Search Results for "etl."
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Showing 111 open source projects for "etl."
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Ethereum ETL
Python scripts for ETL (extract, transform and load) jobs for Ethereum
Python scripts for ETL (extract, transform and load) jobs for Ethereum blocks, transactions, ERC20 / ERC721 tokens, transfers, receipts, logs, contracts, internal transactions. Data is available in Google BigQuery. Ethereum ETL lets you convert blockchain data into convenient formats like CSVs and relational databases. -
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Embedded Template Library (ETL)
Embedded Template Library
C++ is a great language to use for embedded applications and templates are a powerful aspect. The standard library can offer a great deal of well-tested functionality, but there are some parts of the standard library that do not fit well with deterministic behavior and limited resource requirements. These limitations usually preclude the use of dynamically allocated memory and containers with open-ended sizes. What is needed is a template library where the user can declare the size, or... -
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Addax
Addax is a versatile open-source ETL tool
Addax is a data integration and ETL (Extract, Transform, Load) tool designed for high-performance data migration tasks. It simplifies the process of moving data between different systems and formats. -
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Steampipe
Zero-ETL, infinite possibilities. Live query APIs, code & more
...Your cloud is a live database that changes fast. Don't wait on ETL to sync, or rely on old data. Crunch it where it's born, fueling new use cases and swift decisions. -
Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
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Unstract
No-code LLM Platform to launch APIs and ETL Pipelines
...It integrates a visual Prompt Studio environment where users can iteratively design extraction schemas, compare outputs from different models, and monitor costs and accuracy side by side, making it easier to refine prompts and extraction logic before deploying at scale. Unstract supports deploying structured extraction as REST API endpoints or embedding it into data engineering ETL pipelines, which allows it to plug directly into data warehouses, cloud storage, or downstream analytics systems. Its platform works with a broad variety of file types — from PDFs and spreadsheets to images — and includes integrations with databases, cloud storage providers, and vector databases. -
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AWS Data Wrangler
Pandas on AWS, easy integration with Athena, Glue, Redshift, etc.
...Easy integration with Athena, Glue, Redshift, Timestream, OpenSearch, Neptune, QuickSight, Chime, CloudWatchLogs, DynamoDB, EMR, SecretManager, PostgreSQL, MySQL, SQLServer and S3 (Parquet, CSV, JSON, and EXCEL). Built on top of other open-source projects like Pandas, Apache Arrow and Boto3, it offers abstracted functions to execute usual ETL tasks like load/unload data from Data Lakes, Data Warehouses, and Databases. Convert the column name to be compatible with Amazon Athena and the AWS Glue Catalog. Run a query against AWS CloudWatchLogs Insights and convert the results to Pandas DataFrame. Get QuickSight dashboard ID given a name and fails if there is more than 1 ID associated with this name. ... -
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omniparser
Native Golang ETL streaming parser and transform library
Omniparser is a native Golang ETL parser that ingests input data of various formats (CSV, txt, fixed length/width, XML, EDI/X12/EDIFACT, JSON, and custom formats) in streaming fashion and transforms data into desired JSON output based on a schema written in JSON. -
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Logstash
Centralize, transform and stash your data
Logstash is a server-side data processing pipeline that dynamically ingests data from numerous sources, transforms it, and ships it to your favorite “stash” regardless of format or complexity. It supports and ingests data of all shapes, sizes and sources, dynamically transforms and prepares this data, and transports it to the output of your choice. Logstash is extensible, with over 200 plugins available to let you create and configure your pipeline how you choose. -
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SyncLite
Build Anything Sync Anywhere
...SyncLite enables real-time, transactional data replication and consolidation from various sources including edge/desktop applications using popular embedded databases (SQLite, DuckDB, Apache Derby, H2, HyperSQL), data streaming applications, IoT message brokers, traditional database systems(ETL) and more into a diverse array of databases, data warehouses, and data lakes, enabling AI and ML use-cases at all three levels: Edge, Fog and Cloud. SyncLite's novel CDC replication framework for embedded databases, is designed to assist developers in rapidly building general-purpose data-intensive applications, Gen AI Search/RAG applications for edge, desktop, and mobile environments. ... -
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10
Rubix ML
A high-level machine learning and deep learning library for PHP
Rubix ML is a free open-source machine learning (ML) library that allows you to build programs that learn from your data using the PHP language. We provide tools for the entire machine learning life cycle from ETL to training, cross-validation, and production with over 40 supervised and unsupervised learning algorithms. In addition, we provide tutorials and other educational content to help you get started using ML in your projects. Our intuitive interface is quick to grasp while hiding alot of power and complexity. Write less code and iterate faster leaving the hard stuff to us. ... -
11
InfluxDB
The open source time series database
...InfluxDB provides infrastructure and application monitoring, IoT monitoring and analytics and more. It has APIs for storing and querying data, processing it in the background for ETL or monitoring and alerting purposes. This data can also be visualized, explored and more to help businesses seize opportunities and make the best decisions. InfluxDB is easy to start and easy to scale. Learn more about it on https://www.influxdata.com/ -
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Open Semantic Search
Open source semantic search and text analytics for large document sets
...It provides an integrated search server combined with a document processing pipeline that supports crawling, text extraction, and automated analysis of content from many different sources. Open Semantic Search includes an ETL framework that can ingest documents, process them through analysis steps, and enrich the data with extracted information such as named entities and metadata. It also supports optical character recognition to extract text from images and scanned documents, including images embedded inside PDF files. It integrates text mining and analytics capabilities that allow users to examine relationships, topics, and structured data within document collections. -
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NVIDIA Merlin
Library providing end-to-end GPU-accelerated recommender systems
...Each stage of the Merlin pipeline is optimized to support hundreds of terabytes of data, which is all accessible through easy-to-use APIs. For more information, see NVIDIA Merlin on the NVIDIA developer website. Transform data (ETL) for preprocessing and engineering features. Accelerate your existing training pipelines in TensorFlow, PyTorch, or FastAI by leveraging optimized, custom-built data loaders. Scale large deep learning recommender models by distributing large embedding tables that exceed available GPU and CPU memory. Deploy data transformations and trained models to production with only a few lines of code. -
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Prefect
Prefect is a workflow orchestration framework
Prefect is an open-source modern workflow orchestration tool for scheduling, monitoring, and managing data workflows and tasks. It enables Python-native pipeline definitions with robust retries, caching, observability, and a powerful UI—ideal for data engineering and ETL processes. -
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TiDB
Open Source NewSQL Database
TiDB is an open source distributed SQL database that supports Hybrid Transactional and Analytical Processing (HTAP) workloads. It is currently the most actively developed open source NewSQL database, and has a rich set of features including horizontal scalability, strong consistency, and high availability. -
16
Daft
Distributed DataFrame for Python designed for the cloud
Daft is a framework for ETL, analytics and ML/AI at scale. Its familiar Python Dataframe API is built to outperform Spark in performance and ease of use. Daft plugs directly into your ML/AI stack through efficient zero-copy integrations with essential Python libraries such as Pytorch and Ray. It also allows requesting GPUs as a resource for running models. Daft runs locally with a lightweight multithreaded backend. -
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Pathway
Python ETL framework for stream processing, real-time analytics, LLM
Pathway is an open-source framework designed for building real-time data applications using reactive and declarative paradigms. It enables seamless integration of live data streams and structured data into analytical pipelines with minimal latency. Pathway is especially well-suited for scenarios like financial analytics, IoT, fraud detection, and logistics, where high-velocity and continuously changing data is the norm. Unlike traditional batch processing frameworks, Pathway continuously... -
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CocoIndex
ETL framework to index data for AI, such as RAG
CocoIndex is an open-source framework designed for building powerful, local-first semantic search systems. It lets users index and retrieve content based on meaning rather than keywords, making it ideal for modern AI-based search applications. CocoIndex leverages vector embeddings and integrates with various models and frameworks, including OpenAI and Hugging Face, to provide high-quality semantic understanding. It’s built for transparency, ease of use, and local control over your search... -
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Erigon
Ethereum implementation on the efficiency frontier
Erigon is an implementation of Ethereum (execution client), on the efficiency frontier, written in Go. For an Archive node of Ethereum Mainnet we recommend >=3TB storage space: 1.8TB state (as of March 2022), 200GB temp files (can symlink or mount folder <datadir>/etl-tmp to another disk). Ethereum Mainnet Full node ( see --prune* flags): 400Gb. Erigon by default is "all in one binary" solution, but it's possible start TxPool as separated processes. Same true about: JSON RPC layer (RPCDaemon), p2p layer (Sentry), history download layer (Downloader), consensus. Don't start services as separated processes unless you have clear reason for it: resource limiting, scale, replace by your own implementation, security. ... -
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Hamilton DAGWorks
Helps scientists define testable, modular, self-documenting dataflow
...As shown below, it results in readable code that can always be visualized. Hamilton loads that definition and automatically builds the DAG for you. Hamilton brings modularity and structure to any Python application moving data: ETL pipelines, ML workflows, LLM applications, RAG systems, BI dashboards, and the Hamilton UI allows you to automatically visualize, catalog, and monitor execution. -
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ImportExcel
PowerShell module to import/export Excel spreadsheets, without Excel
...Advanced features include adding and formatting tables, setting number/date formats, creating charts, and applying styling or conditional formatting programmatically. The module is optimized for performance (streaming where possible) and supports large datasets, making it useful for ETL tasks, automated reporting, and data analysis in pure PowerShell environments. It integrates well with scheduled jobs and CI pipelines where generating or consuming spreadsheets is part of an automated workflow. -
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AlaSQL
JavaScript SQL database for browser and Node.js for relational tables
AlaSQL.js - JavaScript SQL database for browser and Node.js. Handles both traditional relational tables and nested JSON data (NoSQL). Export, store, and import data from localStorage, IndexedDB, or Excel. We focus on speed by taking advantage of the dynamic nature of JavaScript when building up queries. Real-world solutions demand flexibility regarding where data comes from and where it is to be stored. We focus on flexibility by making sure you can import/export and query directly on data... -
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Instill Core
Instill Core is a full-stack AI infrastructure tool for data
...It provides an end-to-end solution that enables developers to build, deploy, and manage AI-powered applications without needing to manually stitch together multiple tools across the data and model lifecycle. The platform focuses heavily on handling unstructured data such as documents, images, audio, and video, transforming them into AI-ready formats through integrated ETL pipelines and processing workflows. Instill Core includes modular components such as pipelines, artifacts, and model services, which work together to enable flexible and scalable AI system design. It also supports retrieval-augmented generation workflows and model deployment without requiring complex GPU infrastructure management. -
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Pyper
Concurrent Python made simple
Pyper is a Python-native orchestration and scheduling framework designed for modern data workflows, machine learning pipelines, and any task that benefits from a lightweight DAG-based execution engine. Unlike heavier platforms like Airflow, Pyper aims to remain lean, modular, and developer-friendly, embracing Pythonic conventions and minimizing boilerplate. It focuses on local development ergonomics and seamless transition to production environments, making it ideal for small teams and... -
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Superduper
Superduper: Integrate AI models and machine learning workflows
...Developers may leverage Superduper by building compositional and declarative objects that out-source the details of deployment, orchestration versioning, and more to the Superduper engine. This allows developers to completely avoid implementing MLOps, ETL pipelines, model deployment, data migration, and synchronization. Using Superduper is simply "CAPE": Connect to your data, apply arbitrary AI to that data, package and reuse the application on arbitrary data, and execute AI-database queries and predictions on the resulting AI outputs and data.