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Showing 22 open source projects for "aws"
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AWS Data Wrangler
Pandas on AWS, easy integration with Athena, Glue, Redshift, etc.
An AWS Professional Service open-source python initiative that extends the power of Pandas library to AWS connecting DataFrames and AWS data-related services. 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). -
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AWS SDK for pandas
Easy integration with Athena, Glue, Redshift, Timestream, Neptune
aws-sdk-pandas (formerly AWS Data Wrangler) bridges pandas with the AWS analytics stack so DataFrames flow seamlessly to and from cloud services. With a few lines of code, you can read from and write to Amazon S3 in Parquet/CSV/JSON/ORC, register tables in the AWS Glue Data Catalog, and query with Amazon Athena directly into pandas. The library abstracts efficient patterns like partitioning, compression, and vectorized I/O so you get performant data lake operations without hand-rolling boilerplate. ... -
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G2
Interactive data-driven visualization grammar for statistical charts
G2 is a highly interactive data-driven visualization grammar for statistical charts. with a high level of usability and scalability. It provides a set of grammar, and takes users beyond a limited set of charts to an almost unlimited world of graphical forms. With G2, you can describe the visual appearance and interactive behavior of visualization just by one statement, and generate web-based views using Canvas or SVG. We have summarized a series of story design templates from lots of real... -
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CloudQuery
The open-source cloud asset inventory powered by SQL
CloudQuery extracts, transforms and loads your cloud assets into normalized PostgreSQL tables. CloudQuery enables you to assess, audit, and monitor the configurations of your cloud assets. Use standard SQL to find any asset based on any configuration or relation to other assets. Connect CloudQuery standard PostgreSQL database to your favorite BI/Visualization tool such as Grafana, QuickSight, etc. Codify your security & compliance rules with SQL as the query engine. Integrate CloudQuery with... -
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Lithops
A multi-cloud framework for big data analytics
Lithops is an open-source serverless computing framework that enables transparent execution of Python functions across multiple cloud providers and on-prem infrastructure. It abstracts cloud providers like IBM Cloud, AWS, Azure, and Google Cloud into a unified interface and turns your Python functions into scalable, event-driven workloads. Lithops is ideal for data processing, ML inference, and embarrassingly parallel workloads, giving you the power of FaaS (Function-as-a-Service) without vendor lock-in. It also supports hybrid cloud setups, object storage access, and simple integration with Jupyter notebooks. -
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Braket.jl
Experimental Julia implementation of the Amazon Braket SDK
Braket.jl is not an officially supported AWS product. This package is a Julia implementation of the Amazon Braket SDK allowing customers to access Quantum Hardware and Simulators. This is experimental software, and support may be discontinued in the future. For a fully supported SDK, please use the Python SDK. We may change, remove, or deprecate parts of the API when making new releases. -
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ClearML
Streamline your ML workflow
ClearML is an open source platform that automates and simplifies developing and managing machine learning solutions for thousands of data science teams all over the world. It is designed as an end-to-end MLOps suite allowing you to focus on developing your ML code & automation, while ClearML ensures your work is reproducible and scalable. The ClearML Python Package for integrating ClearML into your existing scripts by adding just two lines of code, and optionally extending your experiments... -
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PULSAR
Distributed pub-sub messaging system
Apache Pulsar is a cloud-native, distributed messaging and streaming platform originally created at Yahoo! and now a top-level Apache Software Foundation project. Easy to deploy, lightweight compute process, developer-friendly APIs, no need to run your own stream processing engine. Run in production at Yahoo! scale for over 5 years, with millions of messages per second across millions of topics. Expand capacity seamlessly to hundreds of nodes. Low publish latency (< 5ms) at scale with strong... -
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Cookiecutter Data Science
Project structure for doing and sharing data science work
A logical, reasonably standardized, but flexible project structure for doing and sharing data science work. When we think about data analysis, we often think just about the resulting reports, insights, or visualizations. While these end products are generally the main event, it's easy to focus on making the products look nice and ignore the quality of the code that generates them. Because these end products are created programmatically, code quality is still important! And we're not talking... -
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.NET for Apache Spark
A free, open-source, and cross-platform big data analytics framework
...This means you can use .NET for Apache Spark anywhere you write .NET code allowing you to reuse all the knowledge, skills, code, and libraries you already have as a .NET developer. .NET for Apache Spark runs on Windows, Linux, and macOS using .NET Core, or Windows using .NET Framework. It also runs on all major cloud providers including Azure HDInsight Spark, Amazon EMR Spark, AWS & Azure Databricks. -
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DataGym.ai
Open source annotation and labeling tool for image and video assets
...DATAGYM is an end-to-end workbench to create, annotate, manage, and export the right training data for your computer vision models. Your image data can be imported into DATAGYM from your local machine, from any public image URL or directly from an AWS cloud S3 bucket. Machine learning teams spend up to 80% of their time on data preparation. DATAGYM provides AI-powered annotation functions to help you accelerate your labeling task. The Pre-Labeling feature enables turbo-labeling – it processes thousands of images in the background within a very short time. -
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Amazon Kinesis Flink Connectors
Contains various Apache Flink connectors to connect to AWS data
This library contains various Apache Flink connectors to connect to AWS data sources and sinks. This repository contains various Apache Flink connectors to connect to AWS Kinesis data sources and sinks. Flink maintain backwards compatibility for the Sink interface used by the Firehose Producer. This project is compatible with Flink 1.x, there is no guarantee it will support Flink 2.x should it release in the future. -
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Weave Scope
Monitoring, visualization and management for Docker and Kubernetes
...Weave Scope automatically detects processes, containers, hosts. No kernel modules, no agents, no special libraries, no coding. Seamless integration with Docker, Kubernetes, DCOS and AWS ECS. See your Docker hosts, containers and services in real-time. Easily identify and correct issues to ensure the stability and performance of your containerized applications. View metrics, tags and metadata within the context of a process, container, service or host. Effortlessly navigate from processes inside your container to Docker hosts. ... -
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DataStation Community Edition
App to easily query, script, and visualize data from every database
...Over 20 supported databases out of the box including: PostgreSQL, MySQL, MariaDB, SQLite, SQL Server, Oracle, ClickHouse, Influx 1 (InfluxQL), Influx 2 (Flux), Prometheus, Elasticsearch, AWS Athena, Google BigQuery, Snowflake, Cassandra, Scylla, TimescaleDB, YugabyteDB, CockroachDB, CrateDB, QuestDB, and Airtable. -
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AWS Step Functions Data Science SDK
For building machine learning (ML) workflows and pipelines on AWS
The AWS Step Functions Data Science SDK is an open-source library that allows data scientists to easily create workflows that process and publish machine learning models using Amazon SageMaker and AWS Step Functions. You can create machine learning workflows in Python that orchestrate AWS infrastructure at scale, without having to provision and integrate the AWS services separately. -
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Snowplow Analytics
Enterprise-strength marketing and product analytics platform
Snowplow is ideal for data teams who want to manage the collection and warehousing of data across all their platforms and products. -
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StreamAlert
StreamAlert is a serverless, realtime data analysis framework
...Ingested logs and generated alerts can be retroactively searched for compliance and research. Serverless design is cheaper, easier to maintain, and scales to terabytes per day. Deployment is automated, simple, safe and repeatable for any AWS account. Secure by design, least-privilege execution, containerized analysis, and encrypted data storage. -
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Amazon SageMaker Examples
Jupyter notebooks that demonstrate how to build models using SageMaker
...They have the familiar Jupyter and JuypterLab interfaces that work well for single users, or small teams where users are also administrators. Advanced users also use SageMaker solely with the AWS CLI and Python scripts using boto3 and/or the SageMaker Python SDK. -
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CloverDX
Design, automate, operate and publish data pipelines at scale
...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 -
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Seldon Server
Machine learning platform and recommendation engine on Kubernetes
...It provides an open-source data science stack that runs within a Kubernetes Cluster. You can use Seldon to deploy machine learning and deep learning models into production on-premise or in the cloud (e.g. GCP, AWS, Azure). -
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ExAws
A flexible, easy to use set of clients AWS APIs for Elixir
ExAws is a comprehensive Elixir client library for interfacing with AWS services. It provides low-level request builders for nearly all AWS APIs—like S3, EC2, Lambda, DynamoDB, SQS, SES, Route 53, and more—while supporting streaming, request configuration overrides, telemetry, flexible HTTP clients, and codecs. Its modular architecture enables importing only the services you need with separate packages (e.g., ex_aws_s3, ex_aws_ec2). -
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AWS data tools
AWS tools for data integration and more
Here I list some data tools I created for Amazon AWS S3 and Redshift
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