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Getting Started Quick Start Quick Start Chinese Version Streaming Use Cases Profiling Use Cases FAQ Community Conference Development Contribution Contributors Download Latest version Archived Need Help?
Streaming Use Cases User Story Say we have two streaming data sets in different kafka topics source, target , we need to know what is the data quality for target data set, based on source data set.
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Griffin calculates data source directly, to get appropriate data format, as a preparation for DQ calculation.
In this phase, you can use df-opr and spark-sql rules. Griffin calculates with prepared data, to get the DQ metrics. In this phase, you can use griffin-dsl, spark-sql rules, and a part of df-opr rules.
According to my understanding of the following url , you will find the unique key of the data. After this optimization , what will the sql become?
We don't do any other optimization for accuracy now, just translate it as On clause in spark sql, that works good, as spark sql has some optimization when join with an On clause.
We have to find the unique keys of data in the old version, because we implemented the accuracy algorithm ourselves before, then we need some optimization for it.
That document explains the define of accuracy, based on our old algorithm, but we can compute it with much less information from users now.
There might be some unexpected duplication in source, but in accuracy, the duplication can not be found.
Btw, I think uniqueness is not necessary before accuracy, only if you want to ignore the duplicate ones. But there comes another question, how to get total count of source.
I wish to integrate Apache Griffin with AWS S3 in place of HDFS HDOOP. How can be do that? Base on this, I think you can integrate it with AWS.
If you need streaming mode, you can implement a temporary storage based on AWS S3, or you can skip this for batch mode only.
If you want to save metrics in AWS S3, you may need such a metric persist solution. Thanks Lionel. I am relatively new to Griffin. Would like to explore option 1.
But I am confused a bit know. I see the Spark Job for Graffin DQ matrix is configured with HDFS. How can we change it to S3.?
Which fields in configuration do you concern with? Would you please list them to our mail list? There might be a better place for the discussion.
Everything is running on K8 cluster like a charm when we extended the Apache Griffin image. The change was what you suggested. We simply pointed Hive to S3.
But we have hit a roadblock when we tried to recreate the same idea on the k8 cluster. We cannot figure out how to make use of this jar without HDFS.
This image is pointing at our spark and hive docker images. When livy submit a job to spark cluster, it would be submitted in cluster mode, which means any spark node might be the application driver.
Therefore, each spark node should be able to access the files required by the job, including the jar package and hive-site. HDFS is a natural solution for the global access from each spark node, and HDFS is usually supported in spark cluster, that's why we leverage it by default.
Change sparkProperties. I think a complete file path would be better, I'm not sure what's the relative path it should be, relative for the jar package or for the spark worker, maybe for the latter.
We uploaded griffin-measure. The Spark Job submit works. In the Griffin UI it shows successful. But in the Livy logs it shows error code and also do not shows any metrics.
GET STARTED. ABOUT APACHE GRIFFIN Apache Griffin is an open source Data Quality solution for Big Data, which supports both batch and streaming mode.
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.
WHO USES Apache Griffin.