Search Results for "using"
Sort By:
Showing 17 open source projects for "using"
View related business solutions-
Atera all-in-one platform IT management software with AI agentsAtera’s AI agents don’t just assist, they act. From detection to resolution, they handle incidents and requests instantly, taking your IT management from automated to autonomous.
-
Vibes don’t ship, Retool doesVibe coding tools create cool demos, but Retool helps you build software your company can actually use. Generate internal apps that connect directly to your data—deployed in your cloud with enterprise security from day one. Build dashboards, admin panels, and workflows with granular permissions already in place. Stop prototyping and ship on a platform that actually passes security review.
-
1
lakeFS
lakeFS - Git-like capabilities for your object storage
Increase data quality and reduce the painful cost of errors. Data engineering best practices using git-like operations on data. lakeFS is an open-source data version control for data lakes. It enables zero-copy Dev / Test isolated environments, continuous quality validation, atomic rollback on bad data, reproducibility, and more. Data is dynamic, it changes over time. Dealing with that without a data version control system is error-prone and labor-intensive. -
2
DataQualityDashboard
A tool to help improve data quality standards in data science
...The quality checks were organized according to the Kahn Framework1 which uses a system of categories and contexts that represent strategies for assessing data quality. Using this framework, the Data Quality Dashboard takes a systematic-based approach to running data quality checks. Instead of writing thousands of individual checks, we use “data quality check types”. These “check types” are more general, parameterized data quality checks into which OMOP tables, fields, and concepts can be substituted to represent a singular data quality idea. -
3
Dagster
An orchestration platform for the development, production
Dagster is an orchestration platform for the development, production, and observation of data assets. Dagster as a productivity platform: With Dagster, you can focus on running tasks, or you can identify the key assets you need to create using a declarative approach. Embrace CI/CD best practices from the get-go: build reusable components, spot data quality issues, and flag bugs early. Dagster as a robust orchestration engine: Put your pipelines into production with a robust multi-tenant, multi-tool engine that scales technically and organizationally. Dagster as a unified control plane: The ‘single plane of glass’ data teams love to use. ... -
4
Arize Phoenix
Uncover insights, surface problems, monitor, and fine tune your LLM
Phoenix provides ML insights at lightning speed with zero-config observability for model drift, performance, and data quality. Phoenix is an Open Source ML Observability library designed for the Notebook. The toolset is designed to ingest model inference data for LLMs, CV, NLP and tabular datasets. It allows Data Scientists to quickly visualize their model data, monitor performance, track down issues & insights, and easily export to improve. Deep Learning Models (CV, LLM, and Generative)... -
Outgrown Windows Task Scheduler?Windows Task Scheduler wasn't built for complex, cross-platform automation. Get a free diagnostic that shows exactly where things are failing and provides remediation recommendations. Interactive HTML report delivered in minutes.
-
5
data-diff
Efficiently diff rows across two different databases
...The hard truth is that the quality of the replication is the quality of the data. Since copying entire datasets in batch is often infeasible at the modern data scale, businesses rely on the Change Data Capture (CDC) approach of replicating data using a continuous stream of updates. -
6
SDGym
Benchmarking synthetic data generation methods
...Or write your own custom machine learning model. In addition to performance and memory usage, you can also measure synthetic data quality and privacy through a variety of metrics. Install SDGym using pip or conda. We recommend using a virtual environment to avoid conflicts with other software on your device. -
7
re_data
re_data - fix data issues before your users & CEO would discover them
...Currently, re_data focuses on observing the dbt project (together with underlying data warehouse - Postgres, BigQuery, Snowflake, Redshift). Gather all relevant outputs about your data in one place using our cloud. Invite your team and debug it easily from there. Go back in time, and see your past metadata. Set up Slack notifications to always know when a new report is produced or an existing one got updated. -
8
dbt-re-data
re_data - fix data issues before your users & CEO would discover them
...Currently, re_data focuses on observing the dbt project (together with underlaying data warehouse - Postgres, BigQuery, Snowflake, Redshift). Data transformations in re_data are implemented and exposed as models & macros in this dbt package. Gather all relevant outputs about your data in one place using our cloud. Invite your team and debug it easily from there. Go back in time, and see your past metadata. Set up Slack notifications to always know when a new report is produced or an existing one got updated. -
9
Feathr
A scalable, unified data and AI engineering platform for enterprise
Feathr is a data and AI engineering platform that is widely used in production at LinkedIn for many years and was open sourced in 2022. It is currently a project under LF AI & Data Foundation. Define data and feature transformations based on raw data sources (batch and streaming) using Pythonic APIs. Register transformations by names and get transformed data(features) for various use cases including AI modeling, compliance, go-to-market and more. Share transformations and data(features) across team and company. Feathr is particularly useful in AI modeling where it automatically computes your feature transformations and joins them to your training data, using point-in-time-correct semantics to avoid data leakage, and supports materializing and deploying your features for use online in production. -
Skillfully - The future of skills based hiringSkillfully transforms hiring through AI-powered skill simulations that show you how candidates actually perform before you hire them. Our platform helps companies cut through AI-generated resumes and rehearsed interviews by validating real capabilities in action. Through dynamic job specific simulations and skill-based assessments, companies like Bloomberg and McKinsey have cut screening time by 50% while dramatically improving hire quality.
-
10
SQLBucket
Lightweight library to write, orchestrate and test your SQL ETL
SQLBucket is a lightweight framework to help write, orchestrate and validate SQL data pipelines. It gives the possibility to set variables and introduces some control flow using the fantastic Jinja2 library. It also implements a very simplistic unit and integration test framework where you can validate the results of your ETL in the form of SQL checks. With SQLBucket, you can apply TDD principles when writing data pipelines. To start working, you need to instantiate your SQLBucket core object with the project_folder parameter. ... -
11
EZStacking
EZStacking is Jupyter notebook generator for machine learning
EZStacking is Jupyter notebook generator for supervised learning problems using Scikit-Learn pipelines and stacked generalization. EZStacking handles classification and regression problems for structured data. It can also be viewed as a development tool, because a notebook generated with EZStacking contains: -an exploratory data analysis (EDA) used to assess data quality - a modelling producing a reduced-size stacked estimator - a server returning a prediction, a measure of the quality of input data and the execution time. -
12
WhyLogs Java Library
Profile and monitor your ML data pipeline end-to-end
...WhyLogs calculates approximate statistics for datasets of any size up to TB-scale, making it easy for users to identify changes in the statistical properties of a model's inputs or outputs. Using approximate statistics allows the package to run on minimal infrastructure and monitor an entire dataset, rather than miss outliers and other anomalies by only using a sample of the data to calculate statistics. -
13
CloverDX
Design, automate, operate and publish data pipelines at scale
...CloverDX offers 100+ specialized components which can be further extended by creation of "macros" - subgraphs - and libraries, shareable with 3rd parties. Simple data manipulation jobs can be created visually. More complex business logic can be implemented using Clover's domain-specific-language CTL, in Java or languages like Python or JavaScript. Through its DataServices functionality, it allows to quickly turn data pipelines into REST API endpoints. The platform allows to easily scale your data job across multiple cores or nodes/machines. Supports Docker/Kubernetes deployments and offers AWS/Azure images in their respective marketplace -
14
apache spark data pipeline osDQ
osDQ dedicated to create apache spark based data pipeline using JSON
This is an offshoot project of open source data quality (osDQ) project https://sourceforge.net/projects/dataquality/ This sub project will create apache spark based data pipeline where JSON based metadata (file) will be used to run data processing , data pipeline , data quality and data preparation and data modeling features for big data. This uses java API of apache spark. It can run in local mode also. Get json example at https://github.com/arrahtech/osdq-spark How to... -
15
DataCleaner
Data quality analysis, profiling, cleansing, duplicate detection +more
DataCleaner is a data quality analysis application and a solution platform for DQ solutions. It's core is a strong data profiling engine, which is extensible and thereby adds data cleansing, transformations, enrichment, deduplication, matching and merging. Website: http://datacleaner.github.io -
16
Restful APIs for Data Cleansing
This is sister project for osDQ which provide Restful APIs
...It provides Restful APIs for features for data quality and data preparation features. This project will help projects which want embed data quality and data preparation features in their project or UI using restful calls. Data Cleansing APIs Dockerfile: # Pull base image FROM frnde/jetty-9.4.2-jre8-alpine-cet ADD osdq-v0.0.1.war /var/lib/jetty/webapps/osdq.war EXPOSE 8080 Docker Image https://hub.docker.com/r/vreddym/osdq-web/tags -
17
Toolsverse ETL Framework
Open source Extract Transform Load engine written in Java
...Key Features: * embeddable, open source and free * fast and scalable * uses target database features to do transformations and loads * manual and automatic data mapping * data streaming * bulk data loads * data quality features using SQL, JavaScript? and regex * data transformations Requirements * Java 1.6 and up * At least 4 MB of RAM New in 3.2 (01/18/2013) * Improved auto-update functionality * Bug fixes
- Previous
- You're on page 1
- Next
