Apache griffin ui

You need to prepare the environment for Apache Griffin measure module, including the following software:. You can download demo data and execute. Then we will load data into both two tables for every hour.

Or you can just execute. Then you can get the calculation log in console, after the job finishes, you can get the result metrics printed. Depends on your business, you might need to refine your data quality measure further till your are satisfied. For more details about apache griffin measures, you can visit our documents in github. Toggle navigation.

For simplicity, suppose both two data set have the same schema as this: id bigint age int desc string dt string hour string. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF.Guidance and mentoring for those interested in participating in Apache projects and their communities.

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By clicking "Measures", and then choose "Create Measure". You can use the measure to process data and get the result you want.

Set partition configuration for source dataset and target dataset. The partition size means hive database minimum data unit,used to split data you want to calculate. Set up the measure required information. The organization means the group of your measure, you can manage your measurement dashboard by group later. After you create a new accuracy measure, you can check the measure you've created by selecting it in the listed measurements' page.

By clicking "Jobs", and then choose "Create Job". You can submit a job to execute your measure periodically. After submit the job, Apache Griffin will schedule the job in background, and after calculation, you can monitor the dashboard to view the result on UI.

By clicking on the diagram, you can get the zoom-in picture of it, and know the metrics at the selected time window. The metrics is shown on the right side of the page. By clicking on the measure, you can get the diagram and details about the measure result.

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apache griffin ui

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apache griffin ui

Raw Blame. Users will primarily access this application from a PC. Then, create a job to process the measure periodically. Finally, the heatmap and dashboard will show the data diagram of the measures.

Then you can see all the data assets listed here. There are mainly four kinds of measures for you to choose, which are: if you want to measure the match rate between source and target, choose accuracy. At current we only support accuracy measure creation from UI. Steps: Choose source Select the source dataset and fields which will be used for comparision.

For example, we choose 3 columns here. Choose target: Select the target dataset and fields which will be used for comparision. Mapping source and target Step1: "Map To": Select which rule to match the source and the target. Step2: "Source fields": choose the source column that you want to compare with the target column.By this tutorial, you will be able to build griffin dev environment to go through all griffin data quality process as below.

Quick Start

Click "Data Assets" at the top right corner, to watch all the exist data assets. Click "Measures" button at the top left corner to watch all the measures here, and you can also create a new DQ measurement by following steps.

apache griffin ui

Now you've created a new DQ measurement, the measurement needs to be scheduled to run in the docker container. Click "Jobs" button to watch all the jobs here, at current there is no job, you need to create a new one.

Click "Create Job" button at the top left corner, fill out all the blocks as below. The source and target partition means the partition pattern of the demo data, which is based on timestamp, "Start After s " means the job will start after n seconds, "Interval" is the interval of job, the unit is second.

In the example above, the job will run every 5 minutes. Wait for about 1 minute, after the calculation, results would be published to web UI, then you can watch the dashboard by clicking "DQ Metrics" at the top right corner.

Hi William: Is the version of Hive must be older then 1. When I use hive Lionel Liu could you look at this?

t7seliwa.space Quality control in Streaming t7seliwa.space Griffin

You mean you've tried the way of submitting the job directly using measure. It runs by spark-submit command directly, not through livy. The issue seems like in spark application it can not access your hive tables. We'll have something to check. First is the spark context has generated the hive context as sql context successfully, you can find that in log, if fails, you need to confirm the spark can access hive-site.

Second is spark should be able to access hive, you can check it in spark-shell, to test if it can access this specific table. Third, if you can access this hive table in spark-shell, you can try to submit griffin job by spark-submit command directly, it should perform the same as spark-shell way. Actually, if you submit spark job through livy, it runs in cluster mode, not in client mode, the hive-site.

If i want to submit jobs by using Griffin Web UI and in spark-cluster mode, how to config the hive-site. I can change it by change the config. Please ignore the first question,i have resolve it by put the hive-site.GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together.

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If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. The data quality DQ is a key criteria for many data consumers like IoT, machine learning etc. Apache Griffin is a model-driven data quality service platform where you can examine your data on-demand. It provides a standard process to define data quality measures, executions and reports, allowing those examinations across multiple data systems.

When you don't trust your data, or concern that poorly controlled data can negatively impact critical decision, you can utilize Apache Griffin to ensure data quality.

You can try running Griffin in docker following the docker guide. If you want to deploy Griffin in your local environment, please follow Apache Griffin Deployment Guide. For more information about Griffin, please visit our website at: griffin home page.

You can also subscribe the latest information by sending a email to subscribe dev-list and subscribe user-list. You can also subscribe the latest information by sending a email to subscribe dev-list and user-list:.

You can access our issues on JIRA page. See How to Contribute for details on how to contribute code, documentation, etc. We use optional third-party analytics cookies to understand how you use GitHub. You can always update your selection by clicking Cookie Preferences at the bottom of the page. For more information, see our Privacy Statement. We use essential cookies to perform essential website functions, e. We use analytics cookies to understand how you use our websites so we can make them better, e.Apache Griffin is an open source Data Quality solution for Big Data, which supports both batch and streaming mode.

It offers an unified process to measure your data quality from different perspectives, helping you build trusted data assets, therefore boost your confidence for your business.

Apache Griffin offers a set of well-defined data quality domain model, which covers most of data quality problems in general. It also define a set of data quality DSL to help users define their quality criteria. Source data will be ingested into Apache Griffin computing cluster and Apache Griffin will kick off data quality measurement based on data quality requirements. Apache Griffin provides front tier for user to easily onboard any new data quality requirement into Apache Griffin platform and write comprehensive logic to define their data quality.

Toggle navigation. Step 2 Measure Data Quality Source data will be ingested into Apache Griffin computing cluster and Apache Griffin will kick off data quality measurement based on data quality requirements. Step 3 Metrics Data quality reports as metrics will be evicted to designated destination. Additional Bonus Apache Griffin provides front tier for user to easily onboard any new data quality requirement into Apache Griffin platform and write comprehensive logic to define their data quality.GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together.

Work fast with our official CLI. Learn more. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. The data quality DQ is a key criteria for many data consumers like IoT, machine learning etc. Apache Griffin is a model-driven data quality service platform where you can examine your data on-demand.

It provides a standard process to define data quality measures, executions and reports, allowing those examinations across multiple data systems. When you don't trust your data, or concern that poorly controlled data can negatively impact critical decision, you can utilize Apache Griffin to ensure data quality.

You can try running Griffin in docker following the docker guide. If you want to deploy Griffin in your local environment, please follow Apache Griffin Deployment Guide. For more information about Griffin, please visit our website at: griffin home page.

You can also subscribe the latest information by sending a email to subscribe dev-list and subscribe user-list. You can also subscribe the latest information by sending a email to subscribe dev-list and user-list:. You can access our issues on JIRA page.

See How to Contribute for details on how to contribute code, documentation, etc. We use optional third-party analytics cookies to understand how you use GitHub. You can always update your selection by clicking Cookie Preferences at the bottom of the page. For more information, see our Privacy Statement.

We use essential cookies to perform essential website functions, e. We use analytics cookies to understand how you use our websites so we can make them better, e. Skip to content.

Mirror of Apache griffin Apache Dismiss Join GitHub today GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. This branch is 13 commits behind apache:master. Pull request Compare. Latest commit.


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