Databricks Consulting & Implementation
Build a governed lakehouse that serves BI, machine learning, and AI from a single copy of your data — on AWS, Azure, or Google Cloud.
What is Databricks?
The volume of data a typical enterprise handles — transactions, operational events, application telemetry — has outgrown the batch ETL pipelines many organizations still depend on. Those pipelines tend to hit one of two walls: they cannot keep pace with the throughput, or they keep pace only by letting systems drift out of agreement with one another.
Databricks answers this with a single platform for ingesting, transforming, governing, analyzing, and serving data at scale, built on distributed compute.
It began as a managed Apache Spark service and grew into the lakehouse architecture. More recently it has shifted again — positioning itself as a data and AI platform, with much of its recent work aimed at giving AI systems governed access to authoritative enterprise data.
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Consolidate scattered sourcesBring operational databases, SaaS tools, files, and event streams into one governed store.
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Power reporting and analyticsServe BI tools from warehouse-grade tables without maintaining a separate warehouse.
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Train and deploy modelsFull ML lifecycle through MLflow and Mosaic AI, on the same governed data.
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Supply data to AI agentsGive applications and agents access to authoritative data under central policy.
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One workspace for the whole teamEngineers, analysts, scientists, and BI practitioners work against the same assets.
Database vs. Warehouse vs. Lake vs. Lakehouse
These four terms are often used interchangeably. The real differences between them explain why the lakehouse was proposed at all.
Database
Stores and serves records for day-to-day operations — the reads and writes behind a running application, known as Online Transaction Processing (OLTP). A DBMS handles storage, concurrency, and the query interface. Databricks now also offers Lakebase, a managed Postgres OLTP database, so this boundary is less clean than it once was.
Data warehouse
Consolidates data from many source systems into a single, heavily structured store holding current and historical records. Its purpose is analytical rather than operational: teams query it to find patterns, produce reports, and drive the dashboards that make up business intelligence.
Data lake
Takes the opposite approach to structure. Data arrives from many sources and is kept in its original form, with no obligation to fit a schema on the way in. It accepts almost any format — JSON, CSV, TSV, Avro, ORC, Parquet — and is often used first as inexpensive storage.
Data lakehouse
Combines the two. It keeps the scale, format tolerance, and low storage cost of a lake, and adds the guarantees that make a warehouse trustworthy: ACID transactions, schema enforcement, and governed access. One store serves BI, ML, and AI without a separate copy for each.
| Type | Primary purpose | Data characteristics |
|---|---|---|
| Database | Online Transaction Processing (OLTP) | Structured, current operational data; accessed through a DBMS |
| Data warehouse | Analytics, reporting, and BI dashboards | Highly organized current and historical data from multiple systems |
| Data lake | Low-cost storage and future analytics | Raw, unprocessed data in many formats: JSON, CSV, TSV, Avro, ORC, Parquet |
| Data lakehouse | BI, ML, and AI on any data, in one system | Data lake scale and cost, plus warehouse-style management and ACID transactions |
End-to-end architecture
Source systems are ingested once, governed centrally, and then serve analytics, ML, agents, and applications from the same copy of the data.
What the platform is made of
The lakehouse is delivered through a set of named products. Knowing what each one covers is the fastest way to make sense of Databricks documentation, pricing, and release notes.
Delta Lake & Iceberg
Delta Lake is the open source storage layer that makes the lakehouse work — Parquet files plus a transaction log, supplying ACID transactions, schema enforcement, and time travel on ordinary object storage. Apache Iceberg is also supported, with v3 generally available.
Unity Catalog
The governance layer for data and AI assets: a central catalog of tables, views, volumes, models, and functions with fine-grained access control, automatic lineage, and auditing applied consistently across workspaces and clouds.
Photon & Databricks SQL
Photon is a vectorized C++ query engine that speeds up SQL and DataFrame workloads with no change to user code. Databricks SQL surfaces it through SQL warehouses, including serverless warehouses that start in seconds.
Lakeflow
The unified data engineering product: ingestion through Lakeflow Connect and its 100+ managed connectors, declarative pipelines through Lakeflow Declarative Pipelines (previously Delta Live Tables), and orchestration through Lakeflow Jobs.
Mosaic AI & Agent Bricks
The model and agent side of the platform: model serving, vector search, fine-tuning, and frameworks for building and evaluating agents. Agent Bricks automates much of the work of tuning production agents against your own governed data.
AI/BI & Genie
Dashboards plus Genie, a conversational interface that lets business users question governed data in plain language. Genie One extends this into an agentic assistant working across analytical and operational data alike.
The medallion architecture
Rather than transforming source data into its final shape in a single step, data moves through three sets of tables — bronze, silver, and gold — each holding to a stricter contract than the one before it.
Bronze keeps raw data exactly as it arrived, appended and never rewritten, so a faulty transformation downstream can always be replayed without going back to the source system. Silver is the cleaned, deduplicated, conformed layer that most analysts work against. Gold holds the business-level aggregates that feed dashboards, reports, and machine learning features.
Note that medallion is a convention rather than a product — there is no “medallion” switch to turn on. It is a naming and structuring habit the community adopted and Databricks documentation promotes.
Why teams adopt Databricks
Cloud-native
Designed for cloud deployment from the outset, running on AWS, Azure, and Google Cloud. Customer workloads run on cloud compute, increasingly through serverless options where Databricks manages that compute entirely.
Governed storage
Data files and tables live in cloud object storage with access governed through Unity Catalog. External locations and storage credentials define where data sits and who may reach it; volumes hold files that are not tables.
Open formats, no lock-in
Data written as Delta Lake or Iceberg tables stays readable by engines outside Databricks, which is not true of a proprietary warehouse. Object storage underneath keeps it cheap and durable at any scale.
Unified governance
Access rules are defined once and applied consistently, while Delta Sharing distributes data to other organizations without duplicating it. Lineage tracking, audit logs, and identity provider integration complete the picture.
Data science and AI tools
MLflow handles experiment tracking and model management, while Mosaic AI and Agent Bricks extend the same governed foundation to fine-tuning, serving, and agent evaluation.
Collaborative workspace
Notebooks support Python, R, Scala, and SQL side by side and move into production quickly. Engineers, scientists, and analysts work against the same assets instead of passing files between one another.
Six reasons the architecture won
One copy of the data
No separate warehouse extract to keep in sync. Analysts, scientists, and ML engineers read the current version rather than working from stale copies.
ACID on object storage
The Delta transaction log brings warehouse-grade correctness to cheap cloud storage — atomic writes, schema enforcement, and time travel.
Open formats
Delta Lake and Apache Iceberg are open standards. Your data stays readable by other engines, so the platform decision stays reversible.
Governance in one place
Unity Catalog applies access control, lineage, and auditing across workspaces and clouds, rather than per-tool policies that drift apart.
BI, ML, and AI together
Dashboards, model training, and agents all draw on the same governed tables — no separate feature store pipeline to reconcile.
Elastic, serverless compute
Warehouses start in seconds and scale to the job. You are not sizing a cluster for peak load and paying for it around the clock.
Frequently asked questions
What exactly is a data lakehouse?
An architecture that keeps the scale, format tolerance, and low cost of a data lake while adding the guarantees of a data warehouse — ACID transactions, schema enforcement, and governed access. The practical result is one store that serves BI, machine learning, and AI without maintaining a separate copy of the data for each. Databricks introduced the term around 2020; it is now offered in some form by most major data platform vendors.
How is Databricks different from a traditional data warehouse?
A warehouse requires data to be structured and loaded before it can be queried, and generally stores it in a proprietary format. Databricks keeps data in open formats on your own cloud object storage, accepts structured, semi-structured, and unstructured data, and runs machine learning and AI workloads on the same tables that feed your dashboards.
Is DBFS still the recommended way to store data?
No. DBFS root and DBFS mounts are deprecated and no longer recommended. New accounts are provisioned without access to them, and from 30 September 2026 all new Azure Databricks workspaces are Unity Catalog only, with no DBFS root. New work should use Unity Catalog volumes, external locations, or workspace files. Older documentation and tutorials still reference DBFS heavily, which is a common source of confusion.
Which programming languages does Databricks support?
Python, SQL, Scala, and R, all usable side by side within the same notebook environment. Mixing them within one project is common — transformations in Spark SQL, modelling in Python or Scala, and visualization in whichever language suits the author.
What is the medallion architecture?
A three-layer convention for structuring pipelines. Bronze holds raw data exactly as ingested; Silver holds cleaned, deduplicated, conformed tables; Gold holds business-level aggregates for dashboards and ML features. The main benefit is recoverability — because Bronze is never rewritten, a bug in your transformation logic is fixed by reprocessing forward rather than re-ingesting from the source system.
Which clouds does Databricks run on?
AWS, Microsoft Azure, and Google Cloud. Azure Databricks is a first-party Azure service. Unity Catalog governance and Delta Sharing work consistently across all three, which matters for organizations operating in more than one.
Planning a Databricks implementation?
Whether you are migrating off a legacy warehouse, standing up Unity Catalog governance, or preparing your data for AI, we can help you scope it properly.