Softobiz

DATA INFRASTRUCTURE AND PLATFORMS

Data infrastructure and platform engineering

Our data infrastructure and platforms connect ingestion, storage, processing and governance so analytics and AI can share dependable foundations.

  • A coherent, layered platform, not a pile of point tools
  • Open table formats, so your storage layer never traps you
  • Built to serve today's analytics and tomorrow's AI without a rebuild
THE PLATFORM, LAYER BY LAYER

A clear role for every platform layer.

The lakehouse pattern collapses the old split between a cheap, ungoverned lake and an expensive, governed warehouse.

One open store, with warehouse-grade transactions and lake-grade economics, serves both analytics and AI from the same governed copy of the truth.

IngestionBring in batch and streaming data reliably.CDC, event streams, managed connectors.
Storage (lakehouse)One open store from raw to curated.Open table formats over object storage.
Table formatACID transactions, time travel, schema evolution.Apache Iceberg, Delta Lake, or Hudi.
ProcessingTransform and refine at scale.Spark and dbt across medallion layers.
Serving and semanticConsistent metrics for BI, apps, and models.Semantic layer, query engines, feature and vector stores.
GovernanceCatalog, lineage, quality, and access across all layers.Unity Catalog-style governance, enforced as controls.

One open store serves analytics and AI from the same governed truth.

OPEN OR PROPRIETARY: HOW WE DECIDE

The defining choice is how much you standardize on open formats.

Both are legitimate. The right answer depends on your priorities, not on fashion.

Our bias is to keep your data in open formats even when you buy managed compute, so the storage layer, the part that is expensive and painful to move, never traps you.

Optimizes forPortability, no lock-in, multi-engine access.Speed to value, less operational overhead.
Best whenYou want long-term optionality and control.You want a turnkey platform and lean ops.
Trade-offMore architectural ownership up front.Vendor dependence; costs can concentrate.
Our defaultOpen table formats for storage, so data stays portable.Managed compute where it removes toil.

We build cloud-native on your platform, working with Databricks, Snowflake, BigQuery, Iceberg, Delta, Hudi, Spark, dbt, Airflow, Dagster, Kafka, Flink, and Unity Catalog, alongside your partner ecosystem.

WHAT THIS CATEGORY COVERS

Data infrastructure and platforms for your next workload.

01

Data Platforms

End-to-end platform design, build, and operation across ingestion, storage, processing, serving, and governance.

EXPLORE02

Open Data Lakehouse

An open, portable lakehouse on Iceberg, Delta, or Hudi, with warehouse-grade transactions and lake-grade economics.

EXPLORE03

Agentic Data Foundation

A governed, semantic, AI-ready foundation for agents and models, feeding them the same trusted copy of the truth.

EXPLORE
BUILT TO BE RUN, NOT JUST STOOD UP

A platform only its builders understand is a liability.

We design for operability from day one.

Through our dedicated-team model, we transfer the platform, with its runbooks and standards, to a team that keeps improving it long after go-live.

See the dedicated-team model
PROOF

One foundation, built once, for analytics and AI alike.

Blackwoods cloud data infrastructure
BLACKWOODS · DATA PLATFORM
CASE STUDY

100Ks

We replaced point-tool sprawl across Stibo, MSSQL, and warehouse systems with one governed cloud platform on Azure. The same foundation now serves live inventory, Power BI self-service, and AI-driven data quality without a rebuild in between.

100Ks OF SKUs UNIFIEDREAL-TIME OPERATIONAL VISIBILITYANALYTICS AND AI ON ONE STACK
READ THE STORY
RELATED

Where this connects.

PILLAR

Data and Analytics

EXPLOREANALYTICS

Data and Analytics Services

EXPLORETRANSFORMATION

Data and Cloud Transformation

EXPLOREAI PLATFORMS

Scaled GenAI and AI Platforms

EXPLORE
FREQUENTLY ASKED QUESTIONS

What platform leads ask us first.

A lakehouse collapses the old split between a data lake (cheap, flexible, ungoverned) and a warehouse (governed, structured, expensive) into one open store with warehouse-grade transactions and lake-grade economics. It serves both analytics and AI from the same governed copy of the truth, so you stop maintaining two disconnected stacks.

Both are legitimate; the right answer depends on your priorities. Our default keeps storage in open table formats such as Iceberg, Delta, or Hudi even when you buy managed compute, so the layer that is expensive and painful to move never traps you.

We build cloud-native on Databricks, Snowflake, or BigQuery, with Spark and dbt for processing, Airflow or Dagster for orchestration, Kafka and Flink for streaming, and Unity Catalog-style governance, alongside your existing partner ecosystem.

We design for operability and transfer the platform, with its runbooks and standards, to a team through our dedicated-team model, so it keeps improving long after go-live rather than becoming a liability only its builders understand.

BUILD THE FOUNDATION ONCE

What does your current platform make hard, slow, or expensive?

Tell us, and we will design the layered foundation that fixes it for analytics and AI alike.

Talk to our platform team