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Install nessie with Homebrew

Transactional Catalog for Data Lakes with Git-like semantics. Version 0.108.1 via Homebrew; verified 2026-06-24.

install

Additional install commands

macOS

Homebrewverified · 100%
brew install nessie

local Homebrew formula metadata

overview

Package summary

Transactional Catalog for Data Lakes with Git-like semantics

Commands and aliases

  • nessie

history

Project history and usage

Project Nessie is an Apache-licensed transactional catalog for data lakes with Git-like semantics. It was created in 2020 and applies branches, tags, commits, merges, and consistent multi-table visibility to Apache Iceberg-centered lakehouse workflows.

Project history

The GitHub repository was created on 2020-04-09. Project release notes list a 0.1.0 initial release on 2020-10-01, followed by rapid 2020 and 2021 releases adding Git-like CLI semantics, a Web UI, DynamoDB storage support, Python client and CLI improvements, Iceberg support work, Helm chart support, Spark SQL extension improvements, and API/client compatibility work.

The official docs describe Nessie as doing for data lakes what Git does for source code repositories: changes can happen independently and remain isolated until they are atomically and consistently applied. The docs also say existing Hive or Spark jobs can integrate Nessie through configuration rather than production code changes.

GitHub metadata retrieved on 2026-07-01 showed the repository under the projectnessie organization, written primarily in Java, with about 1.5k stars and 178 forks. The Homebrew formula API reported stable version 0.108.1 and 54 install-on-request events in the prior 30 days when checked on 2026-07-01.

Adoption history

Nessie's adoption is tied to the open table format ecosystem, especially Apache Iceberg. The project homepage names cross-table transactions, Git-inspired data version control, Hive, Spark, Dremio, Trino, Docker, and Kubernetes as part of its operating context. The GitHub README says Nessie supports Iceberg tables and views and focuses on working with the widest range of tools possible.

Nessie became part of the lakehouse packaging conversation because it offers a catalog-level answer to ETL isolation, rollback, staging versus production data, and multi-table visibility. Dremio's public article framed it as Git-like branching and version control for data engineers, and Apache Iceberg documentation points users to Nessie as a catalog option that requires a Nessie server.

How it is used

Package nerds install the `nessie` CLI to interact with a Nessie server: inspecting references, creating branches and tags, committing catalog changes, and supporting Iceberg/Spark workflows. Server-side users run Nessie via a Docker image or Kubernetes deployment, wire engines such as Spark, Trino, Hive, or Dremio to the catalog, and use branches to isolate ETL or experimentation before merging into production.

Why package nerds care

Nessie is significant because it turns catalog metadata into a versioned package-like object: tables and views become named, branched, tagged, and merged resources. For a package catalog, it represents the modern data-infrastructure side of version management rather than a conventional developer CLI.

Timeline

  • 2020-04-09: GitHub repository created.
  • 2020-10-01: 0.1.0 initial release.
  • 2021-10-08: 0.10.1 release notes mention client/API work, storage backend consolidation, and Spark SQL extension changes.
  • 2023-07-03: GitHub releases API page of the latest 100 releases reaches Nessie 0.64.0, showing sustained release cadence.
  • 2026-06-24: Nessie 0.108.1 release published.

Related projects

  • Apache Iceberg
  • Apache Spark
  • Apache Hive
  • Trino
  • Dremio
  • Delta Lake
  • Kubernetes
  • Docker

Sources

security posture

Risk level: orange

formula declares a Homebrew service.

Risk classifier

orange risk · medium confidence · infrastructure

Why

  • formula declares a Homebrew service

Signals

  • metadata:service

Install behavior

  • No Homebrew post-install hook is recorded in formula metadata.
  • Formula metadata declares a service or daemon block.
  • Homebrew bottle metadata is available for 6 platform targets.
  • Installs with 1 runtime dependencies.
  • Build metadata lists 1 build dependencies.

Recommended review

Before unattended agent use, check whether the tool reads plaintext credentials, writes remote state, publishes artifacts, or shells out to plugins.

executables

Installed executables

CommandKindExposureNote
nessiecliglobal executable

freshness

Version and freshness

These signals separate page generation age, package-manager activity, and upstream release comparison. Version lag is warned only when an evidence URL and comparable versions are present.

page generated2026-07-08
manager version0.108.1
manager updated2026-06-24
local dataok
upstreamcurrent
latest detectednessie-0.108.1

https://github.com/projectnessie/nessie

  • okNo freshness warnings were generated.

install metadata

Package metadata

Package keybrew:nessie
Version0.108.1
Package managerHomebrew
Package manager pagehttps://formulae.brew.sh/formula/nessie
Homepagehttps://projectnessie.org
Repositoryhttps://github.com/projectnessie/nessie
Upstream docshttps://projectnessie.org/guides/introduction
LicenseApache-2.0
Source archivehttps://github.com/projectnessie/nessie/archive/refs/tags/nessie-0.108.1.tar.gz
Last updated2026-06-24T19:02:47Z
Pulseupdated
Dependenciesopenjdk@21
Build dependenciesgradle
Bottleavailable (on arm64_linux, arm64_sequoia, arm64_sonoma, arm64_tahoe, sonoma, x86_64_linux)
Homebrew post-installnot defined
Servicedeclared

registry facts

Source database details

Source DatabaseHomebrew formula API
Taphomebrew/core
Full Namenessie
Version Scheme0
Revision0
Bottle Stable Root URLhttps://ghcr.io/v2/homebrew/core
Deprecatedno
Disabledno
Keg Onlyno
URL Keys
  • stable

source trail

Generated from repository data

This page is generated by av-web from the private package SQLite artifact built by scripts/generate-pkg-sqlite.py.

Used sources

  • Geiger risk classifier
  • Nucleus package database
  • av.db category and tag curation
  • cross-ecosystem install command graph
  • curated package history
  • package relationship graph
  • package version freshness
  • package-page enrichment