When extracting data from a JSON file, try the JSON Input Node.
JSON (JavaScript Object Notation) is a common data source.
With ET1’s JSON Input Node you can quickly open your JSON files and begin to transform the data, merge it with other data like CSV data.
In ET1, data is normalized in a data grid view to understand the data as if it was in a normalized view. This is due to the desire for most users to see and understand their data like a data grid. However under the hood the data is JSON because under the hood of ET1 is JavaScript and a DAG Streaming (Graph) engine, which enables ET1 to offer features you’ve never seen before in an ETL software!
Using JSON Input Node
Find the Json Input Node in your with your Hands, or in the hamburger menu in the top right of ET1.
Once your JSON Input Node is on the canvas:
Drag and drop the json file on the node
Click the drag and drop area and a “browse to file” tool will open
find the JSON and open
If the JSON is structured correctly, the node works.
Otherwise the node does not work
Example of JSON format that will work:
[
{
"name": "Impossible",
"mean": "0.5",
"const": "CATS GO MOO"
},
{
"name": "Almost No Chance",
"mean": "2.0",
"const": "CATS GO MOO"
}
]
Now that you’re familiar. Let’s see JSON Input Node in action!
Thanks for learning more about ET1 and know if you have any questions… Please contact us.
Trim/Normalize Node is built to help you quickly clean your data pipelines and like the Column Renamer, built to make data pipeline maintaining simple, not complicated, and more than anything, easy to repeat.
AT TIMES WE NEED CAPITAL LETTERS! Perhaps you-have-a-lot-of-this-happening (special characters you don’t ne3ed).
then there are times we aren’t trying to scream, and perhaps lowercase is a requirement for user names or emails. okay, you’re in a good place. case sensitivity is here too. AlongWithTrimmingWhiteSpace.
ET1’s Trim/Normalize Node helps people quickly clean their data.
You can select more than one column to clean, or just choose 1 column to normalize.
The Trim/Normalize Node was created to help you people quickly clean data pipelines and improve data quality across your data environment (a data environment might be a grouping of individual solutions that look and feel similar).
Cleaning dirty unstructured text for sentiment analysis, parsing HTML, or optimizing pipelines for data visualization – this node helps transition your pipelines into what some consider a piece of their overarching data governance.
Using the Trim/Normalize Node in ET1
Using this node is easy and intuitive. Checkboxes, drop downs, and nothing crazy.
Connect data downstream to your node, adjust the settings, and keep solving.
Connect data
Choose column(s)
Decide to trim ends – space(s) on the left and right only
Decide to remove whitespace – any and all space(s)
Remove special characters, any characters, includes spaces
Choose the case sensitivity
Real-world use case Trim/Normalize Node
In this example we are gaining a file from an end user who needs help with capitalizing all of the Address.
Someone sends us this csv. We open it with the CSV Input Node in ET1 then we want to trim/normalize.
Supplier_ID,Supplier_Name,Address,Email
SUP001,Supplier X,123 Main Street|Suite 100|Anytown|CA 90210,supplierx@example.com
SUP002,Supplier Y,456 Oak Avenue|Building B|Sometown|NY 10001,suppliery@example.com
SUP003,Supplier Z,789 Pine Road|Floor 3|Othercity|TX 75001,supplierz@example.com
We are going to add trim ends, incase future data has padded spaces (thinking ahead), and swapping case to upper to follow internal best practices.
Upper case for Address passes this users current data strategy, their reasoning; some data inputs do not automatically swap to uppercase during the software writing to the database, and the software engineers don’t have time to optimize this part of the software.
Thanks for learning more about ET1 and know if you have any questions… Please contact us.
On your magic quest to join data? We call it the Joiner node.
A simple joining solution that helps people join data at a row level.
In ET1, Joiner is focused on “keeping it simple” and will aim to automatically infers your joins.
ET1 assumes.
Inferring a join means it assumes you prepared the data prior. Like, Id = Id..
Without preparing the data stream prior, the assumptions may fail. Use this to your power, and save time by letting ET1’s Joiner Node assume the correct column for you.
Hint; make it easier by preparing your column headers before using the Joiner Node by using the Column Renamer Node. This will help you save time while using ET1.
Right join is possible by swapping which table you connect to the Joiner node first. This order of operation is considered, and by adjusting what connects to this node first – you’re able to right join. You’re simple using the left join and the understanding of what you just read.
Type: The style of join. Today, we only have inner and left join.
The Joiner Node is the tool for joining data at a row-level, it removes complexities when joining data, and these row-level relationships are likely the ‘key’ we need to use the ET1’s Joiner Node.
Goal, join our data to see if we need more inventory.
Problem, the data is broken into many different tables.
Use case: Purchase data and inventory data can be joined, lets break it down.
While analyzing this request, I found the data has duplicate entries on the column Product. Product has a relationship between tables. However we need the tables to be grouped, or we will be creating a many-to-many join.
Here’s how our Inventory data will look after we group by Quantity, and rename our header to Inventory.
To create a KPI, you need to choose the column and how to aggregate.
Setting up your KPIs in ET1
Open an Aggregate Node, stream data into this node, and open the settings.
We need to create a Sum of Quantity.
We need to swap the measure column to Quantity and the operation to sum!
Recap: The Aggregation Node operation is set to sum and the measure column is set to quantity and this creates a single KPI value for the column quantity.
