DataWorks Data Quality helps you ensure data quality by detecting changes in source data, tracking dirty data generated during data extract, transformation, and load (ETL), and automatically blocking the nodes that involve dirty data to stop dirty data from spreading downstream. This way, you can prevent nodes from producing unexpected dirty data that affects the smooth running of nodes and business decision-making. You can also minimize the waste of time, money, and resources, and ensure that your business always stays on the right track.
- Fees included in your DataWorks bills
You are charged by DataWorks based on the number of Data Quality checks. For more information, see Data Quality Billing Description.
- Fees not included in your DataWorks bills
You are also charged by the compute engines bound to your DataWorks workspace. When monitoring rules are triggered, SQL statements are generated and executed on specific compute engines. In this case, you are charged for the computing resources provided by the compute engines. For more information, see the topic about billing for each type of compute engine. For example, your DataWorks workspace is bound to a MaxCompute project that is billed in pay-as-you-go mode. In this case, you are charged by MaxCompute, not by DataWorks, for executing SQL statements.
Data Quality checks offline data stored in data assets such as MaxCompute tables, E-MapReduce (EMR) Hive tables, and Hologres tables. Data Quality also checks streaming data stored in data channels such as Message Queue for Apache Kafka topics and DataHub topics. Data Quality allows you to configure monitoring rules that focus on the integrity, accuracy, validity, consistency, uniqueness, and timeliness of data. You can configure a monitoring rule for a specific table and associate the monitoring rule with a node that generates the table data. After the node is run, the monitoring rule is triggered to check the data generated by the node and reports data anomalies at the earliest opportunity. You can also configure a monitoring rule as a strong rule or a weak rule to determine whether to fail the associated node when Data Quality detects anomalies. This way, you can prevent dirty data from spreading downstream and minimize the waste of time and money on data restoration.
|Overview||The Overview page provides an overview of alerts and blocks triggered by Data Quality
checks for tables and topics in data sources of the current workspace. This page includes
the following sections:
|My Subscriptions||The My Subscriptions page displays the monitoring rules that you subscribe to and are configured to send alert notifications by email only or by both email and text message. Data Quality supports the following notification methods: Email, Email and SMS, DingTalk Chatbot, Enterprise WeChat Chatbot, and Lark Group Chatbot.|
|Rule Configuration||You can configure monitoring rules by table on the Configure by Table page or by template on the Configure by Template page. For more information, see Configure monitoring rules by table and Configure monitoring rules based on a monitoring rule template.|
|Node Query||On the Node Query page, you can filter nodes by the table name, topic name, node ID, or node name. Then, you can view the historical check records and details of a specific node.|
|Mange noise reduction rules||On the Noise Reduction Management page, you can specify a date on which the detected abnormal data is denoised in the current workspace. Specifically, when abnormal data on the specified day is detected, Data Quality does not send alert notifications or block related nodes.|
|Report Template Management||On the Report Template Management page, you can configure report templates by selecting metrics that collect statistics on rule configuration and rule execution, specifying a statistical period, selecting a report frequency, and setting notification methods. Then, Data Quality generates and sends reports based on the template configurations.|
|Rule Templates||On the Rule Templates page, you can manage a set of custom rule templates and use the rule templates to improve the efficiency of rule configuration.|
- Before you configure monitoring rules for EMR, Hologres, AnalyticDB for PostgreSQL, and Cloudera's Distribution Including Apache Hadoop (CDH), you must collect metadata from the data sources. For more information about how to collect metadata, see the topics in Metadata collection.
- After monitoring rules are configured for EMR tables, Hologres tables, AnalyticDB for PostgreSQL tables, and CDH tables, the rules can be triggered only if the nodes that generate the table data are scheduled by using exclusive resource groups for scheduling.
- You can configure multiple monitoring rules for a table.
- Check offline data
To allow Data Quality to check offline data, you must configure a monitoring rule for a table by performing the following operations: Configure a partition filter expression for the table, associate the partition filter expression with a node that generates the table data, and then create and configure a monitoring rule for the table. After the node is run, the monitoring rule is triggered to check the data identified by the partition filter expression. To determine whether to fail the node when Data Quality detects anomalies, you can configure the monitoring rule as a strong rule or a weak rule. This way, you can prevent dirty data from spreading downstream. On the rule configuration page of a table, you can also set notification methods to receive alert notifications at the earliest opportunity.
- Check streaming data
Data Quality detects stream discontinuity and data latency for Message Queue for Apache Kafka topics and DataHub topics and reports alerts upon anomalies. You can use the custom Flink SQL, dimension table JOIN statement, multi-table JOIN statement, and window functions. To minimize repeated alerts, you can set the alert frequency and the alert severity, such as warning or error alerts.
Configure a monitoring rule
- Create a monitoring rule: You can create a monitoring rule for a specific table. You can also create monitoring rules for multiple tables at a time by using a system template. For more information, see Configure monitoring rules by table and Configure monitoring rules based on a monitoring rule template.
- Subscribe to a monitoring rule: After a monitoring rule is created, you can subscribe to the monitoring rule to receive alert notifications of Data Quality checks by using the notification methods that you configure for the monitoring rule, such as Email, Email and SMS, DingTalk Chatbot, DingTalk Chatbot @ALL, Lark Group Chatbot, and Enterprise WeChat Chatbot.
Trigger the monitoring rule
You must associate a node with the monitoring rule to allow Data Quality to check the data quality of the node. When the node code is executed in Operation Center, the monitoring rule is triggered and transformed into an SQL statement to check the data generated by the code. If anomalies are detected, Data Quality determines whether to fail the node and block the descendant nodes based on the check result and rule settings. This prevents dirty data from spreading downstream.
View the check result
- View the check result in Operation Center
- View the value of the Instance Status parameter. A possible reason why an instance fails a quality check is that the code is successfully executed, but the data generated by the code does not meet expectations. If the instance fails the quality check of a strong monitoring rule, the current instance fails and the descendant instances are blocked.
- Click DQC Log in the lower part of the Runtime Log tab to view the data quality check result. For more information, see View auto triggered node instances.
- View the check result on the Node Query page in Data Quality
On the Node Query page, you can filter nodes by the table name, topic name, node ID, or node name. Then, you can view the historical check records and details of a specific node. For more information, see View monitoring results.