Platform Management Tools

Platform management tools are essential for maintaining, optimizing, and governing data platforms. They encompass a wide range of functionalities including data quality assurance, data governance, workflow management, automation, and even environmental impact management (Green IT). These tools help organizations ensure the reliability, compliance, and efficiency of their data operations.

They can be broadly categorized into six main areas:

• Data Quality: Data quality tools help organizations maintain the accuracy, completeness, and consistency of their data. They often include features for data profiling, cleansing, and monitoring.
• Governance: Data governance tools assist in managing the availability, usability, integrity, and security of data. They help enforce policies, standards, and regulations across the data lifecycle.
• Workflow Manager: Workflow managers orchestrate and automate complex data pipelines. They handle task scheduling, dependency management, and error handling in data processes.
• Automation: Automation tools help streamline and automate business processes and workflows across different applications and services.
• Green IT: Green IT tools focus on reducing the environmental impact of IT operations. In the context of data platforms, this often involves optimizing resource usage and energy consumption.
• Compliance & Security: Tools focused on data protection, anonymization, and regulatory compliance requirements.

Available Tools

Here is a summary table of the main platform management tools we have identified, sorted by subcategory and alphabetical order.

Data Quality & Testing

Governance

Automation

Green IT

Workflow manager

Compliance & Security

*Criteria: >40 contributors, >500 stars, and recent releases/commit

Tool Descriptions by Subcategory

Data Quality

1. Deequ: A library built on top of Apache Spark for defining “unit tests for data”, which helps ensuring data quality and integrity at scale. It enables defining data quality constraints and computing data quality metrics on large datasets.
2. Elementary: An open-source data observability tool that monitors data quality and pipeline operations. It integrates with dbt and provides automated data quality testing, monitoring, and alerting capabilities.
3. Evidently: An open-source framework for evaluating, testing, and monitoring machine learning models and data quality, with support for data drift detection and reporting.
4. Great Expectations: A Python-based open-source library for validating, documenting, and profiling data. It helps teams maintain data quality and improve communication about data between teams.
5. Pandera: A lightweight Python library for statistically validating and testing dataframes, allowing users to define schemas and enforce data quality constraints in data pipelines.
6. Soda Core: An open-source framework for data quality testing and monitoring. It allows users to create data quality checks using a simple YAML syntax and supports multiple data sources.
7. SQLFluff: A dialect-flexible and configurable SQL linter and auto-formatter that helps teams enforce consistent SQL style and catch quality issues across data projects.

Governance

1. Amundsen: A data discovery and metadata engine for improving the productivity of data analysts, data scientists, and engineers. It provides a searchable catalog of data resources across your organization.
2. Apache Atlas: A scalable and extensible set of core foundational governance services. It enables enterprises to effectively and efficiently meet their compliance requirements and improve their data governance.
3. Apache Egeria: An open metadata and governance framework that enables the free flow of metadata between different tools and platforms through open standards and interoperability.
4. Apache Ranger: A framework to enable, monitor, and manage comprehensive data security across the Hadoop and broader data platform ecosystem, providing centralized authorization and audit policies.
5. CKAN: An open-source data management system for building open data portals and catalogs, widely used by governments and organizations to publish and share datasets (AGPL-3.0 license).
6. Datahub: A modern data catalog built to enable end-to-end data discovery, data observability, and data governance. It provides a unified view of all data assets across an organization.
7. Magda: A modern data catalog system that helps organizations manage, discover, and share their data assets. It focuses on federated data discovery and provides rich metadata management capabilities.
8. Marquez: An open-source metadata service for the collection, aggregation, and visualization of a data ecosystem’s metadata. It provides lineage tracking and version control for data assets.
9. Open Metadata: A unified platform for metadata management, data discovery, and data governance. It offers comprehensive features for data quality, lineage, and collaboration.
10. OpenLineage: An open standard and framework for collecting, modeling, and analyzing data lineage metadata across pipelines, enabling consistent lineage tracking regardless of the underlying tools.
11. Spline: A data lineage tracking tool that automatically captures and visualizes data lineage from Apache Spark applications. It provides detailed insights into data transformations and dependencies.

Automation

1. Activepieces: An open-source, no-code business automation tool offering a visual builder and a large library of pieces to connect applications and automate workflows.
2. Huginn: A self-hosted system for building agents that monitor and act on your behalf online, automating tasks such as watching for events and triggering actions across the web.
3. n8n: An extendable workflow automation tool with a focus on ease of use, flexibility, and the ability to run self-hosted. It offers a no-code interface for creating complex workflows, supports over 200 integrations, and allows users to create custom nodes and functionalities.
4. Node-RED: A flow-based, low-code programming tool for wiring together hardware devices, APIs, and online services through a browser-based visual editor.
5. Zapier Platform: An open-source platform for building integrations and automations between web applications. It provides a CLI and SDK for developers to create custom “Zaps” (automated workflows) and integrate new apps into the Zapier ecosystem.

Green IT

1. Cloud Carbon Footprint: An application that estimates the energy use and carbon emissions of cloud computing workloads across different cloud providers.
2. Code Carbon: A Python package that estimates the carbon emissions produced by computing resources during code execution.
3. Green Analysis Tools: A set of tools for analyzing the environmental impact of software applications and websites.
4. Kepler: A Kubernetes-based tool that uses eBPF and machine learning to measure and attribute energy consumption and carbon emissions at the pod and container level.
5. Scaphandre: A power consumption monitoring agent that measures the energy usage of hosts and processes, designed to help track and reduce the environmental footprint of IT infrastructure.
6. SCI: A specification that describes how to calculate a carbon intensity score for software applications, helping organizations measure and reduce their software’s environmental impact.

Workflow Manager

1. Airflow: A platform to programmatically author, schedule, and monitor workflows. It allows you to define complex pipelines as directed acyclic graphs (DAGs).
2. Apache Dolphinscheduler: A distributed and extensible workflow scheduler platform with a focus on cloud native architecture. It provides powerful scheduling capabilities and visual workflow management.
3. Argo Workflows: A container-native workflow engine for orchestrating parallel jobs on Kubernetes. It’s designed for scenarios requiring complex job orchestration.
4. Astronomer Cosmos: An open-source provider that runs dbt projects as Airflow DAGs and task groups, bridging dbt transformations with Airflow orchestration.
5. Dagster: A data orchestrator for machine learning, analytics, and ETL. It provides a unified view of data pipelines and the assets they produce.
6. Flyte: A scalable, Kubernetes-native workflow orchestration platform for building reproducible, production-grade data and machine learning pipelines.
7. Hamilton: A lightweight Python framework for describing data and machine learning pipelines as functions, automatically building a dataflow graph for execution and lineage.
8. Kedro: An open-source Python framework that applies software engineering best practices to create modular, reproducible, and maintainable data science and machine learning pipelines.
9. Kestra: A modern orchestration and scheduling platform with powerful features for building and monitoring data pipelines.
10. Luigi: A Python package that helps you build complex pipelines of batch jobs. It handles dependency resolution and workflow management.
11. Mage.ai: A modern data pipeline tool for transforming and integrating data. It offers a user-friendly interface for building data pipelines.
12. Metaflow: A human-centric framework, originally developed at Netflix, for building and managing real-life data science and machine learning projects from prototype to production.
13. Prefect: A workflow management system designed for modern data stacks. It provides a flexible and intuitive API for defining and executing workflows.
14. Temporal: A durable execution platform that orchestrates long-running, reliable workflows by managing state, retries, and failures across distributed services.
15. Windmill: An open-source developer platform and workflow engine that turns scripts into auto-generated UIs, APIs, and orchestrated flows (AGPL-3.0 license).
16. ZenML: An extensible, open-source MLOps framework for creating portable, production-ready machine learning pipelines across different tools and infrastructure.

Compliance & Security

1. ARX: A comprehensive open-source software for anonymizing sensitive personal data. It implements a wide variety of privacy-preserving techniques and risk metrics for statistical disclosure control.
2. Amnesia: A data anonymization tool that focuses on the protection of personal data through various anonymization techniques. It provides both a graphical user interface and API access for data anonymization tasks.
3. Cerbos: An open-source, decoupled authorization layer that lets teams define and enforce context-aware access control policies as code, independently of the application.
4. Faker: A widely used Python library for generating realistic fake data such as names, addresses, and other personal details, useful for testing and anonymizing datasets.
5. Microsoft Presidio: An open-source SDK for detecting, analyzing, and anonymizing personally identifiable information (PII) in text and images to support privacy and compliance.
6. OPA (Open Policy Agent): A general-purpose, open-source policy engine that unifies policy enforcement across the stack using the declarative Rego language.
7. OpenFGA: An open-source, high-performance authorization engine inspired by Google Zanzibar that implements relationship-based access control (ReBAC) at scale.
8. Permify: An open-source authorization service for building fine-grained, scalable access control based on Google Zanzibar, suited for application-level permissions (AGPL-3.0 license).
9. SDV (Synthetic Data Vault): A Python library for generating synthetic tabular, relational, and time-series data that preserves the statistical properties of real datasets for privacy-safe use (Business Source License, non-OSI).

Making the Right Choice

When choosing platform management tools, consider these key factors:

• The scale of your data operations
• Compliance requirements
• The complexity of your data workflows
• Your organization’s environmental goals
• Integration capabilities with your existing stack
• Community support and maintenance activity
• Deployment requirements (self-hosted vs. cloud)

For ensuring data quality, tools like Great Expectations or Deequ provide robust solutions. For data governance, platforms like Datahub or Open Metadata offer comprehensive capabilities. Complex workflow management might require tools like Airflow or Dagster. For business process automation, both n8n and Zapier Platform provide flexible solutions. Organizations focusing on environmental impact can leverage tools like Code Carbon or Cloud Carbon Footprint.

Remember that effective platform management often requires a combination of tools to address different aspects of data operations. The key is choosing tools that integrate well with your existing data stack and align with your organization’s strategy and goals.

Related Topics

The open-source community has developed numerous solutions for various aspects of data handling, including:

• Ingestion and Transport
• Storage
• Query and Processing
• Analysis and Output
• Platform Management