Companies can use dozens of different information systems whose raw data must be able to be transferred to data lakes, data warehouses, or for analytics, ML/AI, or visualization. One of the primary obstacles to building a robust solution is a simple fact that data has traditionally been hard to share, use and trade. Raw data often resides in disparate data formats (structured, semi-structured) that do not play well together.
Data pipelines are crucial to support the data economy. A data pipeline is a managed entity for data processing and the creation of data products that generate business value. Data products can be, for example, Power BI reports or datasets generated by ML / AI algorithms that are used through APIs.
The data pipeline contains and combines several components that cover connecting and reading the data sources, processing and analyzing the data, transforming it into different data models, and sharing the data through the processed data products. The components are often based on a micro-service model, so individual components may have different developers and different lifecycles. Data pipelines can be made, just like applications, in cloud-native or open-source.
The data pipeline key elements:
Origin - The origin represents the source from which the original data resides.
Destination - The final destination is the ultimate point to which the data is transferred. The final destination can be a data warehouse, an API endpoint, an analytics tool, an application, or more.
Dataflow - Dataflow refers to the movement of data between the origin and destination. One of the most widely used methods for moving data is Extract, Transform, Load (ETL).
Storage - A storage system refers to all systems used to preserve the data throughout the stages of data flow.
Processing - Processing includes all activities involved in moving the data. Data processing can be batch processing, in which data is collected at regular intervals (e.g. once per day) and subjected to performance-intensive processes before the data is distributed. Alternatively, a so-called stream processing with less performance-intensive and more interactive processing can be used. Stream processing operating costs are generally lower than batch processing.
Workflow - Workflow represents a series of processes along with their dependencies in moving data through the pipeline.
Monitoring - Monitoring ensures that all stages of the pipeline are working correctly.
While data pipelines help organize the flow of your data, managing its functions can be challenging. To ensure efficient operation, there are several useful tools and services that serve different needs. Also, in this case, it often makes sense to use services instead of your own work and infrastructure. The professional is able to build a data pipeline so that it is scalable, cost-efficient, secure, capable of real-time data processing and can operate even under heavy load.
Let us in Data Product Business help you when building your data pipelines and data products!