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Deequ with Apache Spark on Azure & AWS

Data quality library for Spark that declares constraints and computes metrics over large datasets, flagging anomalies inside ETL pipelines. Built and maintained by WIEWAVE for Azure Marketplace and AWS Marketplace, on Ubuntu, Debian, Red Hat Enterprise Linux, AlmaLinux and Rocky Linux.

Deequ with Apache Spark at a glance

Key facts about Deequ with Apache Spark
Built & maintained byWIEWAVE
CategoryAnalytics & Big Data
Operating systemsUbuntu, Debian, Red Hat Enterprise Linux, AlmaLinux, Rocky Linux
MarketplacesAzure Marketplace and AWS Marketplace
Offer typesPublic listing, with private offers on request

Availability by marketplace

Where Deequ with Apache Spark is published
MarketplaceStatus
Azure MarketplacePublished
AWS MarketplacePublished
Google Cloud MarketplaceAvailable on request

Need it on another marketplace, or as a private offer for your organisation? cloud@wiewave.com

Operating systems

Deequ with Apache Spark is built on 5 distributions

Each image follows its distribution's own provisioning model, package manager and security tooling — not one build relabelled several times.

  • Ubuntu

    LTS and interim releases, Minimal and Pro variants, built to Canonical's cloud-image conventions.

  • Debian

    Stable and oldstable, with backports where a workload needs a newer runtime than the release ships.

  • Red Hat Enterprise Linux

    RHEL 8 and 9 images, including BYOS and pay-as-you-go licensing models on each marketplace.

  • AlmaLinux

    The 1:1 RHEL-compatible rebuild, and the default landing spot for teams migrating off CentOS Linux.

  • Rocky Linux

    Enterprise-grade RHEL compatibility with a community governance model, on 8 and 9 streams.

How it's built

How WIEWAVE builds and maintains this image

The same four steps behind every offer we've published, including the hardening and CIS Benchmark checks every build goes through.

  1. 01

    Scoped

    The distribution, licensing model and target marketplaces are agreed before anything is built.

  2. 02

    Built & hardened as code

    Packer templates, Ansible provisioning and a pinned package set — then hardened, scanned and checked against the CIS Benchmark for its distribution.

  3. 03

    Certified & published

    Taken through each cloud's own certification pipeline before it goes live on the marketplace.

  4. 04

    Maintained

    Rebuilt on the upstream security cadence and re-published, with old versions retired without breaking deployments.

FAQ

Deequ with Apache Spark: common questions

If yours isn't here, ask our marketplace team directly.

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Need Deequ with Apache Spark customised, or on another cloud?

Tell us the distribution, the marketplace and the commercial model — we'll build, certify and publish it as a public listing or a private offer.