There is a strong demand for enterprise level data integration services. How to find a breakthrough for optimization?

Data Lake 、 Data warehouse 、 The lake and the warehouse are integrated 、 Data center …… People who pay attention to big data , In the near future, these keywords must often brush the screen , This undoubtedly reflects the present Enterprise data integration services There's a lot of demand .
Small T The observed , With the continuous expansion of their business, many enterprises , The amount and type of data required by the daily operation of each department also increases , At the same time, it will continue to produce data , In the face of massive data that is constantly updated , If the enterprise data development team does not do a good job in unified coordination and management , Then everyone under big data will feel like a blind man touching an elephant , Each sees only a part of big data, not a whole , As a result, the coordination efficiency between departments is getting lower and lower .

therefore , In the planning of the overall digital transformation of the enterprise , Data integration The necessity is self-evident . Data integration just as the name suggests , It refers to the process of re centralizing the data in various business data systems that are originally scattered in the enterprise , It serves as the basis for various data projects , It is to connect the user's original data and data governance 、 Key steps of data analysis , It is also a link that is prone to problems in the implementation of traditional data mining analysis projects .
From the perspective of improving the overall data analysis project implementation and development efficiency and development quality , How can we find a breakthrough to optimize the data integration process ？

Data synchronization efficiency , Key factors affecting the quality of data integration

Data integration requirements for most enterprises , Data integration can be basically divided into two categories: offline data synchronization and real-time data synchronization .
One of the most common is offline data synchronization , The application scenario can be simply summarized as ： One data source corresponds to one data destination . The data destination can be a data warehouse , Synchronize the data of the relational database into the data warehouse , It forms a data integration .

and Real time data synchronization The data changes of the source database tables can be synchronized to the target database in real time , Realize real-time data correspondence between the target database and the source database .
But no matter which type of data integration , In the traditional data integration process realized by coding , As long as the amount of data reaches a certain level , The problem of low data synchronization efficiency will appear . This is because in the enterprise data mining analysis project , Data often exists in the independent data system of each department , These small systems are disconnected from each other and form “ data silos ” , When it is necessary to transfer data, it is also necessary to transfer technicians separately according to specific needs , Not only the processing cost is high , Long processing time , The data computing pressure brought by massive data often causes problems in the data migration process , Impact on the overall data quality of the project .
Aiming at the low efficiency of data synchronization , stay Tempo DF in , We go through 3 Three aspects of functional design to help enterprises optimize data synchronization ：
Speed
High performance computing engine ensures efficient migration of massive data
During the implementation of enterprise level massive data integration and migration requirements , The most important issue is the fast and stable data migration .
In terms of data migration speed ,TempoDF The product has been verified in the cluster environment MySQL to Hive The migration speed can reach 190000 strip /s、 Unstructured files FTP to HDFS Migration reachable 150~160M/s. At this stage, all the projects that have been implemented have realized the stable operation of the process , It can fully meet the requirements of enterprise data warehouse 、 Data migration requirements of the data Lake project .

Simple and convenient
Unified arrangement 、 Dispatch 、 Control processes to reduce operation and maintenance costs
To improve the efficiency of data synchronization , In addition to improving the speed of data migration , We can also accelerate project completion time by simplifying and merging related work tasks .
Tempo DF Job scheduling capability and scheduling in 、 Operation and maintenance functions , All migration tasks can be conveniently and centrally carried out , The granularity can be detailed to every structure / The migration of unstructured data is set according to the actual needs .

At the same time, it supports application process publishing and management , It is convenient for the administrator to maximize the process execution efficiency according to the actual situation . When a synchronization task has a problem , Only relevant business data processes are terminated , Other migration tasks are running normally . After the problem is corrected, it can be supplemented again . Make data migration more relevant to the actual business .
intelligence
Easily configure data migration tasks
Because the data integration work often involves the data of multiple business systems , In the traditional processing flow , The coding workload involved is often very large . and Tempo DF Designer tools in , The complex data migration development process can be simplified into a drag and drop operation , Low code intelligent operation helps technicians save working time , Improve work efficiency .

adopt TempoDF With the help of , When data development engineers handle data table migration tasks , No additional coding work is required , Just configure the data source in the designer 、 Data target , And quickly confirm the data type mapping between the two heterogeneous libraries , You can easily complete the task . At the same time, data processing during migration can be configured 、 Resource parameters 、 Flow parameters and dirty data contents . Implement structured 、 Full migration of unstructured data 、 The incremental migration .
Traditional enterprise digital upgrading and transformation process , Often confined to a single business , It ignores the related data of multiple businesses within the enterprise , Lack of deep understanding of data , As a result, the enterprise has obviously invested a lot of costs , But it is difficult to give full play to the valuable value of data assets .
So in the era of digital economy , If enterprises want to truly realize digital transformation , We must pay attention to the implementation of data integration , By connecting global data , Build a standard data asset system after purification and processing , Meet the growing demand of enterprise business for data .
Merrill Lynch data Tempo DF The platform is such a solution that can provide mature massive data integration , Products that complete the first step of massive data analysis and decision-making . From then on, the development and implementation personnel do not have to worry about the underlying data interruption exception every day , Quickly realize the efficient flow of massive data , Directly improve project delivery efficiency 、 Solve the problem of enterprise massive data integration , Lay a solid foundation for users' subsequent data analysis .
If you want to experience a more efficient and convenient data integration implementation , Welcome to Merrill Lynch data products website Apply for probation ~