One version of the numbers
Board packs, investor updates and team dashboards that all pull from the same tested models.
Data platforms
Big data solutions
Make your data usable. We build the pipelines, warehouses and dashboards that turn scattered records into one trusted source your whole business can query, with the governance to keep it clean.
1 placeto ask a question instead of five conflicting reports
Minutesfrom event to dashboard for streaming sources
10x to 100xdata growth absorbed without a redesign
Testedevery table, on every run, before anyone trusts it
The problem with data today
Every team has numbers. Few teams have the same numbers.
Data sits in a CRM, a finance system, a warehouse tool, a marketing platform and a stack of spreadsheets. Each export is a snapshot, each definition is slightly different, and every board pack starts with an argument about which figure is right.
A data platform fixes that by pulling every source into one place on a schedule, transforming it with tested logic and publishing clean tables that everyone builds on. Definitions live in code, so revenue means the same thing in every report.
We design for the volume you will have in three years, not the volume you have now, and we keep the running cost visible so the platform pays for itself in decisions made faster.
What teams build on it
The platform is the foundation. These are the things it makes possible.
Product and usage analytics
Event data modelled into funnels, retention and feature adoption without a separate analytics silo.
A base for machine learning
Clean, documented, historical data is exactly what model training needs, so AI projects start faster.
Regulatory reporting
Auditable pipelines that produce the same figures every time, with lineage back to the source.
Marketing and revenue operations
Attribution, cohort value and account scoring pushed back into the tools those teams work in.
Operational monitoring
Near real time dashboards on the metrics that need a response within the hour, not the month.
Ingestion and storage
Get every source into one governed place
Connectors pull from databases, SaaS APIs, files and events on a schedule, landing raw data in a warehouse or lakehouse with full history kept.
Raw stays raw. Nothing is thrown away, so a definition can change later and be recalculated from the beginning without going back to the source systems.
- Managed connectors plus custom ones for niche systems
- Change data capture from operational databases
- Raw, staging and modelled layers kept separate
- Cost controls with partitioning, clustering and lifecycle rules
01Platform layers
- Raw
- Exact copy of source, append only
- Staging
- Cleaned, typed, deduplicated
- Marts
- Business models, tested and documented
- Serving
- BI, reverse ETL, APIs
Transformation
Business logic in code, reviewed and tested
Transformations are written as version controlled models with tests on every table. A change goes through review, runs against sample data and only ships when the tests pass.
Lineage is automatic, so anyone can see where a number came from and what breaks if a source changes.
- Modular models with reusable logic
- Tests for uniqueness, nulls, ranges and relationships
- Column level lineage and a searchable data catalogue
- Documentation generated from the code
02Quality gates
- On commit
- Compile, lint, unit tests
- On merge
- Full run on staging data
- On deploy
- Freshness and volume checks
- Continuous
- Anomaly alerts on key metrics
Analytics and activation
Answers for analysts, and data back into the tools teams use
A semantic layer defines metrics once so dashboards, notebooks and ad hoc queries all agree. Analysts explore without breaking anything and without re deriving revenue every time.
Reverse ETL pushes clean data back out: audiences to marketing, account health to sales, usage to support, so the platform drives action rather than just reporting.
- Governed dashboards plus safe self serve exploration
- A metrics layer shared across every tool
- Reverse ETL to CRM, marketing and support platforms
- Row level security so people see only their data
03Consumption
- BI
- Looker, Power BI, Metabase, Superset
- Notebooks
- Governed access with the same metrics
- Activation
- Reverse ETL on a schedule
- Embedded
- Metrics in your own product
What a Spykra data platform guarantees
The habits that separate a platform people trust from a pile of pipelines.
Tested data
Every model has assertions about what good data looks like, and a failed test stops the bad data before it reaches a report.
Known freshness
Each table publishes when it last updated and how often it should, with alerts when a source is late.
Governance
Access by role, personal data classified and masked, retention enforced and an audit of who queried what.
Cost visibility
Spend broken down by pipeline and query, with the biggest consumers surfaced so optimisation is targeted.
Backfills without pain
History is kept in the raw layer, so a new definition can be applied to years of data in one run.
A real catalogue
Every table and column described, owned and searchable, so people find data instead of asking around.
How a data platform comes together
Value from the first sources, then breadth added source by source.
Assess and design
We inventory your sources, the questions you cannot answer today and the tools your teams use, then choose the warehouse and the shape of the platform.
First slice
Two or three key sources ingested, modelled and connected to a dashboard that answers a question that mattered before.
Widen and harden
More sources, tests, catalogue, governance and cost controls, plus a semantic layer so metrics are defined once.
Hand over and support
Your analysts are trained to extend the models. We support the platform and add sources as your needs grow.
Warehouses and tooling
Storage and compute
BigQuerySnowflakeDatabricksClickHouseDuckDBPostgres
Movement and modelling
dbtAirbyteFivetranDagsterAirflowKafka
Consumption
Power BILookerMetabaseSupersetHightouchCensus
Questions about data platforms
Do we need a warehouse if we already have a BI tool?
Usually yes. A BI tool queries data but does not clean, join and define it consistently. Without a warehouse and a modelling layer, each dashboard re implements the logic and they drift apart. The warehouse is where the shared truth lives.
How much does a data platform cost to run?
Cloud warehouse costs scale with usage and are often a few hundred to a few thousand pounds a month for a mid sized business. We design partitioning, scheduling and query patterns to keep that predictable, and we report spend by pipeline.
Can our analysts keep working during the build?
Yes. We stand the platform up alongside your current reporting and migrate one area at a time. Nothing is switched off until the replacement is trusted.
What about personal data and compliance?
Personal data is classified on ingest, masked or hashed where it is not needed, access is granted by role and retention rules are enforced automatically. Every query is logged for audit.
Will this help our future AI plans?
Directly. Model training needs clean, well documented, historical data with clear definitions, which is exactly what the platform produces. Teams with a data platform ship AI features far faster.
Tell us the question you cannot answer
The one where three reports disagree. We will show you the platform that would settle it for good.
Start a project Spykra Technologies UK Ltd, London and Mumbai.