What Is a Data Platform?

One governed home for your data.

A data platform is the storage, compute and governance layer that everything else sits on — dashboards, models, applications and AI. Get it right and every project after it gets cheaper. Get it wrong and you pay for the same mistake again in every downstream build.

  • Architected — storage, compute and access designed before the first table lands.
  • Governed — catalog, lineage and access policy built in, not bolted on later.
  • Elastic — scales with load and bills to what you actually consume.
  • Open — standard formats and engines, so you're never locked to one vendor.

Our Offerings

Platforms we build.

Six building blocks. Yours may need three of them, not all six.

Lakehouse Architecture

Delta Lake and Apache Iceberg tables that serve BI queries and machine learning from the same governed copy of the data — no more warehouse-versus-lake standoff.

Cloud Data Warehouse

Snowflake, BigQuery, Redshift or Synapse designed around your query patterns, with warehouse sizing and workload isolation that keeps costs predictable.

Streaming Platforms

Kafka and cloud-native streaming for data that has to be current — inventory, payments, telemetry, fraud signals — with replay and exactly-once handling.

Operational & NoSQL Stores

Document, key-value and time-series stores chosen for the access pattern rather than the résumé, and tuned for the read and write shape you actually have.

Governance & Catalog

A searchable catalog, column-level lineage, data-quality tests and role-based access, so people can find data and auditors can trace it.

Cost & Performance Engineering

Partitioning, clustering, caching and autoscaling policy reviewed against real usage — most platforms we inherit are paying for compute nobody reads.


Our Approach

How a platform build runs.

1

Assess

Inventory the sources, consumers and the reports people actually open. Volume, freshness and access rules get written down before any tool is chosen.

2

Architect

Storage layout, compute model, security boundaries and cost envelope agreed on paper — with the trade-offs stated in plain language.

3

Build

Landing, curation and serving layers implemented as code, with CI, tests and environments from the first commit.

4

Migrate

Sources moved in priority order, with reconciliation against the legacy system so numbers are proven equal before anything is switched off.

5

Operate

Monitoring, cost alerts, quality checks and ownership handed to your team — with the runbook written down, not in someone's head.


Why PineApps

What you get.

🏛️

One Source of Truth

Finance, ops and product read the same numbers from the same governed tables — arguments move from whose figure is right to what to do about it.

💸

Predictable Cost

Compute and storage sized to real workloads, with alerts before a runaway query becomes an invoice.

🔓

No Lock-In

Open table formats and portable SQL mean the platform can outlive any single vendor decision.

⚡

Faster Every Project After

Once the platform exists, dashboards, models and AI features are weeks of work instead of months of plumbing.


Technologies

What we build on.

Lakehouse

DatabricksDelta LakeApache IcebergApache Spark

Warehouses

SnowflakeBigQueryAmazon RedshiftAzure Synapse

Streaming

Apache KafkaAmazon KinesisGoogle Pub/SubFlink

Operational Stores

PostgreSQLMongoDBCassandraRedis

Orchestration

Apache AirflowdbtDagsterStep Functions

Governance

Unity CatalogMicrosoft PurviewAWS Glue CatalogGreat Expectations

Ready to consolidate your data?

Tell us what your data estate looks like today and we'll come back with an architecture, a sequence and an honest cost estimate — within one business day.