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Engineers on every build
Pipeline architecture through the dashboard
US-based
Team, start to finish
No unnamed subcontractor added mid-project
Day one
You own the infrastructure
In your cloud account, from the first commit
10 biz days
To start
From a signed scope to the first sprint
Before the infrastructure
Most 'big data' problems aren't actually big data problems
The label gets attached to any project involving more than a handful of tables, and it's the reason so many teams end up running distributed infrastructure they didn't need for volume a well-tuned Postgres instance would have handled. The honest assessment comes first.
Slow queries are usually an indexing problem, not a volume problem
A query that used to be fast and now isn't is far more often missing an index, an unoptimized join, or a table that needs partitioning than it is a sign you've outgrown a relational database. Diagnose before you migrate.
'Big' is a moving, relative target
A dataset that felt enormous a decade ago fits comfortably in memory on commodity hardware today. The honest question isn't the row count — it's whether a single well-provisioned database, with proper indexing and maybe a read replica, can serve your actual query patterns at your actual concurrency.
Distributed systems trade simplicity for scale you have to actually need
Spark, distributed warehouses, and multi-node pipelines solve real problems at real volume, and they add operational surface area — more to monitor, more that can partially fail, more specialized skill to hire for. That trade is only worth making once the single-node option has genuinely run out of headroom.
The real signal is growth rate, not current size
A dataset growing predictably toward a volume that will outgrow a single instance within the planning horizon justifies building for scale now. A dataset that's been flat for two years doesn't, regardless of how large it already looks on a dashboard.
Query patterns matter more than table size
A large table queried by a single indexed key is a non-event. The same table scanned in full for an ad hoc analytical query, run by five different teams against slightly different logic, is the actual problem — and it's a warehouse and modeling problem, not purely a volume one.
The assessment should produce a number, not a recommendation to modernize
A credible answer states the current volume, the growth rate, and the point at which the current architecture actually breaks — and says plainly when that point is years away and the money is better spent elsewhere.
What it costs
Big data engagement types
Real ranges for the engagements we're actually asked to run. The variable that moves a quote isn't row count — it's how many source systems feed the pipeline and whether the data needs to arrive in seconds or is fine arriving once a day.
| Engagement | Commitment | Timeline | What's included |
|---|---|---|---|
| Data architecture assessment | Fixed scope | 1 – 3 weeks | Volume and growth-rate analysis, a query-pattern review, and a plain recommendation — including when the honest answer is not migrating anything. |
| Batch pipeline build | Fixed scope | 4 – 10 weeks | Scheduled extraction, transformation, and loading from your source systems into a warehouse, with data quality checks and failure alerting built in. |
| Streaming pipeline build | Fixed scope | 6 – 14 weeks | Event-driven ingestion for data that needs to reach a decision within seconds, with backpressure handling and replay for when a downstream consumer falls behind. |
| Warehouse or lake design and migration | Fixed scope | 6 – 16 weeks | Schema and modeling work, historical data migration, and the access layer analysts and dashboards actually query. |
| Ongoing pipeline operations | Ongoing retainer | Ongoing | Monitoring, schema-drift handling as source systems change, and cost management as volume grows. |
Ranges assume US-based senior engineers and include data quality checks and monitoring, not just the happy-path pipeline. A quote well under these bands is usually missing schema-drift handling or alerting, and that gap surfaces later as a silently corrupted dashboard nobody catches until a decision gets made on bad numbers.
Batch vs. streaming
The decision that shapes everything downstream
This is the single highest-leverage architecture decision in a big data project, and it should be driven by the decision the data feeds, not by which pattern is more interesting to build.
Batch is the right default
If a report, a dashboard, or a decision only needs yesterday's numbers, a scheduled batch pipeline is simpler to build, cheaper to run, and far easier to debug than a streaming system. Most business reporting genuinely fits this case.
Streaming earns its cost at a specific kind of urgency
Fraud detection, live inventory, or anything where a decision made an hour late is a decision made too late justifies the added complexity of event-driven ingestion. Ask what actually happens if the number is a day stale before committing to streaming — often, nothing does.
Streaming systems fail in ways batch systems don't
Backpressure when a downstream consumer falls behind, exactly-once versus at-least-once delivery guarantees, and replay when something needs reprocessing are all real design decisions with real failure modes, not solved problems you get for free by choosing Kafka.
Lambda and Kappa architectures solve the same problem two ways
Running both a batch and a streaming path and reconciling them (Lambda) is more moving parts but more forgiving of bugs in either. A single streaming path that can also replay history (Kappa) is simpler to operate once it's built, and harder to get right the first time.
Most teams overestimate how much of their data needs to stream
It's common to build a streaming pipeline for an entire dataset when only one or two specific metrics actually need sub-minute freshness. Splitting the pipeline — streaming what genuinely needs it, batching the rest — is usually cheaper and more reliable than streaming everything.
Storage architecture
Warehouse, lake, or lakehouse — and what actually decides it
The tool matters less than whether the data is structured enough to model ahead of time, and whether the people querying it are analysts writing SQL or engineers running arbitrary code against raw files.
A data warehouse fits structured, known questions
Snowflake, BigQuery, or Redshift store schema-modeled data optimized for the SQL queries a BI tool or analyst actually runs. It's the right default when the questions being asked are largely known in advance.
A data lake fits raw, varied, or not-yet-modeled data
Object storage holding data in its original format — logs, JSON, images, whatever a source produces — deferred structuring until someone actually needs to query it a specific way. It's the right call when you're capturing data before you're sure how it'll be used.
A lakehouse tries to get both without maintaining two systems
Modern platforms add warehouse-style structure and query performance on top of lake-style cheap raw storage. Worth evaluating when a team is tired of maintaining both separately, though it's a newer pattern than either predecessor and worth vetting against your actual query tools.
Schema-on-write vs. schema-on-read is the real trade-off
Defining structure before data lands (warehouse) catches bad data early and costs flexibility. Defining it at query time (lake) keeps ingestion simple and pushes the cost of messy data downstream to whoever queries it later.
Data modeling is the work that actually takes the time
Picking a storage product is a day's decision. Designing the dimensional model, the naming conventions, and the transformation logic that makes the data trustworthy to query is the multi-week part of the project, and it's the part a rushed timeline usually cuts first.
Cost grows with volume whether anyone budgeted for it or not
Storage is cheap; the compute spent scanning it on every query is not, especially on a lake with no partitioning discipline. Partitioning, clustering, and query-cost monitoring belong in the design from the start, not retrofitted after the first surprising invoice.
Data quality and governance
A pipeline is only as trustworthy as its failure handling
The parts of a big data project that don't show up in an architecture diagram are usually the parts that determine whether anyone actually trusts the resulting dashboard.
Schema drift is the normal case, not the exception
Source systems change their fields, types, and formats without warning a downstream pipeline. Detecting drift and failing loudly is far better than silently ingesting malformed data that corrupts a report three steps downstream.
Data quality checks belong in the pipeline, not in a spreadsheet someone reviews monthly
Automated checks for null rates, referential integrity, and expected value ranges catch problems the day they happen instead of the month an analyst notices a number looks wrong.
Lineage matters the first time a number is questioned
Being able to trace a dashboard figure back through every transformation to its source is the difference between a quick answer and a multi-day investigation when a stakeholder asks 'why does this not match what I expected.'
Access control has to be designed, not bolted on
Who can query what, and whether sensitive fields are masked or excluded for a given role, is a governance decision that belongs in the pipeline design — especially once a lake or warehouse becomes the shared source multiple teams query.
Cost and query governance prevent the platform from becoming its own problem
An unrestricted analytical query against a large table can be expensive on usage-priced platforms. Query review, workload management, or usage limits keep one bad query from becoming an unplanned bill.
Related
Related services
What big data projects usually need alongside the pipeline itself.
Questions
Big data questions we get before scoping a build
What teams ask before a first call.
Start with whether a single well-provisioned relational database, properly indexed, can serve your actual query patterns at your actual concurrency. A large row count alone doesn't answer that question — a large table queried by an indexed key is a non-event, while a much smaller table getting scanned in full by five different ad hoc queries can be the real bottleneck.
An assessment should look at current volume, growth rate, and query patterns, and give you a plain answer — including when that answer is that you don't need to change anything yet.
Batch is the right default for most business reporting: if a dashboard only needs yesterday's numbers, a scheduled pipeline is simpler, cheaper, and easier to debug than a streaming one. Streaming earns its added complexity when a decision made even an hour late is a decision made too late — fraud detection or live inventory are the classic cases.
Most teams that build streaming pipelines end up needing it for only one or two specific metrics, not the whole dataset. Splitting the pipeline — streaming what genuinely needs sub-minute freshness, batching the rest — is usually the cheaper, more reliable design.
A warehouse stores schema-modeled, structured data optimized for the SQL queries analysts and BI tools actually run — it's the right fit when the questions being asked are largely known in advance. A lake stores data in its raw, original format and defers structuring until someone needs to query it a specific way, which suits capturing data before you're sure how it'll be used.
A lakehouse tries to combine both in one platform. It's worth evaluating if maintaining two separate systems has become its own overhead, though it's a newer pattern and worth vetting against the query tools your team actually uses.
Schema drift should be treated as the normal case, not an edge case — pipelines built to detect a changed field, type, or format and fail loudly are far safer than ones that silently ingest malformed data and corrupt a report several steps downstream.
That means automated data quality checks — null rates, referential integrity, expected value ranges — running inside the pipeline itself, plus alerting that reaches a person quickly when something doesn't match what was expected.
Distributed infrastructure solves real problems at real volume, but it also adds real operational cost: more to monitor, more that can partially fail, and specialized skills to hire for. That trade is worth making once a single well-tuned instance has genuinely run out of headroom for your actual query patterns and concurrency — not before.
The assessment phase exists specifically to answer this honestly, including saying so when the answer is that your current architecture has years of headroom left.
Partitioning and clustering data so queries scan only what they need, plus query-cost monitoring and workload governance so one unrestricted analytical query doesn't turn into a surprise bill on a usage-priced platform. These belong in the design from the start, because retrofitting them after the first expensive invoice is a harder conversation than planning for them up front.
Storage itself is typically the cheap part. The compute spent scanning that storage on every query is what actually drives cost, and it scales with how the pipeline and warehouse were modeled, not just with how much data exists.
You do. Pipelines, warehouse or lake infrastructure, and the transformation code should live in your own cloud account and your own repository from the first commit, not a vendor's shared environment.
That's the only arrangement that lets you bring in a different team later, audit exactly what a pipeline does with your data, or extend it yourselves without asking anyone's permission.