自定义

BI集成
                     
                         我们感动!为了改善客户体验，Collibra数据质量用户指南已转移到新万博移动客户端
                         
                         新万博移动客户端Collibra文档中心
                         
                         作为Collibra数据质量2新万博移动客户端022.11版本的一部分。为了确保平稳过渡，
                         
                         dq-docs.新万博移动客户端collibra.com
                         
                         仍然可以访问，但DQ用户指南现在只在文档中心进行维护。
                        
                     可以通过将您最喜欢的BI工具连接到底层报表集市来利用自定义报表。下面是一些可以作为构建自己的报告灵感的查询。详情请参阅
                     
                     ERD图
                     
                     获取更大的表列表。
                    
                     长期运行的作业
                    
                     从dataset_activity中选择dataset,run_id,total_time，其中total_time不为空
                    
                     提交的工作
                    
                     从owlcheck_q中选择*
                    
                     按用户分类的工作
                    
                     选择count(*) as owlchecks，用户名from owlcheck_q where updt_ts < now() group by username order by owlchecks desc
                    
                     按用户、数据集划分的作业
                    
                     在dev.public中选择count(*)， user_nm, dataset。Owl_check_history组按user_nm排列，数据集按计数顺序排列
                    
                     按行数最大
                    
                     Select dataset,rc as row_count from dataset_scan order by rc desc
                    
                     按月分类的工作岗位
                    
                     用grp as (select date_trunc('MONTH'， run_id) as by_month from dataset_scan where run_id < now()) select count(*) as owlchecks, by_month from grp group by by_month order by by_month desc
                    
                     用户划分规则
                    
                     选择count(*)作为规则，从owl_rule组中选择user_nm by user_nm order by rules desc
                    
                     按Spark(Cluster)使用率
                    
                     Select * from opt_spark order by num_executors desc
                    
                     来自代理的job id
                    
                     从agent_q中选择remote_job_id，其中remote_job_id不为空
                    
                     数据集的活动
                    
                     从dataset_activity中选择dataset,run_id,total_time，其中total_time不为空
                    
                     具有丰富指标的工作
                    
                     select A.dataset, A.run_id, total_time from dataset_activity where total_time is not null order by total_time desc limit 100)，扫描为(select * from dataset_scan where dataset in (select dataset from activity))，配置为(select * from opt_spark where dataset in (select dataset from activity))，模式为(select count(*) as col_cnt, dataset from dataset_schema where dataset in (select dataset from activity) group by dataset) select A.dataset, A.run_id, C.total_time, A.rc，D.col_cnt, B.driver_memory, B.num_executor,B. col_cnt, B.driver_memoryA. executor_cores, B.executor_memory, B.master FROM扫描A. INNER JOIN配置B ON A.dataset = B.dataset INNER JOIN活动C ON A.dataset = C.dataset and A.run_id = C.run_id INNER JOIN模式D ON A.dataset = D.dataset ORDER BY C.total_time
                    
                     就业机会。加载时间和资源狗万新闻c
                    
                     活动为(select dataset,run_id,total_time from public.)Dataset_activity where total_time不是null order by total_time)，扫描为(select * from public)。Dataset_scan where dataset in (select dataset from activity))，配置为(select * from public)。Opt_spark where dataset in (select dataset from activity))， schema as (select count(*) as col_cnt, dataset from public。select A.dataset, A.run_id, A.updt_ts, C.total_time, A.rc, D.col_cnt, B.driver_memory, B.num_executors,B. datet_schema where dataset in (select dataset from activity) group by dataset)A. executor_cores, B.executor_memory, B.master FROM扫描A. INNER JOIN配置B ON A.dataset = B.dataset INNER JOIN活动C ON A.dataset = C.dataset and A.run_id = C.run_id INNER JOIN模式D ON A.dataset = D.dataset ORDER BY A.updt_ts desc limit
                    
                     数据集扫描和评分模式
                    
                     从public中选择*。Dataset_scan where dataset like 'public.%';
                    
                     数据集扫描和按名称评分
                    
                     从public中选择*。Dataset_scan where dataset ='public.atm_customer';
                    
                     按模式按月扫描-“公共”
                    
                     select dataset, DATE_TRUNC('MONTH'， run_id)作为run_id, count(*)作为Total_Scans from dataset_scan where dataset like 'public%' group by dataset, run_id order by run_id asc
                    
                     规则超过30天
                    
                     select * from rule_output where run_id < NOW() - INTERVAL '30天';
                    
                     计划作业队列
                    
                     从public.owlcheck_q中选择job_id,agent_id,dataset,run_id,status,activity,start_time;
                    
                     数据集架构中的列计数
                    
                     Select dataset, count(*) from dataset_schema group by dataset;
                    
                     分析统计数据
                    
                     select dataset, run_id, field_nm， (null_ratio * 100) as null_percent， (empty_ratio * 100) as empty_percent, ROUND(CAST((100 - (null_ratio * 100) + (empty_ratio * 100)) as numeric)， 3) as完整性from public。dataset_field where updt_ts > '2020-06-01'和dataset = 'ProcessOrder'和run_id > '2021-03-17 00:00:00+00'顺序根据完整性描述
                    
                     元数据/模式/数据类型
                    
                     Select * from public.dataset_schema;
                    
                     配置文件数据
                    
                     Select * from public.dataset_field;
                    
                     查找相似的列
                    
                     select不同的数据集，field_nm, max_abs from dataset_field where max_abs = '无线电信'
                    
                     相同的列名
                    
                     从dataset_field中选择不同的数据集，field_nm = 'authenticated_user'
                    
                     类似的列名
                    
                     从dataset_field中选择不同的数据集field_nm，如“%id%”
                    
                     行为研究
                    
                     从数据集='esg_data'的行为中选择*
                    
                     Postgres Stats中Schema的所有列
                    
                     SELECT table_name FROM information_schematable WHERE table_schema = 'public' ORDER BY table_name
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