Today, data is the fuel that powers the modern world, underpinning almost every product and service we take advantage of. We generate mammoth amounts of data just by going about our day-to-day lives—and hidden within even the most seemingly innocuous data are rich, valuable insights that can help organizations make smarter decisions.
The challenge for businesses lies in how to store, process, and analyze this vast quantity of information effectively to turn raw data into actionable insights.
That’s where products like Snowflake come in. Snowflake is a cloud-based, scalable data warehouse designed for data storage, processing, and analytics.
As a fully managed SaaS data cloud platform, Snowflake removes the need to set up and maintain separate data marts, data lakes, and data warehouses and provides users with secure data-sharing capabilities. With Snowflake, companies don’t need to install, configure, or maintain any software or hardware—everything is ready to use immediately.
There are several reasons why Snowflake is rising to the top of the data solution market, not least its powerful data-sharing capabilities. It also has a unique architecture that separates storage and compute, which allows businesses to scale their data operations. Then there’s its handy consumption-based pricing model based on customer usage, making it accessible to businesses of all sizes. And because Snowflake supports multiple cloud environments, it’s a popular choice for companies using a wide variety of cloud platforms like AWS and Microsoft Azure.
This combination of benefits has seen it achieve a 29% year-over-year growth in product revenue and earn DB-Engines’ DBMS of the Year title no less than three times.
Why implement a data warehouse?
It’s cloud-based:
It’s scalable:
It’s high performing:
It’s secure:
It’s cost-effective:
With the advantages that Snowflake offers, the platform can deliver a significant boost to your organization’s data strategy, helping to move you closer to your business goals.
The core benefit that Snowflake provides its customers is the ability to gain deeper, more accurate, and more useful insights from their data through the platform’s powerful analytics capabilities.
Snowflake’s ease of use, scalability, and automated features can also help improve efficiency, freeing up your IT staff to focus on other strategic initiatives and allowing your business to quickly adapt to changing business requirements and market conditions.
The cloud-based architecture that underpins Snowflake, along with its pay-as-you-go pricing model, can help reduce your overall data warehousing costs too. This cost-saving potential also extends to your governance and data security, with the platform’s features helping to ensure data quality, compliance, and privacy—while avoiding the costly fines and reputational damage that can come with poor data handling practices.
Getting started with a Snowflake data warehouse
If you’re thinking about implementing Snowflake, there are some key steps you can take before, during, and after the deployment that will put you in the best possible position to achieve your organization’s data goals.
Let’s take a look at some key actions and best practices to ensure a successful implementation.
Step 1: Define your business objectives
Firstly, you need to outline your specific data warehousing needs, including the data sources you need to integrate, the types of analysis you want to perform, and your performance and scalability requirements.
Think about what you want from Snowflake. Is it faster analytics? A centralized data repository for cross-department reporting? Cost optimization of your data infrastructure? Conduct stakeholder interviews across relevant departments (like finance, sales, marketing, and IT) to gather information about requirements.
Having a definitive plan of what you want from the platform will help when it comes to purchasing the right version, setting up your solution, and tracking performance over time.
Key steps:
- Identify business use cases for Snowflake; these could cover areas like reporting, analytics, AI/ML, and real-time data processing
- Determine your data goals and create a realistic timeline for achieving them with Snowflake
- Outline the key metrics you’ll use to track progress, e.g. reports should run 3x faster, cloud storage costs should be cut by 30%, or customer data should be updated in real time
Step 2: Assess your existing data infrastructure
Next, you should thoroughly assess your existing data infrastructure to see how and where Snowflake will fit in. This will mean documenting current systems, data flow, storage, and security measures, as well as evaluating the kind of data quality, volume, and accessibility you’re working with.
Identify any bottlenecks, redundancies, scalability issues, and potential integration challenges so you’re well aware of everything that needs to be addressed with your Snowflake implementation (and anything that could create an obstacle).
Key steps:
- Make an inventory of all your data sources (like ERP, CRM, and other databases)
- Identify the data formats you use (structured/unstructured, JSON, Parquet, CSV, for example)
- Create a Data Flow Diagram to visualize how data moves in your organization
Step 3: Choose the right Snowflake edition
There are several versions of Snowflake to choose from, and selecting the right one depends largely on your organization’s needs. Start by evaluating features like Time Travel duration, data-sharing capabilities, and advanced security options. Consider your workload demands and the necessary compute resources (virtual warehouses), estimate your storage requirements, and total up the cost implications of each edition.
For smaller organizations with simpler needs, Standard or Enterprise editions might be suitable enough. Larger enterprises with demanding workloads and advanced requirements should consider Business Critical or higher editions.
You should also try and use a version of Snowflake that’s aligned with your cloud provider to minimize data transfer costs. If you’re on Azure, for example, use Snowflake on Azure.
Now is a good time to sign up for a free Snowflake trial to test features and get familiar with the platform.
Snowflake editions:
- Standard: Good for basic analytics
- Enterprise: Best if you need failover capabilities
- Business Critical: Designed for highly regulated industries
- VPS: Offers extra security and isolation
Step 4: Design your data architecture
Planning data organization in Snowflake involves defining a logical structure to make sure data querying and management are as efficient as possible.
Start by identifying key data domains and creating corresponding databases, and then use schemas to further categorize data by function or department within your databases. Choose appropriate table types (e.g., regular, external, transient) based on data characteristics and access patterns. Consider using clustering keys for frequently queried columns to improve performance and establish a naming convention for databases, schemas, and tables for consistency. Finally, make sure you document your data organization strategy for future reference.
Key steps:
- Define what data will be stored (structured, semi-structured, or unstructured)
- Outline schemas and tables based on business needs (Star Schema vs. Snowflake Schema)
- Set up database, schema, and table structures
- Plan for multi-cluster virtual warehouses to support workload separation and scale compute separately from storage, avoiding bottlenecks
- Consider using Time Travel and Fail-Safe features to protect against accidental data loss
Step 5: Migrate your data
Migrating to Snowflake involves extracting data from source systems, transforming it into a compatible format, and loading it into Snowflake. Luckily, Snowflake offers data loading tools like Snowpipe or the COPY command to help move your data from your existing systems into Snowflake. Remember to validate data integrity post-migration and optimize queries for performance.
Key steps:
- Determine how data will be ingested (consider ETL/ELT tools like Fivetran, Matillion, or dbt)
- Define batch vs. streaming ingestion (you can use Kafka for real-time data)
- Connect Snowflake to data lakes, databases, and APIs and optimize ingestion using Snowpipe for real-time data loading
Step 6: Set up and launch your Snowflake environment
If you’ve already set up a Snowflake free trial account, it’s time to upgrade and get all your configurations and settings straightened out. You’ll need to construct virtual warehouses for compute resources, set up security features like network policies and access controls, and integrate with existing tools.
Key steps:
- Set up user roles and access controls (RBAC)
- Configure storage, compute resources, and auto-scaling
- Load a test dataset and execute sample queries to evaluate performance
- Integrate with BI tools (you might use Power BI, Tableau, or Looker) and build dashboards, reports, and other necessary visualizations
Step 7: Develop your governance and compliance policies
To make sure your Snowflake solution remains secure and compliant with all relevant data handling and privacy regulations well into the future, you should establish strict governance rules and best practices from day one.
Establish a data governance framework defining data ownership, access controls, and data quality standards, alongside robust security measures, including role-based access control, network policies, and encryption, to ensure compliance with regulations like GDPR, HIPAA, or CCPA. Data retention policies and data masking or anonymization techniques can also be implemented as needed to meet required handling rules.
For auditing purposes, data lineage tracking should be set up to help with accountability and compliance. Be sure to document all policies and procedures and regularly review and update them to make sure you’re keeping up with the ever-evolving regulatory landscape.
Key steps:
- Enable data masking to protect sensitive data
- Set up audit logging for compliance
- Ensure SOC 2, GDPR, or HIPAA compliance based on your industry
Finally, the most important step of all: Get the right talent on board
One of the final steps on your Snowflake implementation journey is also one of the most important ones: hiring the right people.
Although you can get started using Snowflake with basic knowledge of SQL, database concepts, and some familiarity with CSV files and JSON semi-structured data, to really make the most of the platform, you need a Snowflake Data Engineer.
Hiring a Snowflake Data Engineer is a critical step for any organization wanting to successfully implement and maintain a Snowflake data warehouse. Their specialized expertise means that your organization can maximize performance, provide ongoing support and maintenance, and uphold proper data security.
A Snowflake expert can help you design and implement a solution that meets your specific needs and takes full advantage of all that Snowflake has to offer, from data modeling and performance tuning to cost optimization.
A good Snowflake Data Engineer will be able to help you create stages, databases, tables, views, and virtual warehouses. They’ll know how to load structured and semi-structured data and how to consume Cybersyn data from the Snowflake Data Marketplace. They’ll be able to perform analytical queries on data in Snowflake, including joins between tables, cloning objects, and undoing user errors using Time Travel.
Snowflake Data Engineers know how to keep your solution and its data safe by creating roles and users, granting user privileges, and securely sharing data between platforms and accounts. Your Snowflake Data Engineer will help you monitor, optimize, and scale your Snowflake data warehouse, as well as manage ongoing costs, integrate other services and solutions from Snowflake’s Data Marketplace, and create new use cases across the organization.
Adding someone to your team who can help you get the most out of Snowflake is a smart investment and will make sure you’re getting the most out of your new data warehousing solution.
Get cost-effective Snowflake talent solutions with Revolent
As a member of the Snowflake Partner Network, we can help you access the talent you need while reducing your recruitment, onboarding, and training costs.
At Revolent, we hire experienced IT professionals and put them through our intensive training that transforms them into certified, delivery-ready Snowflake Data Engineers. Our training modules include data ingestion, orchestration, governance, and modeling with additional training on data engineer patterns, scripting, visualizations, Tableau, Fivetran, Airflow, and Airbyte.
They’re then deployed to your organization, where they’ll be ready to make an impact from day one. Plus, we offer ongoing support and development post-deployment to ensure our Data Engineers can continue to grow their skills. At the end of their placement, these valuable Data Engineers can transfer to your team permanently at no extra cost to you.
