Everyone in the Indian tech world is talking about Snowflake. It’s the shiny new thing in the world of data, promising to solve all our problems with its incredible speed, flexibility, and power. Companies, from fast-growing startups to massive enterprises, are rushing to move their data from old, clunky systems to this modern cloud data platform. And why not? The promise of easily scaling up, paying only for what you use, and getting insights from your data faster than ever is incredibly tempting.
But here’s a little secret that many learn the hard way: migrating to Snowflake is not as simple as packing your bags and moving into a new house. It’s more like building that new house from the ground up. It’s a complex, high-stakes project that, if not planned properly, can turn into a nightmare of spiralling costs, broken data pipelines, and massive delays.
If you are thinking about making the move, you’re in the right place. This article will break down the top five real-world challenges you will face when migrating to Snowflake, explained in simple, layman Indian English. More importantly, we’ll give you practical, battle-tested advice on how to overcome them, so your migration is a success story, not a cautionary tale.
Challenge 1: It's Not a "Lift and Shift" – You Have to Rethink Your Data's Home (Information Architecture)
The first and most common mistake people make is thinking they can just copy-paste their old data setup into Snowflake. This is what we call a "lift and shift," and it’s a recipe for disaster.
The Problem: Why Copy-Pasting Your Old Setup Fails
Imagine you’ve been living in an old, single-story bungalow for years. Now, you’re moving into a modern, 50-story skyscraper. Would you try to build your old bungalow’s layout on the 30th floor? Of course not. You’d use the new space, the new technology, and the new design to your advantage.
Your old data system (like Teradata, SQL Server, or Netezza) is that bungalow. Snowflake is the skyscraper. They are built on completely different principles. Legacy systems have rigid structures, while Snowflake has a unique, flexible architecture that separates data storage from computing power.
If you simply try to replicate your old schemas, data types, and structures, you will run into huge problems :
Poor Performance: Your queries will be slow because you aren’t using Snowflake’s powerful features, like its multi-cluster architecture, which is designed to handle many tasks at once.
High Costs: You’ll end up paying more because your inefficient design will use more computing resources than necessary.
Wasted Potential: You will be using a Ferrari to drive in city traffic, completely missing out on the speed and power you’re paying for.
The Solution: Plan Before You Build
The key to avoiding this is careful planning. Before you move a single byte of data, you need to design a new blueprint for your data’s new home.
Create a Migration Checklist: The first step is to take a complete inventory of everything you need to move. Make a list of all your tables, views, stored procedures, user permissions, and data pipelines. You can't plan a journey without knowing what you need to pack.
Design for the Cloud: Work with your data architects to design a new information architecture that is built for the cloud. This means thinking about how to structure your data to take advantage of Snowflake’s features, not just replicating what you had before.
Think About Security from Day One: Plan your security and access controls right from the beginning. Decide who needs access to what data and design a role-based access control (RBAC) model that is both secure and easy to manage.
Challenge 2: The Herculean Task of Moving Your Old Data (Historical Data Migration)
Once you have a plan, you face the next giant hurdle: actually moving all your historical data. For many companies, this can mean moving petabytes of data, and it’s often a slow and painful process.
The Problem: It's More Than Just a Data Transfer
Moving massive amounts of data from an old, on-premise system to the cloud is full of technical roadblocks.
Slow Extraction: Getting data out of old legacy systems can be incredibly slow due to their outdated architecture and limited connectivity.
Data Formatting Issues: Snowflake expects data to be in specific formats like CSV or Parquet. Preparing your data in the right format and staging it correctly can be a time-consuming and manual process.
Network Bottlenecks: The physical transfer of huge datasets over the internet can be slow and unreliable, especially if you have a lot of data.
Errors and Failures: During a large data transfer, things will go wrong. Without proper error handling and logging, finding and fixing these issues can be a nightmare.
The Solution: Break It Down and Use the Right Tools
You can’t move a mountain all at once. You have to break it down into smaller, manageable rocks.
Use Snowflake’s Native Tools: Snowflake provides powerful commands like PUT and COPY that are designed for loading large amounts of data efficiently. The COPY command, in particular, can load data in parallel, which significantly speeds up the process.
Optimize Your Files: The size of your data files matters. Don’t try to load one giant file. Break your data into smaller, compressed files. This allows Snowflake to load them in parallel, making the process much faster.
Leverage Automation Tools: Instead of doing everything manually, consider using third-party data integration tools like Fivetran, Matillion, or Qlik Replicate. These tools are designed to handle the complexities of data migration. They can connect to your old systems, automatically format the data, and load it into Snowflake with minimal effort, often with just a few clicks.
Challenge 3: Your Old Code Won't Speak the New Language (Code and Pipeline Conversion)
This is one of the most underestimated and painful parts of any migration. You have thousands, maybe even millions, of lines of code in your old system—data pipelines, reports, and business logic. You can’t just copy this code over to Snowflake and expect it to work.
The Problem: The "Silent Bugs" in Translation
Every database system has its own unique version of the SQL language. Code written for Teradata (TeradataSQL) or SQL Server (T-SQL) will not work in Snowflake without significant changes.
Manual Conversion is a Nightmare: Manually converting all this code is incredibly time-consuming and prone to errors. Some companies have tried using simple find-and-replace scripts, only to find they created more problems than they solved.
"Silent Bugs" Can Kill Your Project: This is the real danger. Some code might seem to work after conversion but will produce the wrong results because of subtle differences between the systems. A famous example is case sensitivity. In SQL Server, 'Apple' is the same as 'apple'. In Snowflake, by default, they are different. This one small difference can lead to tons of silent bugs that are incredibly difficult to find and fix.
Stored Procedures and Triggers Don't Translate: Many legacy systems rely heavily on stored procedures and triggers. Snowflake does not support these in the same way. This logic needs to be completely re-written and re-architected, often using Snowflake Tasks or external orchestration tools.
The Solution: Automate, Refactor, and Test Rigorously
There is no magic wand for code conversion, but a smart approach can save you a lot of pain.
Use Automated Tools (With Caution): There are tools that can automatically convert legacy SQL to Snowflake’s dialect. These can be a good starting point and handle a lot of the basic syntax changes, but they are never 100% perfect.
Don't Just Convert, Refactor: See this as an opportunity to modernize your code. Instead of trying to do a one-to-one conversion of your old, complex stored procedures, consider rebuilding that logic using modern data transformation tools like dbt (Data Build Tool). This will make your new pipelines cleaner, more efficient, and easier to maintain.
Test, Test, and Test Again: Validation is critical. You need to go through the converted code line by line and test it thoroughly to ensure it produces the exact same results as the old system. This is tedious but absolutely necessary to avoid those dreaded silent bugs.
Challenge 4: Flipping Your Brain from ETL to ELT
This challenge is less about technology and more about changing the way your team thinks about data processing. For decades, we’ve been taught to do things one way, and Snowflake asks us to do it completely differently.
The Problem: The Old Habit of Cleaning Data First
Traditionally, we have used a process called ETL (Extract, Transform, Load).Extract: Pull the data from the source system.
Transform: Clean, filter, and reshape the data on a separate server.
Load: Load the clean, transformed data into the data warehouse.
*Snowflake is designed for a different approach called ELT (Extract, Load, Transform). *
Extract: Pull the data from the source system.
Load: Load the raw, untouched data directly into Snowflake.
Transform: Use Snowflake’s powerful compute engine to clean, filter, and reshape the data after it’s already inside the warehouse.
This shift is a major challenge because it requires a complete redesign of your existing data pipelines and a fundamental change in your team's mindset.
The Solution: Embrace the Cloud-Native Way
Switching to ELT is not just a technical change; it’s a strategic one that unlocks the full power of the cloud.
Leverage Snowflake's Power: The whole point of ELT is to use Snowflake’s massive, scalable compute engine to do the heavy lifting of data transformation. This is much more efficient than using a separate, often underpowered, transformation server.
Use Continuous Data Ingestion: Adapt your pipelines to use Snowflake features like Snowpipe, which can automatically and continuously load new data as it arrives. This is perfect for the ELT model, where you want to get raw data into the system as quickly as possible.
Rewrite Transformations in SQL: Your team will need to rewrite the transformation logic using Snowflake’s powerful SQL capabilities. This might take some effort upfront, but it will result in much faster and more scalable data pipelines in the long run.
Challenge 5: How Do You Trust the Data in Your New Home? (Data Validation and Quality)
After all the hard work of planning, moving, and converting, you have one final, crucial challenge: making sure the data in Snowflake is accurate, complete, and trustworthy.
- The Problem: "Garbage In, Garbage Out" on a Supercomputer A migration is a complex process, and it’s easy for errors to creep in. If you don’t meticulously validate your data, you could end up with a very fast, very expensive system that gives you the wrong answers. This is the classic "garbage in, garbage out" problem, but now it's happening on a platform that can cost you a lot of money.
You need to verify everything :
Are the row counts in the new tables the same as the old ones?
Are the data types for each column correct?
Do key metrics like counts, sums, and averages match between the old and new systems?
Furthermore, if your data quality was poor in the source system, simply moving it to Snowflake won't magically fix it. In fact, it can make the problems even more obvious.
The Solution: A Three-Point Check Before, During, and After
Ensuring data trust requires a disciplined approach to validation at every stage of the migration.
Before You Migrate (Know What's Good): Before you move anything, analyze your source data. Understand its structure, identify quality issues, and establish a baseline for what the correct data should look like. You need a "single source of truth" to compare against.
During the Migration (Check the Transfer): As you move the data, use simple checks like row counts and checksums to ensure that nothing was lost or corrupted during the transfer.
After the Migration (Reconcile and Monitor): This is the most important step. Once the data is in Snowflake, run detailed reconciliation reports that compare the data in Snowflake against your legacy system to find any discrepancies. After you go live, implement automated data quality checks within Snowflake to continuously monitor the health of your data and catch any issues before they impact your business users.
Final Words: A Tough Climb, But a Great View from the Top
Migrating to Snowflake is a major undertaking, and it’s filled with challenges that can trip up even the most experienced data teams. From rethinking your entire data architecture and wrestling with code conversion to the fundamental mindset shift from ETL to ELT, the path is anything but simple.
However, these challenges are not insurmountable. By understanding the pitfalls ahead of time and approaching the migration with a clear, strategic plan, you can navigate the complexities successfully. The key is to remember that this is not just a technical project; it's a strategic business initiative. Careful planning, rigorous testing, and a willingness to embrace new, cloud-native ways of working are essential.
The journey might be tough, but for companies that get it right, the rewards are immense. A successful migration to Snowflake can truly transform your business, unlocking the speed, scalability, and powerful insights you need to win in today’s data-driven world.