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How to make business data more accessible with Dynamic ETL Pipelines

Sumit Raj
September 08, 2022

We live in a world of big data. Data is created, updated, and deleted at a pace not imaginable just a decade ago.


I mean, data is coming at us from all angles. It's produced on every domain and every app service. This is especially true for business data. Enterprises rely on dozens (hundreds?) of SaaS apps every day.Slow Down GifThe sheer volume of data is the main reason it’s so challenging to actually make use of it. It’s one reason why people continue to use their gut to make business decisions.


Yet, there are ways to manage the challenge of data volume. Ways that can make data more accessible and useful for business intelligence…

Dynamic ETL makes Big Data more accessible

ETL is an acronym for ‘Extract, Transform, Load’. This refers to the set of processes to extract data from a source, transform it, and then load it into a data store. This allows the data to be used for machine learning.


The whole point of ETL is to make data accessible. So, how the ETL process is set up will impact the usefulness of your data.

Transformer GifFor instance, you can choose to hardcode the ETL process, so it always happens the same way. However, this approach will likely break when it encounters data it doesn’t expect. It will also likely not scale well for the same reason.


A better approach is Dynamic ETL.


When we keep the ETL pipeline dynamic, we can work with data in different states from different sources. We can also dynamically transform (i.e. prepare) the data as needed without going through the process of hardcoding every possible scenario we might encounter.


This ensures the data pipeline can scale across a wide range of use cases.

Implementing a dynamic ETL comes with challenges

Architecting a dynamic ETL can be a complicated process. And it takes some effort and patience to do this effectively.


Just consider the following points as you begin:

  • What protocols are used by sources for communication?
  • Which attribute of an entity is a global identifier?
  • Is there an entity present in multiple sources which needs to be merged?
  • What transformation steps are needed to fit extracted data into a target schema?
  • Do we need to clean/filter the data?

Extra Spicy GIFThere are many design considerations that need to be made when planning and architecting a dynamic ETL system. We need to ensure that the system is scalable without hardcoding it.

A feasible data strategy? A modular, metadata driven approach

One approach would be to start with limited dynamic features. An example would be starting with a pipeline that supports only HTTP REST calls for ingestion.


As more and more use cases appear, the pipeline can be adjusted to support an increasing array of features. Hence, it is key to modularize such features for easy reuse. Writing code driven by metadata will allow for reuse.Parent Trap GIF

There are several tools available for this step in the process. Tools such as Rivery, Fivetran, and dbt support a wide range of techniques across Extraction, Transformation and Load. These tools can make this task much easier. However, the cost and the initial learning curve should be considered when deciding whether or not to use them.

Tingono makes your customer data easy to understand, useful, and beautiful

So, it’s likely Dynamic ETL makes sense for your data-driven business. It helps you scale and make better use of your data. And there are tools that put this approach within reach.

The good news is you may not need to worry about all of this. 😲


This is especially true if you want to use your data to reduce customer churn or expand customer accounts. Tingono is here to help!SLoth GIF

In fact, Tingono is already doing the heavy lifting of Dynamic ETL for you. We adopted the Dynamic ETL approach to data processing because of its ability to scale. And because it can process data regardless of schemes or condition.


This was ideal for us because our platform turns your unique business data into customizable data models. We knew we would encounter vastly different types of data from our customers.


So, whatever condition your data is in, we’re ready for you! And we’re ready to help you use your data to proactively reduce churn and expand customer accounts. Let’s do this!