Updated on 2023-03-03 GMT+08:00

Step 1: Process Design

This guide uses the collection of operations statistics from a taxi vendor in 2017 as an example.

Requirement Analysis

Requirement analysis helps you develop a data governance framework to support the process design for data governance.

In this example scenario, the following data problems exist:
  • No standardized model is available.
  • There is no standard for data field naming.
  • Data content is not standard, and data quality is uncontrollable.
  • Statistics standards are inconsistent, hindering business decision-making.
With data governance of DataArts Studio, we expect to achieve the following objectives:
  • Standardized data and models
  • Unified statistics standards and high-quality data reports
  • Data quality monitoring and alarm
  • Daily revenue statistics
  • Monthly revenue statistics
  • Statistics on the revenue proportion of each payment type

Service Survey

Before using DataArts Studio, conduct a service survey to understand the component functions required in the service process and analyze the subsequent service load.

Table 1 Service survey table


Configuration Item

Information to Be Collected

Survey Result




Organizations and relationships between the enterprise's big data departments


Properly plan workspaces to reduce the complexity of workspace dependency

Access control permissions on data and resources between departments


User permissions and resource permissions control are involved.


DataArts Migration

Data source from which the data is to be migrated and the data source version

CSV source data files in the OBS bucket


Full data volume of each data source

2,114 bytes


Daily incremental data volume of each data source



Types and versions of data sources at the destination

MRS Hive 3.1


Data migration period: day, hour, minute, or real-time



Network bandwidth between data sources at the source and destination

100 MB


Description of the network connectivity between the data sources and integration tools



Database migration: number of survey tables and maximum table size

N/A. In this example, data needs to be migrated from OBS to the database.

Understand the scale of database migration and whether the migration duration of the largest table is acceptable.

File migration: number of files, and whether the size of any file reaches 1 TB

A CSV file smaller than 1 TB



DataArts Factory

Whether job orchestration and scheduling are required



Services required in orchestration and scheduling, such as MRS, GaussDB(DWS), and CDM

DataArts Migration and DataArts Quality of DataArts Studio, and MRS Hive

Understand application scenarios of jobs to further investigate the suitability of platform capabilities for customer scenarios.

Number of jobs

Less than 20

Understand the job scale. Generally, the job scale is described by the number of operators and can be estimated based on the number of tables.

Number of times a job is scheduled


Determine the DataArts Studio edition based on the scheduling quota of each DataArts Studio sales edition.

Number of data developers




DataArts Architecture

Data sources and number of tables

Only one CSV file

Analyze source data to understand the data source and overall situation.

Services, requirements, and benefits

Standardize data and models and collect statistics on revenue in a flexible manner.

Analyze the destination to understand the purposes of data governance and digitalization.

Data survey, data overview, data standards degree, and industry standards overview


Analyze the process to understand the standards and quality compliance in the data governance process.


DataArts Quality

Requirements and benefits

Data quality monitoring

Monitor more data sources and rules.

Number of jobs


You can manually create dozens of jobs or enable the function of automatically generating data quality jobs on DataArts Architecture. If the API for creating data quality jobs is called, more than 100 quality jobs can be created.

Application scenarios

Standardize and cleanse data at the DWI layer.

Generally, before and after data processing, the data quality is monitored from six dimensions. If any data that does not comply with rules is detected, users will receive an alarm notification.


DataArts Catalog

Data sources to support

MRS Hive


Data volume

A table contains fewer than 100 records.

A maximum of 1 million tables can be managed.

Scheduling frequency of metadata collection


Collection tasks can be executed by hour, day, or week.

Key metrics of metadata collection


The key metrics include the table name, field name, owner, description, and creation time.

Application scenarios of tags


Tags are highly related keywords that help you classify and describe assets to facilitate search.


DataArts Security

Data sources to which access is controlled


Access to the following components is controlled: HDFS, Hive, HBase, Yarn, Kafka, Storm, and Elasticsearch.

Data security levels to be identified


A maximum of 10 data security levels can be defined.

Data sources to be masked

In this example, data in the MRS standard trip table needs to be masked to DWS.

Only DWS and MRS data sources can be masked.

Data sources that require watermarking


Watermarks can be embedded only for DWS and MRS data sources.


DataArts DataService

Open data sources

Revenue summary table

Generally, these data sources store the tables at the final layer after a data warehouse is established. Such tables contain high-quality service data but fewer records, which can be directly displayed.

Daily data calls


If the database response takes a long period of time due to complex extraction logic, the data calling volume will decrease.

Number of peak data calls per second


The number of peak data calls per second varies depending on the edition in use and data extraction logic.

Average latency of a single data call


The database response duration is related to the data extraction logic.

Whether data access records are required



Data access method: intranet or Internet



Number of DataArts DataService developers



Process Design

The figure below shows the process design for data governance based on the requirement analysis and service survey. All subsequent data governance operations will be performed according to this service process.

Figure 1 Process design