Open Source Linux Data Pipeline Tools - Page 2
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Data Pipeline Tools for Linux
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Orchest
Build data pipelines, the easy way
Code, run and monitor your data pipelines all from your browser! From idea to scheduled pipeline in hours, not days. Interactively build your data science pipelines in our visual pipeline editor. Versioned as a JSON file. Run scripts or Jupyter notebooks as steps in a pipeline. Python, R, Julia, JavaScript, and Bash are supported. Parameterize your pipelines and run them periodically on a cron schedule. Easily install language or system packages. Built on top of regular Docker container images. Creation of multiple instances with up to 8 vCPU & 32 GiB memory. A free Orchest instance with 2 vCPU & 8 GiB memory. Simple data pipelines with Orchest. Each step runs a file in a container. It's that simple! Spin up services whose lifetime spans across the entire pipeline run. Easily define your dependencies to run on any machine. Run any subset of the pipeline directly or periodically. -
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PipeRider
Code review for data in dbt
PipeRider automatically compares your data to highlight the difference in impacted downstream dbt models so you can merge your Pull Requests with confidence. PipeRider can profile your dbt models and obtain information such as basic data composition, quantiles, histograms, text length, top categories, and more. PipeRider can integrate with dbt metrics and present the time-series data of metrics in the report. PipeRider generates a static HTML report each time it runs, which can be viewed locally or shared. You can compare two previously generated reports or use a single command to compare the differences between the current branch and the main branch. The latter is designed specifically for code review scenarios. In our pull requests on GitHub, we not only want to know which files have been changed, but also the impact of these changes on the data. PipeRider can easily generate comparison reports with a single command to provide this information. -
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Tributary
Streaming reactive and dataflow graphs in Python
Tributary is a library for constructing dataflow graphs in Python. Unlike many other DAG libraries in Python (airflow, luigi, prefect, dagster, dask, kedro, etc), tributary is not designed with data/etl pipelines or scheduling in mind. Instead, tributary is more similar to libraries like mdf, loman, pyungo, streamz, or pyfunctional, in that it is designed to be used as the implementation for a data model. One such example is the greeks library, which leverages tributary to build data models for options pricing. -
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Union Pandera
Light-weight, flexible, expressive statistical data testing library
The open-source framework for precision data testing for data scientists and ML engineers. Pandera provides a simple, flexible, and extensible data-testing framework for validating not only your data but also the functions that produce them. A simple, zero-configuration data testing framework for data scientists and ML engineers seeking correctness. Access a comprehensive suite of built-in tests, or easily create your own validation rules for your specific use cases. Validate the functions that produce your data by automatically generating test cases for them. Integrate seamlessly with the Python ecosystem. Overcome the initial hurdle of defining a schema by inferring one from clean data, then refine it over time. Identify the critical points in your data pipeline, and validate data going in and out of them. Build confidence in the quality of your data by defining schemas for complex data objects. -
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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 run Unzip the zip file Windows : java -cp .\lib\*;osdq-spark-0.0.1.jar org.arrah.framework.spark.run.TransformRunner -c .\example\samplerun.json Mac UNIX java -cp ./lib/*:./osdq-spark-0.0.1.jar org.arrah.framework.spark.run.TransformRunner -c ./example/samplerun.json For those on windows, you need to have hadoop distribtion unzipped on local drive and HADOOP_HOME set. Also copy winutils.exe from here into HADOOP_HOME\bin -
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go-streams
A lightweight stream processing library for Go
A lightweight stream processing library for Go. go-streams provides a simple and concise DSL to build data pipelines. In computing, a pipeline, also known as a data pipeline, is a set of data processing elements connected in series, where the output of one element is the input of the next one. The elements of a pipeline are often executed in parallel or in time-sliced fashion. Some amount of buffer storage is often inserted between elements. -
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gusty
Making DAG construction easier
gusty allows you to control your Airflow DAGs, Task Groups, and Tasks with greater ease. gusty manages collections of tasks, represented as any number of YAML, Python, SQL, Jupyter Notebook, or R Markdown files. A directory of task files is instantly rendered into a DAG by passing a file path to gusty's create_dag function. gusty also manages dependencies (within one DAG) and external dependencies (dependencies on tasks in other DAGs) for each task file you define. All you have to do is provide a list of dependencies or external_dependencies inside of a task file, and gusty will automatically set each task's dependencies and create external task sensors for any external dependencies listed. gusty works with both Airflow 1.x and Airflow 2.x, and has even more features, all of which aim to make the creation, management, and iteration of DAGs more fluid, so that you can intuitively design your DAG and build your tasks. -
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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. With lakeFS, your data lake is version controlled and you can easily time-travel between consistent snapshots of the lake. Easier ETL testing - test your ETLs on top of production data, in isolation, without copying anything. Safely experiment and test on full production data. Easily Collaborate on production data with your team. Automate data quality checks within data pipelines. -
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memphis
Next-Generation Event Processing Platform
Memphis enables building modern queue-based applications that require large volumes of streamed and enriched data, modern protocols, zero ops, up to x9 faster development, up to x46 fewer costs, and significantly lower dev time for data-oriented developers and data engineers. Queues and brokers are a mission-critical component in the modern application architecture and should be highly available and stable as possible. Provide great performance while maintaining efficient resource consumption. Increase observability, integrations with 3rd-party monitoring tools, real-time notifications, stream lineage, and therefore troubleshooting time reduction. Enable rapid development and ultra-short time-to-production. -
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10
nonechucks
Deal with bad samples in your dataset dynamically
nonechucks is a library that provides wrappers for PyTorch's datasets, samplers and transforms to allow for dropping unwanted or invalid samples dynamically. What if you have a dataset of 1000s of images, out of which a few dozen images are unreadable because the image files are corrupted? Or what if your dataset is a folder full of scanned PDFs that you have to OCRize, and then run a language detector on the resulting text, because you want only the ones that are in English? Or maybe you have an AlternateIndexSampler, and you want to be able to move to dataset[6] after dataset[4] fails while attempting to load! PyTorch's data processing module expects you to rid your dataset of any unwanted or invalid samples before you feed them into its pipeline, and provides no easy way to define a "fallback policy" in case such samples are encountered during dataset iteration. -
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rudderstack
Privacy and Security focused Segment-alternative, in Golang
Quickly deploy flexible, powerful customer data pipelines, then send the data to your entire stack—without the engineering headache. Our complete toolset makes it easy to level-up your customer data stack. Spare your data engineers the headache. Our 180+ integrations, along with custom webhook sources and destinations, save data teams hundred of hours. Say goodbye to different versions of the truth. Our SDKs track anonymous and known users at the source and reconcile users in your warehouse and SaaS tools. Go beyond event streaming and control all of your customer data on your own terms. Learn how we can help you build a customer data platform. RudderStack treats your data warehouse as a first-class citizen among destinations, with advanced features and configurable, near real-time sync. RudderStack is built API-first. It integrates seamlessly with the tools that the developers already use and love. -
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whylogs
The open standard for data logging
whylogs is an open-source library for logging any kind of data. With whylogs, users are able to generate summaries of their datasets (called whylogs profiles) which they can use to track changes in their dataset Create data constraints to know whether their data looks the way it should. Quickly visualize key summary statistics about their datasets. whylogs profiles are the core of the whylogs library. They capture key statistical properties of data, such as the distribution (far beyond simple mean, median, and standard deviation measures), the number of missing values, and a wide range of configurable custom metrics. By capturing these summary statistics, we are able to accurately represent the data and enable all of the use cases described in the introduction.
