The data development platform is a one-stop, full-scenario data processing platform that integrates stream and batch processing. It supports PB-level data processing and is compatible with traditional data warehouse processing. It is an essential foundation for building an enterprise-level big data processing platform. Its output results are mainly used for real-time/offline data analysis and display.

If you understand data warehouse technology, you can understand the data development platform as a big data ETL tool. ETL is the abbreviation of Extract-Transform-Load, which is used to describe the process of extracting, transforming, and loading data from the source to the destination.

Data extraction can be divided into offline extraction and real-time extraction. Offline extraction can be applied to scenarios where data updates are not sensitive within a certain period of time, generally scheduled extraction within a fixed period. Real-time extraction can be applied to scenarios that are very sensitive to the latest status of data. When new data is inserted or old data is updated, extraction is performed immediately.

Data conversion is the core link, which involves data cleaning, format conversion, missing value filling, duplication removal, information addition and other operations on the extracted data.

A set of data with unified format, high structure, high data quality and good compatibility provides reliable data support for subsequent analysis and decision-making.

Data loading refers to loading the processed data into the target database after extraction and transformation are completed.

Therefore, the data development platform is like a data processing plant, which extracts the original data from the forms of this application or across applications, and further summarizes, processes, handles and integrates them.

Obtain accurate, reliable and consistent result data. The entire process is low-latency and highly real-time, thus meeting the diverse, flexible and complex data analysis needs of enterprises.

Smart Data Development Platform-Function

Enterprises often have a large amount of raw data that has not been processed, so it is difficult to obtain data and the data quality is worrying, which also leads to high data analysis costs. The main function of the data development platform is to integrate various scattered, non-completely structured, and non-uniform data in the enterprise, so as to form a unified enterprise-level data warehouse and provide a quality-assured data source for the enterprise's analysis and decision-making.

The data development platform supports both offline consumption and real-time consumption modes. It can meet the needs of building consumption scenarios with full coverage, full append, and incremental judgment conditions based on timestamps/log files/queues/incremental fields, providing enterprises with a very broad range of operation plan options.

To cope with complex data environments, the data development platform provides complete data cleaning functions, ensuring high reliability of data supply through operations such as field verification, key field inspection, format type consistency inspection, and invalidity inspection. For possible missing spaces in the data, the data development platform provides missing value filling functions to ensure the consistency and integrity of data types. At the same time, to prevent repeated data uploads that affect the accuracy of business analysis, the data development platform supports flexible settings of filtering rules, which can continuously monitor and filter data, identify problems, and prompt users to solve them.

During the data processing, the data development platform can complete field splitting, field merging and field matching. Field splitting is to intercept part of the information in a field and split the field into two or more fields. Field merging is to combine several fields into a new field or combine field values ​​with other text, numbers, etc. to form a new field. Field matching is to obtain the required data from the associated database with the same field. Generally speaking, field matching requires that there is at least one associated field between the original database and the associated database, and batch query matching of corresponding data is achieved based on the associated field.

To ensure the flexibility of data supply, the data development platform is equipped with a data conversion function, which can unify and standardize the data format to facilitate subsequent analysis operations, including record format conversion (for example, conversion to json, csv, txt, avro, etc.) and field format conversion (unify the format of field values ​​and standardize the calculation caliber). Among them, field format conversion supports structure conversion and row and column conversion. Structural conversion is to adjust the structure of data according to business needs, mainly referring to the conversion between one-dimensional data tables and two-dimensional data tables. Row and column conversion is to convert row and column data to observe data from different dimensions, so that it meets the needs of business analysis. In addition, during the data processing process, users can also perform encryption/decryption operations on data as needed, add custom identification fields, and further meet their personalized requirements.

Smart Data Development Platform-Value

In the process of data analysis, the time and cost spent by users on data processing often accounts for more than 70% of their overall investment. The data development platform is the data "engine" in the big data analysis system, and constitutes a very important part of the data warehouse system, playing a key role in connecting the past and the future.

The data development platform is responsible for extracting data (including relational data, flat data files, etc.) from dispersed and heterogeneous data sources, cleaning, converting, integrating, and finally loading it into the data warehouse or data mart, providing a stable and unified data source for online analytical processing and data mining. Therefore, the data development platform embodies the following values ​​in the enterprise data governance system:

Break down data silos: Integrate data silos generated during the enterprise informatization process to generate a data view for the entire enterprise, providing a comprehensive, stable and quality-assured data source for the enterprise's analysis and decision-making.

Mining data value: mining enterprise big data resources, obtaining the rules or trends hidden in the data, and then making the next prediction. This process requires the use of complex algorithms, statistical models and a large amount of data, so the parallel algorithms and grid computing capabilities provided by the data development platform will play a key role.