Data Flows And Data Pipelines: The Evolution of a Process
In addition to the rapidly-increasing amount of external data companies collect, volumes of internal data are constantly generated from customers, partners, product engineering teams, and operational teams in organizations every day. Most generated data is stored in inconsistent formats across data silos, warehouses, data lakes, and lakehouses, and finding relevant data is almost as hard as accessing it.
A traditional data pipeline moves data from the source to the destination, but multiple pipelines can originate from the same source. Data pipelines were originally designed to connect data from multiple sources and collect it into a single storage system, and another level of pipelines are built to move collected data into an algorithmic solutions platform. With the constant need for data pipelines and connections, keeping up with existing pipelines while constantly building new ones is time-consuming and expensive, both financially and in terms of resources.
Data pipelines are still an integral part of many organizations, but the increased number of data stores and changing data demands across decision makers are making the data pipeline seem like a legacy technology. Data flows have emerged as a modern take on the traditional pipeline, providing expanded functionality and usability as well as the flexibility to scale to meet modern data needs.
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; therefore, in a multimodal network, data pipelines are fragile, leading to data losses and/or gaps in governance.
- Data Ingestion: During the integration of new data, complete backloading of the entire history into the data store is performed. Then, to reduce pipeline stress on a computing system, code is discarded for initial ingestion. Regular ingestion happens every day or as scheduled, capturing new data. Should the data pipeline become compromised, data would need to be backloaded into the data store to regain functionality.
- Pipelines are Built into a Single Script: Most data pipelines are built into a single script, so when the script fails for a certain data type, data engineers need to go back and troubleshoot the pipeline. Even after the associated errors are fixed, the complete data pipeline then requires manual auditing, which is time-consuming and cumbersome.
- Quality Measurement: Once data is ingested into a pipeline, performing transformations in real time can be impossible or difficult. Once an error is induced in the data, the error will trickle down through the other pipelines into analytical algorithms and/or operational systems.
Data pipelines are built on a linear structure to follow the flow of data, while modern data requirements are ascertained based on customer inputs, value comparisons, 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 for analytical data, batch-based processing is performed. Bidirectional data flows can help eliminate data sprawl. Using data flows, records can be requested at any time after a flow is built.
- Self Service: Data flows can be automated based on each endpoint; hence, if a business leader wants to view customer data from the last week, they can do so without manually extracting the data and pipelining it through the process by using data flows. A low-code/no-code data flow reduces the required build time and enables inspection of the output.
- Governance and Access: Data flows create improved data governance. Bidirectional connectors enable users to use data as required, rather than creating copies of data to improve processing speeds. Access can be measured and controlled based on users and requirements without any extra layers of authentication.
- Branches from Data: Data flows can empower operational systems. Suppose that you run an eCommerce website on which a customer is facing an order delay. Customer support in a traditional pipeline environment would take hours to pull the associated data from logistics. But with data flows, customer support can obtain the order details and logistics data while the marketing department can also acquire a slice of that data to run their analytics.
While the terms ‘data pipeline’ and ‘data flow’ are often used interchangeably, they describe different versions of the same process. Drawing the distinction between the two and upgrading from data pipelines to data flows is an integral building block of a modern data solution, and using the correct term can mean the difference between manually maintaining a pipeline and automating a robust flow. Which option are you using?
If you’re looking for more information about how Nexla is automating data engineering, get a demo or book your free unified data solution strategy session today. For more on data and data solutions, check out the other articles on Nexla’s blog.
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