Ensuring High-Quality Data with Snowflake's Data Validation Features

What is Data Validation? 

Have you ever wondered what data validation is all about? Think of it as the gatekeeper for your data, ensuring everything’s in order. When handling data, it’s a big deal because it keeps the information you rely on accurate, consistent, and genuinely helpful in making decisions. Picture running a business with messy data—you’d end up with bad calls and wasted time.

Data validation steps to double-check everything before you save it, work with it or dig into the details. Get it right, and you have a rock-solid foundation you can trust for all your business moves and number-crunching. 

Why Bad Data Can Hurt Your Business 

Insufficient data is like a hidden trap waiting to trip up your business. It’s not just a tiny annoyance; it’s a big deal that can throw everything off track. 

Here’s how it plays out: 

• Decisions That Flop: When your data’s off, it’s like flipping a coin instead of planning. You end up making moves that backfire big time. 

• Money Down the Drain: Bad data can turn your budget into a black hole. Studies say it can gobble up 15-25% of your revenue—a serious chunk of cash is disappearing. 

• Customers Walking Away: If you’ve got the wrong contact info, your marketing team might as well be talking to a brick wall. Customers don’t get what they need and are not shy about taking their business elsewhere. 

• Inventory Headaches: Stock too little, and you’re waving goodbye to sales. Stock too much, and you’re stuck with stuff collecting dust. Either way, your profits take a hit.

• Regulatory Trouble: Mess up enough, and you might catch the attention of regulators who aren’t known for their sense of humour. 

Fig 1.1 Standard Data Validation vs Data Validation in Snowflake 

How Snowflake Helps Solve Data Problems 

What is Data? 

Data is all those raw bits and pieces—like facts, numbers, or little details—that businesses scoop up every day. Think about customer purchases, team member info, or stats about what’s trending in the market.

It’s not just about piling up a mountain of this stuff. What matters is that it’s good quality, makes sense for your needs, and isn’t a hassle to get your hands on. Data is like today’s companies' heartbeat—it keeps things pumping, from the small choices you make daily to those big, game-changing plans. 

Types of Data in Businesses 

Let’s talk about the kinds of data businesses juggle daily—it’s not just one flavour; it’s a whole assortment! Here’s the scoop: 

• Structured Data: This is the super-organized type. Imagine a filing cabinet where everything’s labelled and lined up just right—think spreadsheets or database tables with neat rows and columns. 

• Semi-Structured Data: This one’s a bit more laid-back. It’s got some order, but it’s not fussy about sticking to a strict grid. 

• Unstructured Data: Now, this is the free spirit of the bunch. We’re talking emails, photos, videos—stuff that doesn’t play nice in tidy little boxes. 

Common Data Quality Issues 

Let’s be honest: data can sometimes be a mess. It’s not perfect, and those imperfections can throw a wrench on things. Here’s what tends to go wrong: 

• Missing or Incomplete Data 

• Duplicate Records 

• Inconsistent Formatting 

• Outdated Information 

• Schema Drift 

How Do Data Errors Happen? 

So, where do these slip-ups come from? Well, it’s usually a mix of us humans messing up, tech going haywire, or stuff getting lost in translation when combining sources. Check out some examples: 

• Form Design Issues: A confusing online form that leaves users scratching their heads—and entering junk. 

• Software Bugs: Some glitches in the system twist the data while handling it.

• ETL Failures: That’s “extract, transform, load”—when moving data around, it goes wrong. 

• Sensor or Equipment Malfunctions: A wonky device spitting out nonsense readings. 

• Improper Data Type Conversions: Accidentally turning a number into text and throwing everything off. 

Your First Look at Snowflake's Data Protection 

What is Snowflake? 

Imagine you’ve got a massive pile of data—like a library stuffed with books—and you need someone to keep it all organized and easy to find. That’s where Snowflake comes in. It’s a cloud-based platform that acts like a super-smart librarian for your data, efficiently handling storage, processing, and analytics.

What’s excellent about Snowflake is how it adapts to whatever you throw. Whether your data grows overnight or you need quick answers from a significant analysis, it covers you. 

Snowflake splits things up in a way most old-school databases don’t. It separates storage, computing power, and services into three distinct layers. This means you can crank up one part—like adding more storage—without messing with the others. It’s perfect for businesses whose data needs are all over the place, and it’s secure and straightforward to use to boot. 

How Snowflake Keeps Your Data Safe 

Let’s talk security—because nobody wants their data to fall into the wrong hands. Snowflake locks things down tight. For starters, all your data is encrypted, whether it’s sitting still or moving around. They use AES-256 encryption, like putting your info in a safe that only you can unlock. 

And if something goes wrong? No panic. Snowflakes has a “time travel” feature that lets you rewind to an earlier version of your data. Plus, with automated backups, your info’s always tucked away safely. 

Simple Ways to Check Data Quality 

You’ve got all this data—how do you ensure it’s legit? Snowflakes make it straightforward. Here’s how you can keep things in check: 

• SQL Queries for Missing Values: Run a quick check to spot any holes—like flipping through a book to see if pages are missing. 

• Constraints for Data Integrity: Set ground rules, like ensuring key fields aren’t blank. It’s like insisting every book has a title. 

• Consistency Checks: Make sure everything lines up across your datasets—think of it as checking that an author’s name is the same everywhere. 

• Data Profiling Functions: Snowflake’s built-in tools are used to get the lay of the land, such as seeing how many unique entries are in a column. It’s a snapshot of your data’s health. 

• Automated Testing Frameworks: Want to step it up? Set up regular tests to clean your data—like a robot double-checking your work. 

Easy Data Validation Techniques 

Checking for Missing Information 

Missing data is a total pain. It’s like trying to piece together a story when half the pages are gone—your analysis ends up shaky and unreliable. Thankfully, Snowflake’s got our back with neat tools to catch those gaps.

For instance, you can use

COUNT(NULLIF(column_name, '')) to spot where values are missing in your datasets. 

Ensuring Data Makes Sense 

Let’s be honest; data can get weird sometimes. You’re running a business, and suddenly, you notice some orders have shipping dates before the order dates. That’s like delivering a package before someone clicked “buy”—total nonsense, right? Or maybe you see a customer listed as 150 years old. Unless your business caters to immortals, that’s a red flag. 

Basic Rules for Good Data 

So, what’s the secret sauce for “good” data? It’s not just about piling up tons of it—quality’s where it’s at. Here’s my rundown of the must-haves: 

• Accurate 

• Complete 

• Consistent 

• Timely 

• Relevant 

• Accessible 

• Compliant 

Think of these as your data’s rulebook for staying sharp and reliable. Stick to them, and you’re in great shape. 

Practical Examples for Beginners 

Real-World Data Validation Scenarios 

Typical scenarios where data validation is essential include verifying customer details during onboarding or ensuring product pricing accuracy in an e-commerce database. For example, an online retailer might validate that: 

• Product prices are positive numbers 

• Discount percentages fall between 0% and 100% 

• Order quantities are integers greater than zero 

• Shipping addresses contain all required fields 

• Email addresses follow the correct format 

Step-by-Step Examples 

Example: Checking for duplicate customer records in Snowflake: 

Example: Validating email format: 

 Example: Identifying outliers in transaction data: 

Common Mistakes to Avoid 

• Not enforcing unique constraints 

• Failing to handle NULL values properly 

• Overlooking data type mismatches 

• Ignoring case sensitivity in string comparisons 

• Assuming all dates use the same format 

• Neglecting to validate data after migrations or transformations 

• Implementing overly strict validation that rejects valid but unusual data 

Tools and Features for Data Checking 

Snowflake's Built-In Validation Tools 

Snowflakes covered you with a toolkit that makes data checking less like a chore. Here’s what you’ll find inside: 

• Constraints (like PRIMARY KEY, UNIQUE, NOT NULL): These are like little signposts for your data, pointing out what’s allowed—like no repeats or empty spots. 

• Query-based validation: It’s like having a data detective on speed dial—just run an SQL query to spot anything that doesn’t look right. 

• Stored procedures for automation: Think of these as your trusty assistants, taking care of the repetitive stuff so you don’t have to. 

• Task scheduling for regular checks: Set up a routine, and Snowflake will monitor your tasks—like reminding you to double-check your work. 

• Streaming and time travel features: Have you ever wanted to see what your data looked like yesterday? Snowflake lets you rewind and track every change. 

Simple Checks You Can Do 

You don’t need to be a tech genius to keep your data in line. Here are some easy, practical things you can try: 

• SQL queries to spot anomalies: Whip up a quick query to catch weird patterns—like numbers that jump out of nowhere or gaps where data should be. 

• Automated alerts for inconsistencies: Set up a heads-up system so you immediately know if something’s off. 

• Periodic data audits: Give your data a regular checkup—maybe once a month—to ensure it’s all good. 

• Validation dashboards: Build a simple dashboard to monitor your key stats, similar to a dashboard for your car but for data. 

• Metadata tracking: Snowflake monitors changes to your data’s setup so you can see if anything has been tweaked behind the scenes.