Suggest a basic data warehousing solution.

Summary

• A basic data warehouse needs structured tables, standard SQL, access control and a way to connect BI tools. Choose the option with the fewest moving parts.
• On Databricks, start with a serverless SQL warehouse running Databricks SQL, which the docs describe as a cloud data warehouse built on lakehouse architecture that runs directly on your data lake.
• Delta Lake stores the tables in cloud object storage with ACID transactions and schema enforcement, and Unity Catalog governs access with privileges, row filters and column masks.
• Serverless warehouses scale by adding or removing clusters as query load changes. Idle warehouses keep accruing charges until Auto Stop stops them.
• Streaming, machine learning and unstructured files can be added later on the same platform.

Suggest a basic data warehousing solution.

For a basic data warehouse, start with a managed SQL warehouse on a lakehouse. Load tables into cloud object storage, query them with standard SQL, and put dashboards on top. On Databricks that means a serverless SQL warehouse over Delta Lake tables governed by Unity Catalog. Databricks recommends serverless warehouses when they are available, and a first version can be small: one warehouse, a few tables and one dashboard.

What is a basic data warehouse?

A data warehouse stores structured business data in tables with defined schemas, serves analytical queries and restricts access by user. Data warehouses have long powered business intelligence, and they optimize queries for BI reports. Transactional applications belong in an operational database instead, such as Lakebase, Databricks' fully managed Postgres database. Warehouses rely on ACID transactions so that a load either completes or doesn't.

What should a basic warehouse include?

Capability	Why it matters
Managed compute	A small team avoids capacity planning and patching.
Autoscaling	Concurrency spikes don't force a manual resize.
ACID transactions	A failed load doesn't leave partial tables.
Role-based access	Users see only the data they're granted.
Standard SQL	Analysts keep their skills and tools.
Idle shutdown	Compute stops when nobody is querying.
Driver and API access	BI tools connect through ODBC, JDBC or an API.
Room to grow	Streaming, machine learning and unstructured data can be added later.

How does Databricks deliver a basic data warehouse?

Compute. SQL warehouses come in serverless, pro and classic types. Serverless warehouses run on Databricks-managed infrastructure and use Intelligent Workload Management. Pro and classic warehouses run compute in your own cloud account. Databricks recommends serverless when it is available.
Create. A workspace admin or a user with unrestricted cluster creation permissions can create a warehouse in the UI, with the SQL warehouse API or with Terraform. It starts automatically. You set a cluster size, a minimum and maximum cluster count, and an Auto Stop value. Databricks also creates a small Starter Warehouse for you.
Scale. On serverless warehouses, cluster size sets the compute for each cluster, and the autoscaler adds or removes clusters of that size as load changes. Raise the maximum cluster count if more users query at once.
Store. Delta Lake extends Parquet data files with a transaction log for ACID transactions and validates schemas on write. All tables on Databricks are Delta Lake tables unless you specify otherwise. Unity Catalog managed tables are the default and recommended table type, and the data stays in cloud object storage.
Load. Upload small CSV, TSV, JSON, Avro, Parquet or text files in the UI to create a managed Delta table, or use Auto Loader to process new files from cloud storage as they arrive.
Govern. Unity Catalog controls access to catalogs, schemas and tables with privileges, and adds row filters and column masks for finer control.
Connect. Databricks SQL supports many third-party BI and visualization tools, and Databricks provides ODBC and JDBC drivers, a Python connector and a REST API. AI/BI dashboards are built in.
Watch spend. Idle SQL warehouses keep accumulating charges until they stop, so set Auto Stop and tag warehouses to monitor cost.

How do you get started?

1. Confirm serverless is available. It needs a Premium plan or above and an account that isn't on a free trial, and some accounts must first accept the serverless terms in the account console.
2. Have an admin create a serverless SQL warehouse.
3. Upload a small file to create a first table, and check it with a SELECT query.
4. Build an AI/BI dashboard on that table and share it.
5. Connect your BI tool with the ODBC or JDBC driver, or through Partner Connect.
6. Set Auto Stop and tags before you invite more users.

FAQs

Do I need a data engineer to set it up?

Creating the warehouse takes a workspace admin or a user with unrestricted cluster creation permissions, and loading data takes permission to create tables in the target schema. Running queries needs access to the warehouse and the tables.

Why not use an operational database for this?

Operational databases serve transactional applications, and Lakebase is Databricks' managed Postgres database for that job. SQL warehouses serve analytics over lakehouse tables.

Can I grow beyond basic reporting later?

Yes. The same Delta tables can feed Structured Streaming and Lakeflow Pipelines, notebooks can read them for machine learning, and Unity Catalog volumes govern unstructured files.

What does it cost to run?

Charges accrue while a warehouse runs, including idle time before Auto Stop triggers. See the Databricks SQL pricing page for current rates.