Hey there, data enthusiasts! Today, we’re diving into something that might seem small but can be a huge headache—special characters in your column names. Yep, those pesky spaces, hyphens, symbols, wingdings (yes, the 90’s are back, baby!) can really mess up your data loading process. But don’t worry, we’ve got a practical solution for you: DataLakeHouse.io. Let’s chat about why it’s the best way to load files into any data cloud, especially when you’re dealing with those tricky special characters.
The Special Character Struggle
We’ve all been there. You’ve got a file(s) with column names that include spaces or weird symbols, and suddenly your smooth data loading process hits a bump. Traditional methods to handle this can be a pain. Renaming columns manually? Writing complex scripts? Letting your developers who are leveraging the data downstream each handle this their own? No, thank you! These methods not only take forever but also leave plenty of room for mistakes and inconsistency.
Let’s Look at an Example
Uploading a csv file from your laptop into a Snowflake table is simple, easy as a one-time activity.
Once the file has been uploaded Snowflake does a basic check of formatting and lets you know if it finds any errors.
It’s common for column headers to have a space between words so Snowflake flags this to determine how you want to address. Clicking on the errors dropdown, we click on the Autofix which tells Snowflake to replace a space with an underscore _ in the column heading.
For column names that have special characters, such as a () or wingdings…it’s fun to say out loud…Snowflake places double quotes around the column header. In our example, the column name in the CSV is titled Net Promoter Score (NPS)
Load your data and the column name is set to Net Promoter Score (NPS) in the Snowflake table. Great?…
select * from DLH_DEMO_SANDBOX.TEST.CSV_LOAD
where "Net Promoter Score (NPS)" = 358
This is ok for quickly getting data into Snowflake since what’s happening is ANSI compliant (IT speak for a set of common standards to query a database); however, if you’re using a tool to query this data you’ll save your developers, potentially yourself, from wasted time. For example, in PowerBI one needs to apply the following in order to query the column with a space in the column name.
In the power query the column name with the special character, ” ” in this case, just needed to be set in a double quote.
SELECT TRANSACTION_ID, TRANSACTION_TYPE, “”Net Promoter Score (NPS)“” FROM DEMO_DB.PO.LIBRARY_METRICS
The single quote ” and squared brakes [] both didn’t work in the power query.
Many tools recommend not using quoted identifiers for database object names. These quoted identifiers are accepted by some tools, but they may not be valid when using other tools that manage or query database objects.
Why Datalakehouse.io to the Rescue
There’s got to be a better way?! Yes, Datalakehouse.io makes this whole process a breeze. Here’s why:
1. It Handles Special Characters for You
One of the coolest things about Datalakehouse.io is that it automatically deals with special characters in column names. No need to spend hours renaming stuff or writing scripts. It just works, saving you tons of time and hassle.
In our example, upload the CSV file into DataLakeHouse.io and run a Sync Bridge to land the data into Snowflake where any downstream process or application can easily consume this column. No additional coding or manual processes needed!
2. Easy Peasy Data Pipeline Management
Managing data pipelines can be complicated, but not with Datalakehouse.io. Its user-friendly interface makes setting up and managing your data pipelines, Sync Bridges, a walk in the park. You’ll be up and running in no time without breaking a sweat.
3. Perfect Fit with Data Clouds
Datalakehouse.io and data clouds are like peanut butter and jelly—they just go together. The platform is designed to work seamlessly with Snowflake, BigQuery, Azure, Teradata, Redshift, etc. supporting a bunch of data formats and sources. This means you can get your data into your data cloud without any compatibility headaches.
4. Top-Notch Data Quality
Good data is crucial, right? Datalakehouse.io has your back with solid features for data validation, transformation, and governance. This ensures that the data you load into Snowflake is top quality, helping you make smarter decisions based on reliable info.
5. Scales Like a Boss
Got a ton of data? No problem. Datalakehouse.io is built to handle big data volumes with ease. It scales like a champ, so your data loading process stays smooth and fast, no matter how much data you’re dealing with.
The Alternatives: Meh
Sure, there are other ways to load files into your data cloud, but they usually come with more hassle. Manual methods are slow and error-prone, and custom ELT solutions require a lot of effort and maintenance. In contrast, Datalakehouse.io automates the hard stuff and integrates seamlessly with your data cloud, saving you time and reducing the chance of mistakes.
Wrapping It Up
Handling special characters in column names can be a real pain, but Datalakehouse.io makes it easy. It automates the tricky parts, simplifies data pipeline management, and ensures your data is top quality. Plus, it scales effortlessly to meet your growing data needs.
So, if you’re looking for a hassle-free way to load your files into your data cloud, give Datalakehouse.io a try. You’ll wonder how you ever managed without it!
Until next time, happy data trails!
