Data Warehouse ETL is a series of processes:
- Extract data from across an organization;
- Transform data for consumption;
- Load data to a relational or non-relational data store;
- Use this data to facilitate business intelligence, reporting, analytics, data science, and other activities.
Let's Unpack This:
Any time one moves data there is an exceptionally high probability that some type of data transformation will need to take place.
It’s a natural implication of modern business that important company data will become siloed. When data is siloed, it’s hard to make meaningful business decisions, being uncertain of whether or not you have all the information.
Many issues can contribute to this data silo challenge — several operational units may use different software to do the same activity. Business critical legacy systems often house eons of historical data in system-specific data formats – making extraction and prep extremely time consuming. Still too – employees often store key dataset in programs like Excel making it less accessible.
So how do we gather all of the data for a data warehouse?
Extract
Adaptors are pieces of configurable software designed to “extract data.” For example, in K3 we have a “CSV Adaptor” that allows you to drop CSV files in a folder for automatic import. As you can imagine there are A LOT of adaptors out there for nearly every type of scenario: databases, APIs, files in different formats etc.
How do we transform data to be useful in a data warehouse?
This is but one teeny tiny sliver of an enormous data transformation challenge. Data analysts spend 90% of their time wrangling data.
Transform
Once we have all the data together we notice a big problem: all the data looks different. One software system labels customers as “Cust” and another refers to them as “CS.” If we wanted to run a report of our customers, we’d have to align these to be the same. This is where the transformation part of ETL could take both of these and sync them as “Customer.”
This is but one teeny tiny sliver of an enormous data transformation challenge. Data analysts spend 90% of their time wrangling data. With our K3 ETL tool, we have created this process in a low-code environment (in other words, we put it in a really good user interface).
How do we load data into a data warehouse?
Load
Once the data is harmonized, canonical, prettied and generally cleaned up, it’s ready to be loaded into an SQL or NO-SQL database. SQL examples include Oracle, Postgres, MSSQL, etc. NO-SQL databases are Snowflake, Redshift, Mongo, SAP Hana, etc. There are tradeoffs for each, which is a longer discussion.
If we’ve done our job right, at the end of the day, a business will be able to make confident decisions with the optimized data.
If we’ve done our job right, at the end of the day, a business will be able to make confident decisions with the optimized data.
Better yet, they can quickly and easily de-silo their data with K3, our low-code, easy-to-use and understand Data Warehouse ETL solution developed specifically to simplify data prep, integration and transformation for business and IT/technical users alike.
Contact us to see how we can help with all of your data management needs or stand K3 up for yourself in the Amazon Marketplace.
