Data & Analytics
Big Data Engineering
Distributed processing, streaming pipelines and data lakes for data that genuinely outgrows a single machine. But the first thing we usually tell people is that they don’t need any of it: most “big data” is modest data a well-built database handles far more cheaply. We say so before you spend on a cluster.
What Big Data means in practice
Who it’s for: Organisations with data that has genuinely outgrown a single machine or a database (real scale, real velocity), who want it engineered properly, and who would rather be told honestly if they don’t actually need it.
Big data is the most over-applied term in this industry. It is meant to describe data at a scale where a single machine and a traditional database can no longer cope: data whose volume, velocity or variety forces you into distributed systems that spread the work across many machines because no one machine is enough. That is a real and demanding engineering problem. But the great majority of organisations that believe they have it do not. They have modest data they think is big, and they are about to spend heavily on tooling built for a problem they don’t have.
The honest reality is that a well-designed PostgreSQL instance comfortably handles tens of millions of rows, and a modern cloud data warehouse (BigQuery, Snowflake, Redshift), will chew through billions on demand, far more cheaply and far more simply than a Spark or Hadoop cluster you have to run yourself. Big-data tooling is heavy, expensive, operationally complex, and needs specialist people to keep alive. Reaching for it when a database would do is not being ahead of the curve; it is over-engineering that will cost you money, time and momentum for years. We have seen more projects sunk by an unnecessary cluster than by a database that was “too small”.
So this service starts with a question, not a sale: is your data actually big? Usually the answer is no, and if it is, we will tell you plainly and point you at the simpler, cheaper thing that fits. When the answer is genuinely yes: datasets measured in tens of terabytes and up, event streams arriving faster than any single database can absorb, or machine learning over data that will not fit in memory anywhere, then distributed processing is the right tool, and it is one we build and operate properly. This is engineering for the real thing, offered honestly, and withheld honestly.
What you get
- A straight assessment of whether your data is genuinely big, and a recommendation to use a database or warehouse instead when it is not, before any cluster is built
- Distributed batch processing with Apache Spark, for transformations over datasets too large for a single machine
- Streaming pipelines built on Kafka for ingestion and Flink or Spark Structured Streaming for real-time processing of high-throughput event data
- A data lake on object storage with columnar formats such as Parquet, partitioned and laid out so queries read only what they need
- The batch-versus-streaming decision made deliberately per pipeline, not by fashion, with the cheaper, simpler option chosen wherever it fits
- Governance, lineage and access control designed to work at scale, so a large data platform does not become an ungoverned data swamp
- Cost modelled and monitored honestly, because the whole point of distributed systems is that they are expensive if run carelessly
What Big Data does for you
Money not burnt on a cluster you didn’t need
The most expensive mistake in this space is adopting big-data tooling for data that isn’t big. The licences, the specialist staff, the operational overhead, the complexity tax on every future change. Starting from an honest look at your actual scale, and choosing a database or warehouse when that is what fits, saves that money before it is spent. It is the benefit vendors cannot offer you.
Systems that keep working as data grows
When the scale is genuine, distributed processing means adding machines rather than hitting a ceiling. A pipeline built on Spark or a stream processed by Flink grows with your data instead of falling over at the next threshold, so the platform you build this year is still the platform you run in three, not a rewrite waiting to happen.
Real-time reaction where it earns its keep
Some decisions cannot wait for a nightly batch, fraud as it happens, monitoring that must alert now, personalisation in the moment. Streaming pipelines process events as they arrive, so the system reacts in seconds. We build this only where the value justifies the added complexity, because real-time is genuinely harder to run than batch and should not be chosen for its own sake.
Why teams choose us for Big Data
- We will tell you when you don’t need big data (which is most of the time), and point you at the cheaper database or warehouse, rather than selling you a cluster because clusters are what we sell
- Senior data engineers who have actually run distributed systems at scale do the work, so the trade-offs are made by people who know what breaks at three in the morning, not by someone reading a vendor’s reference architecture
- We operate what we build, which is the strongest possible reason to keep a platform as simple as the scale allows and to treat cost and governance as first-order concerns from day one
- We are blunt about the trade-offs of every choice (batch versus streaming, lake versus warehouse, distributed versus single-node), instead of defaulting to whichever is more impressive to put in a diagram
What Big Data includes
The concrete pieces of work this covers, scoped to what your problem actually needs.
Distributed batch processing with Spark
Apache Spark spreads a transformation across a cluster so datasets far larger than any single machine’s memory can be processed. We build Spark jobs that are correct, tuned and cost-aware, partitioned sensibly, spilling gracefully, and not quietly shuffling terabytes across the network because a join was written naively.
Streaming ingestion with Kafka
When events arrive continuously and faster than a database can absorb, Kafka is the durable, high-throughput backbone that decouples the systems producing data from those consuming it. We design topics, partitioning and retention for real throughput, see our Kafka technology page for how we approach it in depth.
Real-time stream processing
Apache Flink and Spark Structured Streaming process events as they land, aggregating, joining and enriching streams in flight, handling the genuinely hard parts of streaming like late-arriving data, windowing and exactly-once semantics that trip up naive implementations.
Data lakes and columnar storage
A lake on cheap object storage lets you land raw data of any shape first and structure it later. We build these on columnar formats such as Parquet, partitioned and organised so a query reads only the columns and partitions it needs. The difference between a lake that is cheap to query and a swamp that is expensive and slow.
Batch-versus-streaming architecture
The central design decision in any large data platform, and the one most often made by reflex. We choose per pipeline on the merits: batch where periodic is fine and far simpler to run, streaming only where the value of immediacy justifies the operational cost, and we are honest that streaming is the harder system to keep alive.
Governance and cost at scale
A large platform without governance becomes an ungoverned swamp nobody trusts, and without cost discipline becomes a bill nobody can explain. We build in cataloguing, lineage, access control and cost monitoring from the start, because retrofitting them onto a platform that is already large is far harder than designing them in.
Where it fits
A warehouse that finally hit a real wall
A genuinely large analytical workload, tens of terabytes, transformations that no longer finish in the window they have. That a single database or a naive warehouse setup can no longer serve. We introduce distributed processing where it is actually needed, often alongside the warehouse rather than replacing it, so the heavy jobs move to a system built for them and the rest stays simple.
High-throughput event streams processed in real time
Clickstream, telemetry, transactions or sensor data arriving in a continuous flood that must be processed as it lands, for fraud detection, live monitoring or in-the-moment personalisation. We build the Kafka ingestion and the Flink or Spark Streaming processing to handle the throughput without dropping events or falling behind under load.
A data lake for variety that resists tidy tables
Logs, JSON, images, semi-structured exports and unstructured text arriving in too many shapes and too fast to model into neat relational tables up front. We build a lake that lands it all cheaply on object storage in open columnar formats, so nothing is lost and structure can be applied later, for the questions and models that come after.
Machine learning over data that won’t fit in memory
Training or feature engineering over datasets far too large for one machine, where the computation itself has to be distributed. We build the distributed pipelines, feature generation across a cluster, data prepared and partitioned for training at scale. That make machine learning on genuinely large data tractable rather than a machine that runs out of memory and dies.
How we approach Big Data
We start by trying to talk you out of it. Before we design a single distributed pipeline, we want to know the actual size of your data, how fast it arrives, and what you need to do with it, because those three numbers usually reveal that a database or a cloud warehouse solves the problem for a fraction of the cost and complexity. This is not false modesty. Recommending the simpler tool is the single most valuable thing an honest data engineer does, and it is the advice that big-data vendors are structurally unable to give you.
When the scale is genuinely there, we build distributed systems the way they should be built: choosing batch or streaming deliberately per pipeline rather than defaulting to the more impressive one, laying data out in columnar formats so queries stay cheap, and treating governance and cost as first-class concerns from the start rather than problems to discover once the platform is large and unmanageable. And because we operate what we build, every decision is made by someone who will have to keep the cluster running at three in the morning, which is a powerful incentive to keep things as simple as the scale allows.
How we approach a big-data engagement
The first phase is the one that saves you the most money: establishing whether your data is genuinely big. We measure it (real volume, real arrival rate, real growth trajectory), and test it against what a well-tuned database or a cloud warehouse can actually do, which is far more than most people assume. If the honest conclusion is that you don’t need distributed systems, that is where we stop and tell you so, and we would consider that a successful engagement, because it spared you a cluster you would have regretted.
When the scale is real, we move to design: which processing model each pipeline needs, how data is laid out and partitioned, where the lake sits relative to any warehouse, and how governance and cost control are wired in from the start. We resist the temptation to make everything streaming or to build a lake for data that would sit perfectly well in a warehouse. Every component earns its place against the alternative of not having it, because in distributed systems every component you add is another thing that has to be operated and paid for.
Then we build incrementally against real data volumes, not a toy sample that hides the problems that only appear at scale. Distributed systems fail in ways single-node systems do not (data skew, network shuffles, stragglers, back-pressure), and those surface only under real load, so we test under it. The platform ships with the observability, cost monitoring and runbooks needed to operate it, because a distributed system you cannot see into is a distributed system you cannot keep running.
Distributed processing, and the batch-versus-streaming decision
The defining idea of big-data architecture is distribution: when data or computation exceeds what a single machine can hold, the work is spread across a cluster of many machines that process their share in parallel and combine the results. Spark does this for batch transformations; Flink and Spark Structured Streaming do it for continuous streams. This is what makes genuinely large-scale processing possible, and it is also what makes these systems heavy, because coordinating many machines introduces failure modes, tuning demands and operational overhead that a single database simply does not have. Distribution is a tool you reach for when you must, not a badge of sophistication.
The central architectural decision is batch versus streaming, and it is the one most often got wrong. Batch processing runs over a bounded set of data on a schedule (every hour, every night), and is dramatically simpler to build, reason about and operate; for most analytical needs it is entirely sufficient, and choosing it is a sign of good judgement, not a lack of ambition. Streaming processes each event as it arrives, giving real-time results, but at a real cost in complexity: you must handle late and out-of-order events, windowing, state that lives between events, and exactly-once guarantees that are genuinely hard to get right. We choose streaming only where the value of immediacy clearly justifies that cost, and batch everywhere else.
Storage sits underneath both. A data lake on object storage holds raw and processed data cheaply in open columnar formats like Parquet, where the columnar layout means a query touching three columns of a two-hundred-column dataset reads only those three: the difference between fast and cheap versus slow and expensive at scale. Sensible partitioning ensures a query for one day’s data does not scan a year’s. Often the right architecture is a lake and a warehouse together: the lake for cheap storage of everything and heavy distributed processing, the warehouse for fast structured analytics, rather than a doctrinaire choice of one. We design for what your queries actually do, not for a reference diagram.
Governance, access control and lineage at scale
Governance is harder at scale precisely because there is more of everything, more data, more sources, more consumers, more places a mistake can hide. A large data platform without governance does not stay a lake; it becomes a swamp, a sprawl of datasets nobody can find, trust or explain, which is one of the most common and most expensive failure modes in this field. We build cataloguing and lineage in from the start: what data exists, where it came from, what was done to it, and what depends on it, so the platform stays legible as it grows rather than becoming a liability that people work around.
Access control at scale cannot be a matter of trusting that everyone behaves. When data is distributed across a lake, a warehouse and a stream of live events, access has to be enforced consistently across all of them, at the granularity the data demands: table, column and sometimes row level, so a person or a job sees only what it should. We design least-privilege access into the platform and make it auditable, because with sensitive data at this scale a breach or a misuse is a serious event, and “we assumed access was fine” is not an answer anyone wants to give a regulator.
Personal and regulated data brings obligations that scale does not excuse, if anything it sharpens them, because a lake makes it easy to accumulate and retain data far beyond the purpose it was collected for. Under UK GDPR that is a genuine risk, so we are deliberate about retention, minimisation and the ability to locate and delete an individual’s data across a distributed platform, which is materially harder than in a single database and has to be designed for rather than bolted on. Governance is not a compliance afterthought here; at this scale it is part of the architecture or it does not exist.
Signs it’s time
- Your datasets are genuinely in the tens of terabytes and up (not gigabytes you can hold on one large machine), and a single database or warehouse has hit a real, measured wall you cannot tune your way out of
- You are ingesting event streams at a throughput and continuity that no single database can absorb (millions of events a minute, arriving constantly), and need to process them in real time as they land
- You need to run machine learning or heavy transformation over data far too large to fit in memory on any one machine, so the computation itself has to be distributed across a cluster
- You have wide variety (logs, clickstream, sensor, semi-structured and unstructured data), arriving too fast and in too many shapes to model into tidy tables up front, and need a lake to land it first and structure it later
Big data, a database and a data warehouse, knowing which you need
These three sit on a spectrum of scale, and knowing where your problem actually falls on it is worth more than any tool. A relational database like PostgreSQL is the right home for the vast majority of workloads: it handles tens of millions of rows without breaking a sweat, and with good indexing and modelling it stretches much further than people expect. Reaching past it should be a decision forced by a real, measured limit, not a hunch that your data feels big, and certainly not because a database sounds unfashionable. Most organisations never need to leave this tier, and that is a perfectly good place to be.
A cloud data warehouse (BigQuery, Snowflake, Redshift), is the next step, and it dissolves most of what people imagine they need big data for. These systems run analytical queries over billions of rows across a distributed engine you never have to operate, billed by usage, with none of the cluster-babysitting that Spark or Hadoop demand. For the overwhelming majority of “our data is too big for a database” problems, a warehouse is the answer: far cheaper, far simpler, and far faster to stand up than a self-managed distributed platform. We reach here long before we reach for a cluster, and it is where most of these conversations end.
Genuine big-data tooling (Spark, Kafka, Flink, a data lake), earns its place only past the point where even a warehouse struggles or the wrong shape of tool: datasets in the tens of terabytes and beyond, event streams that must be processed in real time as they arrive, wide unstructured variety that resists tidy tables, or machine learning over data that will not fit in memory anywhere. That is a real tier, and when you are in it these tools are the right and necessary answer, built properly. But the discipline that matters most is knowing which tier you are actually in, and being told the truth about it, especially when the truth is the cheaper, less impressive one.
Technologies we build it with
Chosen per problem, not per fashion. This is the stack we most often reach for on this work.
How we deliver
- 01
Discover
We map the system, the constraints and the business it serves, including the parts nobody documented.
Architecture brief
- 02
Architect
Decisions get made, written down and defended before a line of production code exists.
Decision records
- 03
Build
Short cycles against working software. You see progress in the product, not in a status deck.
Shipping increments
- 04
Operate
Monitoring, incident response and iteration. The system is alive, so the engagement is too.
Runbooks & SLOs
Want a straight answer on Big Data?
A short call with a senior engineer, before you write a brief. If Big Data is the wrong answer for your situation, we will say so and tell you what we think is right.
What changes
The right tool, honestly chosen
Either a proven case that your data is genuinely big and needs distributed systems, or (far more often), the cheaper database or warehouse answer, delivered before you spent on a cluster you didn’t need.
Scale that actually holds
When the scale is real, processing that spreads across many machines and keeps working as the data grows, batch or streaming, laid out so queries stay fast and costs stay proportionate.
Cost you can see and defend
Distributed platforms are expensive when run carelessly. Yours is modelled, monitored and kept in proportion to the value it produces, with the cost drivers named rather than discovered on the invoice.
Industries we serve
Domain knowledge changes what gets built. A few of the sectors we know before the first meeting.
How pricing works
- The first piece of work is often a short, focused assessment of whether you have a big-data problem at all, measuring your actual scale and growth, and testing it against what a database or warehouse can do. It is deliberately small and it frequently ends the engagement with a recommendation that costs you far less than a platform would have. We would rather charge for a day of honest assessment than for years of running infrastructure you did not need.
- When distributed systems are genuinely warranted, the main cost drivers are the number and complexity of pipelines, whether they are batch or streaming, and the scale of data being processed. Streaming pipelines are more involved to build and to operate than batch ones, and a platform with real-time requirements is a larger undertaking than one where periodic processing suffices: we scope and price those honestly rather than quoting a batch price for a streaming problem.
- Ongoing infrastructure cost is a first-class part of the conversation, not a surprise on the cloud bill. Distributed systems are expensive to run if built carelessly: an untuned Spark job or a badly partitioned lake can multiply your compute cost, so we model the running cost as part of the design and treat keeping it proportionate as part of the engineering, not an afterthought.
- Where a smaller first step makes sense. An assessment, or a single pilot pipeline built against real data before committing to a full platform: we will recommend it. It is far cheaper to learn early whether the architecture holds at your real scale than to fund an entire platform that discovers a fatal design flaw once it is large and in production.
Typical timeline
- 01
Scale assessment
Measuring your actual data volume, velocity and growth, and testing it honestly against what a database or warehouse can do. Short, and it frequently ends with a recommendation to use the simpler, cheaper tool, which is a successful outcome, not a failed one.
- 02
Architecture and design
When the scale is real: deciding batch versus streaming per pipeline, how the lake and any warehouse fit together, how data is laid out and partitioned, and how governance and cost control are built in from the start rather than retrofitted.
- 03
Build against real volumes
Building the pipelines incrementally and testing them under real data volumes, not a toy sample, because data skew, shuffles, back-pressure and the other ways distributed systems fail only appear at genuine scale.
- 04
Operation and handover
Shipping the platform with the observability, cost monitoring and runbooks needed to run it. Because we operate what we build, this is designed for keeping a distributed system alive in production, not just for passing a demo.
What working with us actually means
We talk you out of it when we should
Most organisations that think they have big data do not, and we say so, pointing you at the database or warehouse that fits, before you spend on a cluster. That advice is worth more than any pipeline, and it is precisely the advice a big-data vendor is structurally unable to give you, because their business depends on you needing the cluster.
Senior engineers who have run these systems
The trade-offs in distributed systems (batch or streaming, how to partition, when a shuffle will cost you), are made by people who have actually operated Spark clusters and Kafka streams at scale, not by someone assembling a reference architecture from a vendor’s slides. Experience here is the difference between a platform that runs and one that quietly haemorrhages money.
We operate what we build
Because we may be the ones keeping the cluster alive, every decision is made with operation in mind, which is the strongest possible force pushing a design toward the simplest thing that meets the real scale. It also means governance, cost monitoring and observability are built in from the start, not discovered as gaps once the platform is large.
Blunt about the trade-offs
Distributed systems, streaming and data lakes each buy you something real and cost you something real, and we tell you both sides of every choice rather than defaulting to whatever looks most sophisticated. You get the honest case for and against, so the decision is yours and made on the merits.
How to engage us
Three ways to work with us on this, chosen to fit the problem, not our margin.
- Dedicated team A standing team that works only on your product, in your rituals and your tooling. Best when the roadmap outlives the project. Ongoing product development
- Staff augmentation Named senior engineers embedded into your existing team, reporting into your leads. Best when you know what to build and need capacity. Filling a capability gap
- Software outsourcing A defined outcome delivered end-to-end by an accountable team. Best when you want the result owned, not just the hours filled. Outcome-owned delivery
Related services
Part of Data Engineering. Other work we do alongside this.
Common questions
How do I know if I actually have big data?
By the numbers, not the feeling. If your data fits comfortably in a well-tuned database (tens of millions of rows, gigabytes rather than tens of terabytes), you do not have big data, whatever it feels like. If a cloud data warehouse could handle your analytics, you almost certainly do not need distributed tooling either. Genuine big data means data that has outgrown even those: tens of terabytes and up, event streams too fast for any single database to absorb, or computation that will not fit in memory on any one machine. We measure your actual scale before recommending anything, and the honest answer is usually that you don’t need it.
Why not just use Spark or Hadoop to be safe, won’t it scale better later?
Because it is expensive insurance against a problem you probably will never have. A Spark or Hadoop cluster is heavy to run, needs specialist people, and taxes every future change with complexity, and if your data is modest, all of that cost buys you nothing a database would not have done more cheaply and reliably. Building for a scale you don’t have is over-engineering, and it slows you down for years. A well-designed database or warehouse scales far further than most people assume, and if you ever genuinely outgrow it, that is the right time to add distributed processing, not before.
What is the difference between batch and streaming, and which do I need?
Batch processing runs over a fixed set of data on a schedule (hourly, nightly), and is far simpler to build and operate; for most needs it is entirely sufficient and choosing it is good judgement. Streaming processes each event the moment it arrives, giving real-time results, but at a real cost: you have to handle late and out-of-order events, windowing, state between events, and exactly-once guarantees that are genuinely hard. You need streaming only when the value of reacting immediately (fraud detection, live monitoring, in-the-moment personalisation), clearly justifies that added complexity. For everything else, batch is the honest answer, and we default to it.
How does Kafka fit into a big-data platform?
Kafka is the durable, high-throughput backbone for ingesting event streams. It sits between the systems producing data and the systems consuming it, absorbing a continuous flood of events that no single database could take directly, and letting multiple consumers read the same stream independently. It is what makes real-time pipelines possible: events land in Kafka, and a stream processor like Flink or Spark Structured Streaming reads from it and processes them as they arrive. It is genuinely useful at real throughput, and genuine overkill if your events are modest. We cover how we design and operate it in depth on our Kafka technology page.
What is a data lake, and how is it different from a data warehouse?
A data lake stores raw data of any shape (structured, semi-structured, unstructured), cheaply on object storage, typically in open columnar formats like Parquet, and lets you apply structure later. A warehouse stores structured, modelled data optimised for fast analytical queries. The lake is for landing everything cheaply and for heavy distributed processing over huge or varied data; the warehouse is for fast, structured analytics. They are not rivals: the right architecture for a genuinely large platform is often both, with the lake feeding the warehouse. But if your data is modest and already structured, you likely need neither a lake nor big-data tooling, just a warehouse, or even a well-built database.
Thinking about Big Data?
Tell us the problem in your own words, not in requirements. A senior engineer reads it and comes back with a straight view on whether Big Data is the right answer here, or what would be.
- 01A senior engineer reads it. Not a form queue, and not an account manager.
- 02We reply either with questions or with a straight answer that we are not the right fit.
- 03If it looks like a fit, a technical call with the person who would actually run the delivery.
- 04Then scope, effort and risk in writing, before anyone signs anything.