AutessaDB

Data lake and analytics

A columnar data lake for events, logs, history and exports: bulk loads, partitions, analytical SQL, a semantic layer, plain-language Ask, data agents and archive tiers, all under the same security as your tables.

Create a free database

order_lines · data lake1.8B rows · columnarSAMPLE

rows1.8B

storage212 GB

partitionMONTH(order_date)

last load2h ago

loadmoderowsrejected

orders-2026-09.parquetAPPEND41.2M0

orders-2026-08.csvAPPEND39.8M12 reported

-- why it matters

What does data lake and analytics cover?

The AutessaDB data lake stores billions of rows in columnar storage: bulk-load CSV and Parquet, query with analytical SQL, ask in plain language and let agents answer.

Some data is read one record at a time; some is read a billion rows at once. AutessaDB - Columnar is the data lake for the second kind: lakehouse-style columnar storage you bulk-load from files and tables, analytical SQL, a semantic layer that keeps every answer consistent, plain-language Ask, agents that answer data questions, and archive tiers for data you must keep for years. It is not a separate warehouse: it is the same AutessaDB, with the same sharing, row rules and masks on every query.

Who works here

Analysts query in the SQL Workbench; operations and finance leads Ask in plain language; data owners load files and describe tables; agents answer questions with SQL tools; apps render dashboards; admins set retention and archive policies.

In one sentence each

Columnar data lake tables · Lakehouse-style columnar tables for events, logs, history and exports, built for aggregations, group-bys, window functions and joins over billions of rows.
Bulk loading and partitioning · Load CSV and Parquet files or whole AutessaDB tables, append or replace, see every bad row reported, and partition by date so queries read only what they need.
SQL Workbench · A SQL editor that knows your tables and measures, estimates scan size as you type, keeps history, and downloads results as CSV or Parquet.
Semantic layer · Descriptions, grain, synonyms, measures, joins and verified queries teach people and agents how to query a table, and Suggest with AI drafts them for review.
Ask in plain language · Type a question such as "monthly revenue by region this year" and Ask proposes SQL from your descriptions, measures and verified queries. Nothing runs until you click Run.
Data agents · One click creates describe, query and result tools for the tables you pick, so an agent can answer data questions with SQL, preferring your verified queries.
Security on every query · Sharing, row rules, hidden columns and masked columns apply to every SQL query, and masked values still group, count and join without ever being shown.
Retention and archive tiers · Retention deletes rows past a set age; the archive tier moves older days to compressed Parquet and restores them as a read-only table when you need them.

-- subtopics

Everything in data lake and analytics

Columnar data lake tables

Lakehouse-style columnar tables for events, logs, history and exports, built for aggregations, group-bys, window functions and joins over billions of rows.

Bulk loading and partitioning

Load CSV and Parquet files or whole AutessaDB tables, append or replace, see every bad row reported, and partition by date so queries read only what they need.

SQL Workbench

A SQL editor that knows your tables and measures, estimates scan size as you type, keeps history, and downloads results as CSV or Parquet.

Semantic layer

Descriptions, grain, synonyms, measures, joins and verified queries teach people and agents how to query a table, and Suggest with AI drafts them for review.

Ask in plain language

Type a question such as "monthly revenue by region this year" and Ask proposes SQL from your descriptions, measures and verified queries. Nothing runs until you click Run.

Data agents

One click creates describe, query and result tools for the tables you pick, so an agent can answer data questions with SQL, preferring your verified queries.

Security on every query

Sharing, row rules, hidden columns and masked columns apply to every SQL query, and masked values still group, count and join without ever being shown.

Retention and archive tiers

Retention deletes rows past a set age; the archive tier moves older days to compressed Parquet and restores them as a read-only table when you need them.

-- on screen

What it looks like

Bulk loading and partitioning

Load data · app_eventsAPPENDSAMPLE

sourcerowsrejectedstatus

events-2026-10.parquet412M0loaded

crm_export.csv9.4M31 reportedloaded

table · support_tickets1.2M0loading

partitioned by MONTH(event_date)estimateSAMPLE

JFMAMJJASOND

WHERE event_date >= DATE '2026-10-01' reads 3 of 12 partitions

SQL Workbench

SQL WorkbenchScans ~1.2 GBSAMPLE

SELECT region, date_trunc('month', order_date) AS month,
       SUM(net_amount) AS revenue
FROM order_lines
WHERE order_date >= DATE '2026-01-01'
GROUP BY 1, 2 ORDER BY 2, 1

regionmonthrevenue

NA2026-012,411,093.20

EMEA2026-011,502,118.45

APAC2026-01612,077.40

Download CSVDownload ParquetSave as verified query

-- faq

Questions about data lake and analytics

What is the AutessaDB data lake?

AutessaDB - Columnar, a columnar data lake built into AutessaDB for millions to billions of rows. You bulk-load files or tables, query with analytical SQL, ask in plain language and give agents tools to answer data questions, with the same security as every other table.

What files can I load into the data lake?

CSV and Parquet files, from your computer or fetched by the server, plus every row of an existing AutessaDB table.

Can the SQL Workbench change data?

No. Each run is one read-only statement; anything that changes data or settings is rejected before it runs.

What is a semantic layer?

Metadata that explains a table: what a row is, what columns mean, named measures, joins and verified example queries, so people and agents query it consistently.

Does Ask run queries on its own?

No. Ask proposes SQL and puts it in the editor; nothing runs until you click Run.

Can an AI agent answer questions about my data lake?

Yes. Give it the three generated tools and it describes the tables, writes SQL, runs it and reads the result.

Does row-level security apply to SQL?

Yes. Hidden columns cannot be selected, masked columns return masked values, and row rules filter what each person's queries see.

Can I keep data lake data for years without keeping it all queryable?

Yes. Set hotDays for the queryable window and archiveDays for total retention; older days move to compressed Parquet in the archive.

Other areas of AutessaDB

data_modelingTables and data modeling →AutessaDB tables are typed, related and computed: text, number, select and media columns, foreign keys, AutessaScript computed columns, write-once ledger tables and one-click knowledge bases.queries_and_apisQueries, APIs and SDKs →One query model everywhere: the same filters, sorts, aggregates and bulk writes work in the builder, the REST API, the CLI, scripts and agent tools.vector_searchSemantic search and AI →Vector search is a column setting, not a second database: flip it on and AutessaDB indexes, enriches and serves every value to people and agents.row_level_securityRow-level security and access →Every read and write, from a person, agent, script, app or API key, passes through the same row rules, column masks and access log.triggersTriggers and pipelines →Row changes become automation: triggers run tools, agents and writes, chain across tables, and leave a full execution history.files_and_appsFiles, apps and operations →AutessaDB is also the backbone for files and apps: Drives, app sign-in tables and per-visitor rules, with soft delete as the safety net.

$ autessa autessadb tables create

Data lake and analytics, built into one database.

Use it out of the box, then shape it to how your company works. Tables, the data lake, security and triggers are all the same AutessaDB, from the builder, the API or the CLI.

Create a free database

Read the AutessaDB guide or install the CLI.

100% customizable, code and all

Make AutessaDB fully your own.

Use it out of the box, then change anything. Ask Autobot, Autessa’s AI builder, for a new field, screen, report, workflow or agent and it builds it into your copy of the app. Or open the code and edit it yourself. Nothing is locked: the app, its data and its code are yours.

Start free and make it yours