialist financial services recruitment, over two decades, we’ve matured and optimised our potential.

In 2024, we’re most recognised and credited for our abilities to solve technology-driven, resource-based, and project delivery challenges that demand speed, scale, and flexibility. 

Our Data & Analytics (kdb) Practice is no exception to this transformative process.

Originating from a simple kdb+/q skills enquiry back in 2010, the Practice (within the safe hands of Global Strategic Practice Manager Ben Rutter) today services the resourcing requirements of more than 40 financial institutions throughout the UK, USA, Canada, Europe and APAC.

Our team of resources spans a diverse range, from Senior kdb Developers to Quant Developers, kdb Analysts, Architects, Infrastructure Developers, Market Data Engineers, and Support Professionals, ensuring we deliver tailored solutions to our clients' unique needs. 

More than this, having earned our place in the close-knit kdb community through credible achievements and reputation, our team now sits centrally between both our clients and SME networks.

This empowers us to help both parties succeed in their goals... be those personal stories of career development or successes aligned with skills acquisition and project deliverables. 

Our practice caters to a diverse clientele, including investment banks, hedge funds, prop trading firms, and crypto clients, with outreach into broadcast and telecommunications markets. Beyond kdb, we cover a wide range of technologies, including Snowflake, AI, KX Insights, Cloud, Java, JavaScript, C#, C++, Scala, Haskell, Kotlin, Python, Ruby, PHP, Node.JS, React, Angular, HTML, UX, DevOps, BA/PM, QA, Infrastructure, and Support. 

With a unique partner-led practice model, combined with a rich network of SME partners and backing from kdb creators, KX, we are positioned as leaders in the field. Leveraging our market dominance within kdb+ and across mid-senior talent markets, we offer unparalleled access to niche kdb, data, and analytics SME resources. With a focus on quality, expertise, and risk mitigation, we empower clients to make informed decisions and maximize the value of their investments.

Talking of KX, when asked about our partnership, James Corcoran has this to say: 

“The recent partnership with Caspian One as system integrators for kdb technology signifies a commitment to enhancing implementation efficiency and customer support. This collaboration is aimed at providing clients in the financial sector with seamless integration, enhanced services, and reliable support.” 

" To create partnerships where technology, innovation, and human ingenuity combine seamlessly, empowering individuals and organizations to reach their full potential and thrive on transformative change. "

As we celebrate our twentieth year in business, we remain firmly aligned with our brand vision.

With a proven track record of success and a dedicated team of experts, we look forward to continuing to exceed expectations, forge strong partnerships, and drive positive outcomes in the years to come. Connect with us today, and let's embark on the next chapter of success together. 

In conclusion

This report underscores the robust demand for data and analytics skills, particularly within the domain of kdb technology, against a backdrop of dynamic market conditions.

Noteworthy trends include Python's ascendancy, the growing importance of AI-related proficiencies, and the resilience of data engineering roles.

Looking forward, a sense of optimism pervades, with a strategic focus on harnessing AI, cloud technologies, and targeted talent acquisition strategies to foster innovation and gain competitive advantage.

Our Data & Analytics (kdb) Practice stands poised to address our clients' evolving needs, offering tailored solutions, unparalleled expertise, and strategic partnerships aimed at unlocking the full potential of data-driven insights. 

Meet with the Data & Analytics Practice