Open-source alternatives to Aws Data Wrangler

DataHub is a metadata management platform designed to unify technical, operational, and business context across diverse data ecosystems. By utilizing a graph-based metadata model and an event-driven ingestion architecture, it creates a centralized source of truth that maps complex data relationships, lineage, and ownership. This foundational framework enables organizations to maintain a synchronized view of their data landscape, supporting both human-led discovery and automated data operations. The platform distinguishes itself through its focus on grounding artificial intelligence and autono

dbt-core is a command-line framework for transforming data within a warehouse using modular SQL and version control. It functions as a data transformation engine that enables users to define data structures and business logic through declarative configuration files, which the system then compiles into executable code. By managing complex data dependencies through a directed acyclic graph, it ensures that transformation tasks execute in the correct order while maintaining a manifest-driven state to track lineage and execution history. The project distinguishes itself through an adapter-based d

Ibis is a portable Python dataframe library and multi-backend query engine that provides a unified interface for executing data transformations across diverse compute engines. It functions as a Python SQL expression compiler and dialect transpiler, allowing users to define data logic once and execute it across cloud warehouses, embedded databases, and distributed clusters without rewriting code. The project distinguishes itself through a database backend abstraction that decouples transformation logic from the underlying execution engine. It enables polyglot data workflows by mixing raw SQL s

Amundsen is a data catalog and discovery platform that provides a centralized directory for indexing tables and dashboards. It functions as a metadata management system and search engine, allowing users to locate and understand available data assets across diverse distributed sources. The platform includes capabilities for data lineage tracking to map the origin and movement of datasets between systems. It also serves as a data profiling tool, calculating distribution and quality statistics for individual table columns to provide automated insights into the nature of the data. The system man

Kedro is a data science pipeline framework and orchestration tool designed to build reproducible and modular data engineering workflows. It functions as an MLOps project template and Python data workflow tool that enforces software engineering best practices to move projects from prototype to production. The system distinguishes itself through a centralized data catalog manager that abstracts data access and versioning across various file formats and cloud storage systems. It further separates processing logic from data access via a lazy-loading data registry and provides a standardized proje

Polars is a high-performance columnar data processing library designed for efficient analytical workflows. It functions as a structured data library that organizes information into typed columns, utilizing the Apache Arrow memory format to enable zero-copy data sharing and cache-friendly, vectorized operations. The engine is built to handle large-scale tabular datasets, providing both local and distributed analytical runtimes that scale from single-machine environments to multi-node clusters. The project distinguishes itself through a sophisticated lazy query engine that constructs abstract e

LanceDB is a vector database and columnar data store designed to function as a versioned dataset manager and vector search engine. It serves as a high-performance backend for indexing and retrieving high-dimensional embeddings, providing the foundation for machine learning data pipelines. The system distinguishes itself through a combination of cloud-native object storage and immutable version tracking, allowing for data time-travel and reproducible AI experiments. It integrates hybrid search capabilities, merging dense vector similarity with BM25 full-text search and SQL-like scalar filters

Data engineering practice repository providing tutorials, distributed processing engines, and Python data pipeline automation scripts. The system encompasses automated data validation, distributed compute aggregation, embedded columnar querying, lazy evaluation planning, partitioned storage export, and cloud storage retrieval. The capability surface covers cloud integration and storage, data engineering and pipelines, data processing and analytics, data quality and testing, database and storage, file management, and monitoring and observability.

Gravitino is a federated metadata lake and unified data catalog designed to manage tables, files, and AI models across diverse data sources and cloud storage. It serves as a centralized interface for governing schemas, access controls, and tagging across relational databases, messaging queues, and object stores. The project distinguishes itself by unifying the management of AI assets, such as machine learning models and their version lineages, alongside traditional tabular data. It also implements the Iceberg REST specification to provide a standardized metadata server and proxy for lakehouse

Presto is a distributed SQL query engine designed for high-performance analytical processing across heterogeneous data sources. It functions as a data federation platform and massively parallel processing engine, allowing users to execute interactive queries against diverse storage systems without requiring data migration. By mapping remote metadata and structures to a unified relational namespace, it enables seamless cross-platform analysis through a standard SQL interface. The engine distinguishes itself through a pluggable connector architecture and a shared-nothing distributed processing

Rudder Server is a customer data platform and event routing pipeline designed to collect, transform, and route customer event data from various sources to data warehouses and business tools. It functions as a customer identity resolver, linking identifiers from multiple sources to build a unified identity graph and comprehensive behavioral customer profiles. The system differentiates itself through reverse ETL capabilities, which push processed customer segments and audiences from data warehouses back into operational third-party applications. It also provides a containerized data plane for K

Daft is a distributed dataframe library and multimodal data processor designed to handle large-scale structured and unstructured data. It functions as a vectorized execution engine that processes tables alongside images, audio, and video, utilizing a unified schema to manage diverse data types. The project distinguishes itself by combining distributed data engineering with large-scale AI inference. It provides an AI data pipeline for batch-optimizing model prompts and generating high-dimensional text embeddings, while utilizing zero-copy memory sharing to execute custom Python functions witho

RavenDB is a multi-model NoSQL document database designed for high-performance, ACID-compliant data storage. It persists structured information as schema-flexible JSON documents and utilizes a unit-of-work session pattern to track entity changes and batch modifications into atomic transactions. The platform is built on a distributed architecture that supports horizontal scaling through sharding and ensures high availability via multi-node, master-to-master cluster replication. The database distinguishes itself through a self-optimizing query engine that automatically creates and maintains ind

CloudQuery is a cloud infrastructure ETL tool and multi-cloud data pipeline designed to collect, synchronize, and normalize resource metadata from various cloud providers and SaaS platforms. It functions as a centralized asset inventory manager and security posture manager, extracting configuration and state data into relational databases, data lakes, or data warehouses. The system distinguishes itself by transforming complex, nested cloud API responses into flat relational tables, enabling the use of standard SQL for asset querying and analysis. It employs a modular plugin system for data ex

PyCaret is a Python AutoML platform and MLOps lifecycle manager designed to automate machine learning workflows. It functions as a low-code environment that leverages a scikit-learn native engine to execute preprocessing, training, and evaluation for tabular data. The platform distinguishes itself as an LLM-powered ML copilot, using large language model agents to analyze datasets, design experiment configurations, and explain model results. It also serves as a Kubernetes ML orchestrator and model registry, enabling the versioning of trained pipelines and their promotion to production API endp

Hopsworks - Data-Intensive AI platform with a Feature Store

Serverless proxy for Spark cluster

TextQL is a command line SQL query engine designed to execute relational queries directly against structured text files, such as CSV and TSV, without requiring a database import. It functions as a relational text file analyzer and a CSV processor that treats plain text files as virtual tables for filtering, joining, and aggregating data. The tool is built as a pipe-compatible data transformation utility, allowing it to process data from standard input and output formatted datasets. It enables relational joins across multiple files or directories within a single query to analyze relationships

xorm is a relational mapper and object-relational mapping tool for Go. It translates Go structures into SQL queries and maps database rows back into native objects, providing a multi-dialect database driver that supports MySQL, PostgreSQL, SQLite, Oracle, SQL Server, and TiDB. The project features a read-write splitting manager that routes modification requests to a primary database and read requests to replicas. It includes a database schema synchronizer to automatically align table structures and indexes with application data models, as well as a fluent SQL query builder for constructing co

StarRocks is a distributed SQL OLAP database engine designed for real-time analytics and high-performance multi-dimensional analysis. It functions as a data lakehouse query engine that enables SQL execution across large datasets and external open table formats without requiring local data imports. The system employs a shared-nothing distributed architecture and utilizes the MySQL protocol to integrate with business intelligence tools. It maintains real-time data consistency through a primary key upsert model and accelerates query response times using vectorized execution and cost-based optimi

SynapseML is an Apache Spark machine learning library designed for building and scaling machine learning workflows and data pipelines across distributed clusters. It serves as a distributed machine learning pipeline framework and a distributed inference engine for executing hardware-accelerated predictions and deep learning tasks on large-scale datasets. The project functions as a cloud AI integration layer, allowing users to apply pretrained artificial intelligence services for text, vision, and speech within distributed pipelines. It also includes a dedicated suite of tools for distributed

DataHub is a metadata management system and data catalog platform designed to provide a centralized directory for discovering, managing, and documenting datasets across a diverse data stack. It serves as a comprehensive framework for metadata management, incorporating a data governance framework to classify sensitive information and assign ownership for organizational accountability. The platform distinguishes itself through AI-enabled data discovery, which connects large language models to a metadata graph to allow for natural language search and exploration of data assets. It also provides

Ydata-profiling is an automated exploratory data analysis framework designed to generate comprehensive statistical reports and visual summaries from dataframes. It functions as a diagnostic tool for assessing data quality, identifying missing values, duplicates, and outliers, while providing a scalable engine for profiling massive datasets across distributed enterprise environments. The project distinguishes itself through its ability to handle large-scale data through distributed task orchestration and lazy stream processing, which minimizes memory overhead during complex computations. It in

Sails is an enterprise Node.js web framework designed for building scalable applications using a model-view-controller architecture. It functions as a realtime web application framework and a database agnostic object-relational mapper, providing a structured environment for developing server-side applications. The framework enables bi-directional data exchange between clients and servers via persistent connections to support real-time features. It utilizes a consistent interface to interact with various relational and non-relational data stores, allowing the application to remain independent

koa2-note is a project focused on Koa2 web server development and Node.js asynchronous programming. It provides a framework for building web servers and APIs using an asynchronous middleware pipeline to handle request and response cycles. The project emphasizes a layered backend architecture that decouples routing, business services, and data models. It distinguishes itself through the integration of relational databases for persisting user sessions and application data, alongside a build process that includes JSX-to-JavaScript compilation for frontend assets. The capability surface covers b

This project is a Node.js Express REST API reference implementation for a blogging platform. It serves as a structured blueprint for building server-side applications, demonstrating the integration of content management, user profiles, and social following. The implementation features a stateless identity management layer using cryptographically signed tokens to secure private resources and manage user sessions. It also includes social networking primitives for tracking user relationships to facilitate activity feeds and content discovery. The backend covers relational database management th

Dropwizard is a Java web application stack and REST framework used to build production-ready web services. It integrates an embedded HTTP server with operational tools to provide a complete environment for developing network interfaces that exchange JSON data. The project provides a set of core technology stacks for relational data mapping, schema migration, and operational performance monitoring. It includes a relational database migration tool to track schema updates and execute versioned scripts before an application starts. The framework covers a broad capability surface including reques