We live in a world of big data. Data is created, updated, and deleted at a pace not imaginable just a decade ago.
In 2013, we created (just) 9 zettabytes of data.
For context, one zettabyte is one trillion gigabytes. One gigabyte can probably allow you to stream 200 songs, or it’s about a 65,000-page word document.
So think about those, and now multiply them by a billion.
Now, we’re going to go even larger.
In 2023, we are on track to create to create 120 zettabytes of data.
Wow!
It’s pretty wild to think about how much data creation has changed in such a short amount of time.
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.
On a given day, an employee might jump into Hubspot, use two-factor verification to get into Teams, then into Salesforce, use Microsoft Office to write a note, and then they might use Slack to update their boss!
Those are all data points, no matter how small. Those small data points clearly add up.
The 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 Extract, Transform, Load makes Big Data more accessible, and more useful for ML Applications
ETL stands for ‘Extract, Transform, Load’. This refers to the set of processes to extract raw data from one system, transform it, and then load it into a data store or data warehouse. 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.
And obviously, only useful data does you any good. We’ve talked before about making sure that your data is quality.
So using the right data is just as important as having enough data.
For 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 dynamic ETL Tools can be a challenging software engineering hurdle
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?
There 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. This can be done by your data engineering team.
Hence, it is key to modularize such features for easy reuse. Writing code driven by metadata will allow for reuse.
There are several tools available for this step in the process.
Let’s take a look at our options.
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 and useful
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.
And now, you’re equipped with the best grasp on dynamic ETL to make a decision on implementing it.
Knowledge is power, right?
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. We’ve been working on something awesome.
Tingono is here to help!
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.
Everyone’s in a different place, and that’s perfectly fine.
As we said, Dynamic ETL is so great because it’s both customizable and adaptable for many varied business cases.
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!
