Significant volumes of data are generated from customers, partners, product engineering, and many more operational teams of an organization every day. Most of this data generated is stored in inconsistent formats across data stores and for any data-driven organization, data is a goldmine.
A traditional data pipeline moves the data from source to destination, but there could be one or multiple pipelines originating from the same source. Data pipelines were originally designed to connect data from sources and collect them onto a single storage system; another level of pipelines emerged when the collected data was moved to an algorithmic solutions platform.
Data pipelines are still an integral part of many organizations, but the increased number of data stores and changing data demands across decision makers is making the pipeline seem like legacy technology.
Why Traditional Data Pipelines are Hard to Work With
Source or Sources: Data may come from a warehouse, data lake, SaaS application, or any other form of data store. A pipeline needs to be built from each source and each source needs a connector, so in a multimodal network data pipelines are fragile leading to data loss.
Data Ingestion: During new data integration, a complete backloading is performed for the entire history into the data store. Then, to reduce the pipeline stress on a computing system, we discard the code for initial ingestion. Regular ingestion happens every day or as scheduled, capturing the data with new data. In some cases, the data pipeline may get compromised, and now to regain the data you would need to backload the complete data into the data store.
Pipelines are built on a single script: Most data pipelines are built into a single script, so when the script fails for a certain data type, data engineers will need to go back and troubleshoot. Such data pipelines are cumbersome, and after fixing the errors the complete data pipeline would need to be manually audited.
Quality Measurement: Once the data is ingested into the pipeline, performing transformation can be impossible or difficult in real-time. Once the error is induced in the data, the error will trickle down through the other pipelines into analytical algorithms or operational systems.
Data pipelines are built on a linear structure for the flow of data, while the modern data requirements are ascertained based on customer inputs, value comparison, third-party access, and data development.
How Data Flows Address Modern Data Needs
Bidirectional Flow: Operational data needs real-time or near real-time processing, while in analytical data batch-based processing is performed. Bidirectional flow of data can help you to eliminate the data sprawl. Using the data flow, records can be requested anytime once the flow is built.
Self Service: Data flows can be automated based on each endpoint, hence if you have a business leader who wants to view customer data for the last week, they would be able to do it without manually extracting the data and pipelining through the process using data flows. A low-code/no-code data flow reduces the build time, and enables inspecting of the output.
Governance and Access: Data flows create improved governance of data.Bidirectional connectors enable users to go and use data as required rather than create copies on systems to improve processing speed. Access can be measured and controlled based on users and requirements without any extra layer of authentication.
Branches from Data: Data flows can empower operational systems. Suppose you run an eCommerce website where a customer is facing a delay with an order, the customer support in a traditional pipeline environment would take hours to pull that data from logistics. But with data flow, customer support can get the order details and logistics data while the marketing department also can get a slice of that data for running their analytics.
If you want to build your own data flow that is scalable, repeatable, and automated, contact us.