Difference Between FineDataLink and Kettle

Task Development

Low learning cost: Process-based task development enables a quick start with a low learning threshold. Anyone with SQL capabilities can perform complex data processing.

High development cost: The function design style of Kettle is close to traditional code development tools, making the costs of understanding functions and developing tasks high.

As a Browser/Server end tool, FineDataLink supports the separation of users and permissions, and allows multiple users to log in to the environment at the same time to develop data tasks for their respective departments.

Unable to develop tasks collaboratively: Kettle is a Client/Server end product without a user system. It does not allow multiple users to develop tasks at the same time.

The Data Pipeline function of FineDataLink supports millisecond-level data real-time duplication, fast backup of enterprise business system databases, and master-slave separation of business systems.

When the business database undertakes continuous writing into and batch extraction from business systems at the same time, the performance of the database will decline, leading to issues such as jammed operations in the front end.

The Data Pipeline function of FineDataLink supports real-time incremental data updates and can be used to build quasi-real-time data warehouses.

Traditional data warehouses use the T+1 strategy to update data, making them unsuitable for high-timeliness scenarios like large monitoring screens, and unable to meet the monitoring and analysis requirements of enterprises.

The Data Pipeline function of FineDataLink supports batch synchronization of multiple tables in the business system, improving work efficiency by 30% in the same data extraction scenario.

Due to the large number of business systems and database tables within the enterprise, the original layer construction of a data warehouse requires creating hundreds of data extraction tasks.

FineDataLink enables you to build an enterprise data center and publish the result data to the business system or other data consumers in the form of APIs in a safe and stable way.

The data connection via APIs and data service functions of FineDataLink helps you build a connected and shared data foundation, and open up a complete data link from data supply to sharing.

Kettle lacks a secure data sharing mechanism. As the number of data consumers increases, the IT department will have to repeatedly develop the same data processing logic.

By leveraging the FDL Data Service function, enterprises can securely and reliably conduct cross-domain data transmission even with an external network environment. This not only saves the cost of leased lines but also enables enterprises to independently monitor and manage anomalies.

If an enterprise needs to transmit data among multiple branches across different areas, dedicated data lines will be used to ensure data security and stable transmission. However, dedicated lines cost hundreds of thousands or millions of yuan per year, which is a heavy burden for most companies.

The Data Development function of FineDataLink greatly reduces the development and labor costs of backing up data from Jodoo to local databases, and supports the return of processed data for subsequent processes or applications to meet compliance requirements of cloud data management.

• According to national data security management regulations, data of some enterprises (especially state-owned enterprises/government agencies) must be archived locally.

• As the usage time of cloud data storage reaches the upper limit, historical data is difficult to retain.

• Data backup and maintenance costs are very high either through developing code-based interfaces or exporting the Excel files manually.

Task O&M

Convenience of development and scheduling: FineDataLink is easy to use, and encapsulates nodes based on business functions. The configurations of schedules are visualized, reducing the nuisances of code development. FineDataLink realizes higher development efficiency with lower operation costs.

High task management cost and unstable task operation:

Kettle does not allow managing schedules, and cannot complete the settings of scheduled operations. You need to use Windows/Linux timers for scheduled execution. In the Linux system, command lines need to be used for operation, which is cumbersome. Meanwhile, due to the instability of system timers, Kettle tasks are prone to execution failure.

Operation and maintenance monitoring: FineDataLink supports real-time display of operation logs, accurate troubleshooting of errors, real-time monitoring of operation status, and convenient operations to relieve the huge workload of O&M personnel.

Unable to pay global attention to task operation status: Kettle does not support an overview of global tasks. After an error is reported, you cannot quickly find the task that reported an error in the last run. This may cause data developers to realize the errors only after users mention them, which may affect the efficiency of data decision-making analysis.

Multi-terminal warning: The schedule configuration and message reminder functions enable quick data reminder through channels like WeCom, DingTalk, Feishu, SMS, and email.

Inconvenient error reporting during task running: If a Kettle task reports an execution error, you can only be reminded through traditional methods such as SMS and email, which are not convenient for most customers.

FineDataLink provides the load distribution function. You can independently control resources of scheduled tasks, pipeline tasks, and data services by simply dragging in the visual page.

The memory resources and concurrency control schemes of Kettle are adjusted through configuration files and parameter settings. No productized memory resource regulation mechanism is provided.