ETL frameworks, which make it possible to visually map and normalize data, are imperative for organizations that want to make data-driven decisions. In part one of this two-part blog series, we discussed the importance of establishing an ETL framework[CT1] . Now we’re going to delve into the challenges that can arise during this process and how K3 ETL (extract, transform, load) makes moving data downstream a whole lot easier. While ETL might sound like a simple three-step progression, it need to be treated as a multi-disciplinary framework
Using a no-code ETL tool makes it possible to deliver cleansed and meaningful data without hiring an army of IT specialists. The majority of ETL functions should never be done in raw code. It just creates a massive bottleneck.
ETL Framework Challenges
According to McKinsey, the majority of large IT projects run a whopping 45 percent over budget. Likewise many companies suffer from slow turnaround times on ETL projects. Why? Not enough developers. Here are three thoughts for building a sustainable ETL framework:
Think data integrity and long-term sustainability.
It’s too simple for a good coder to throw in an ETL fix. It solves the problem fast. But this action always throws a wrench into a firm’s data velocity. These little ETL fixes are almost always outside of a standard SDLC (software development life cycle) . Not to mention this organization’s ETL is now tied to a specific IT person or department. What happens when that developer gets busy on another project? What happens when that developer leaves the company>
Best practice: Choose an ETL tool that is sustainable now and in the future. Our K3 ETL solution and accompanying K3 connectors deliver data integrity and long-term sustainability with no coding required. Allowing non-coding business analysts to drive the majority of ETL functions is key to improving and maintaining overall data velocity.
Data transformation should take brains not brawn
Raw data is a beast. The very second one decides to move data there is a 99% chance it is going to need some form of transformation. THis can be visual data mapping, data rules organization and prepping data from a wide variety of sources..
Best practice: An ETL tool that is both intuitive and ready-to-go is an ideal choice when handling a wide variety ofl data forms and non-conforming (read legacy) data sources. K3 ETL seamlessly preps data from different systems into one usable format. It also allows code-free transformation through a specialized mapping and rules engine. Employing a one-stop solution like K3 ETL, K3 for Amazon Redshift, K3 for Snowflake or K3 for Google Cloud makes it possible to handle data flows from anywhere.
Don’t confuse data problems with data science problems
etting your arms around data and maintaining data velocity in an organization is one set of challenges. Analyzing the data and driving meaningful results is another. The important part is that one always comes before the other.
Best practice: If you are having trouble with your data science team or are just building your data science team its important not to get the cart before the horse. A lot of companies make the mistake of hiring data science personnel before they have figured out how they will manage the data science feedstock: data. K3 can help you with that.
Wondering how K3 ETL can solve challenges when establishing an ELT framework? Request a free demo with one of our experts to learn more.
