Share this on:
What You'll Learn
This is a one-stop guide to gain an in-depth understanding of Snowflake migration, why it’s important, best steps to migrate from legacy to Snowflake, and everything else. Not to miss is the LumenData advantage section that talks about our key capabilities that help customers migrate to Snowflake in record time.
What is Snowflake?
Snowflake is a leading AI Data Cloud Company that offers cloud-based data warehouse solutions for data storage and analytics. The company was founded by Benoît Dageville, Thierry Cruanesin in 2012. Snowflake is built on three leading cloud platforms – Amazon Web Services, Google Cloud Platform, and Microsoft Azure.
Be it data warehousing, data lakes, data science, data engineering, or secure data sharing – Snowflake provides it all. What Snowflake provides is a serverless offering.
It means that you don’t need to install, configure, or manage any kind of physical or virtual hardware or software. Snowflake has around 10,249 global customers, including Canva, Honeywell, Open AI, and several large enterprises. The customers have more than 5.3 billion average daily queries on the data cloud. Currently, the platform has 2,803 marketplace listings where you can find and buy ready-to-use data and applications from the Snowflake Marketplace.
A Quick Overview of the Snowflake Architecture
Snowflake’s differentiated architecture provides you with the best of both worlds – shared-disk and shared-nothing database architecture. Let’s first understand what these architecture types mean:
Shared Disk Architecture
One central storage layer that can be accessed by all cluster nodes. Multiple cluster nodes that have CPU and Memory, but no disk storage can communicate to a central storage to access data for processing.
Shared Nothing Architecture
It is a distributed computing architecture. Each node or server is independent. It has its own disk storage, CPU, and Memory. All data is partitioned and stored locally.
Snowflake combines these two architectures to form a new hybrid architecture. Like shared-disk architecture, it leverages a central repository that’s accessible from all compute nodes. And like a shared-nothing architecture, it processes queries using MPP (Massively Parallel Processing) compute clusters.
Each node in the cluster stores one part of the data set locally. This hybrid architecture by Snowflake enables users to simplify data management via shared-disk capabilities and enhance performance with shared-nothing architecture.
Understanding Snowflake Architecture Layers
This section will provide you with a quick overview of what makes the Snowflake platform – its main components.
Image Credit: Snowflake
Database Storage
This is the layer where all the data gets stored in Snowflake. All operations related to secure and elastic data storage are done here. Snowflake’s storage layer is elastic in the sense that you have to pay monthly based on your consumption.
When your data is loaded into Snowflake, it is optimized, compressed, and reorganized into its columnar format. As a customer, you cannot directly access the data objects stored by Snowflake. You can access them via SQL queries operations run using Snowflake.
Query Processing
This is the middle layer of the Snowflake architecture where queries are executed using virtual warehouses. The query processing layer enables users to analyze data through requests. Each virtual warehouse consists of compute resources like CPU, memory, and temporary storage. One thing to note here is that every virtual warehouse is an independent compute cluster.
It does not interact or share resources with other virtual warehouses. If you’re wondering about the advantages of these virtual warehouses, there is more than one benefit. These virtual warehouses can be scaled anytime without impacting running queries. You can set the virtual warehouses to auto-scale with minimum and maximum cluster size.
Cloud Services
This is the outermost layer of the Snowflake architecture. With the support of ANSI SQL, Snowflake data cloud platform enables you to optimize your data and maintain your data infrastructure. The cloud services layer includes metadata management, query parsing and optimization, and data security and governance. Some of the services handled in this layer are:
- Login request
- Query submission to Snowflake
- Storage of metadata required for query optimization and data filtering
What is Snowflake Migration?
When you transfer your data, schemas, and various other database objects from your existing data warehouse to Snowflake – it’s called Snowflake migration. Whether you want to move from on-premises, legacy systems or between clouds – Snowflake is your go-to platform and enables you to experience a seamless data transformation journey.
Legacy databases have a lot of limitations like slow queries, scalability issues, high licensing costs, and many others. If your current database warehouse is not scalable, provides slow analytical performance, does not offer quick data sharing, or charges you more than what you need – now is the time to switch to a modern cloud alternative like Snowflake.
In case you want to migrate to Snowflake on AWS, you could use Snowflake’s modeling tool developed in cooperation with AWS to calculate your Snowflake migration ROI. With this tool, you can calculate:
- Return on Investment
- Total Cost of Ownership
- Payback Period
- Net Present Value of the Project
Why Snowflake Migration
The Snowflake AI Data Cloud is a single platform that helps eliminate data silos and simplify data architecture. It provides you with several highly beneficial capabilities such as interoperable storage, elastic compute, Cortex AI, built-in governance solution, Snowflake Marketplace to discover and access data and apps critical to your organization, and many more.
Here’s why Snowflake is special:
- Fully managed software-as-a-service platform that offers a pay-as-you-go pricing model.
- Works with structured, semi-structured, and unstructured data.
- Separate storage and computers with flexible scaling options.
- Multiple options for connecting to Snowflake like web-based UI, native connectors, third-party connectors, ODBC and JDBC drivers, & more.
- Zero-copy cloning capability that allows users to create quick copies of their data without duplicating storage.
- Multi-factor authentication, federated authentication and SSO, and many other enhanced authentication features.
- APIs for Java, Python, Scala to build applications that process data in Snowflake without moving it.
- Solid ecosystem of third-party partners and technologies.
There are many benefits of prioritizing Snowflake migration. Here are our top 5 reasons to migrate to Snowflake from legacy systems:
- Storage for all data types
- Quick, real-time analytics
- Near-zero maintenance
- Consumption-based pricing
- Enhanced data security
Snowflake Migration Approach
Before you get started with your migration to Snowflake, it is important to understand which migration strategies are available and which one you could leverage for your organization.
Three Snowflake Migration Approaches That You Could Consider for Your Business:
Lift and Shift
You move your existing data and applications to Snowflake without making any changes to your current data architecture. It is relatively simple and quick and capable of handling large data sets.
Lift, Fix, & Land
You optimize your applications and processes before shifting to the Snowflake AI Data Cloud.
This way you enable enhanced data quality and optimized utilization of the Snowflake platform.
Complete Redesign
Your data platform and applications go through complete re-architecting to align with Snowflake’s capabilities. Starting over helps you maximize the benefits of Snowflake’s cloud platform.
Choosing the right migration approach for your business is quite simple. You need to have a strong understanding of your data ecosystem. Engage with stakeholders and application owners to understand why your business needs this data warehouse migration, how much raw data there is, and data types that could be removed to reduce the load.
Snowflake Migration Key Steps
Step 1
Assess your current data architecture. Things to take note of include data volumes, data types, ETL processes.
Step 2
Plan and finalize your migration strategy and approach – whether you want to implement lift and shift, redesign, etc.
Step 3
Get started with your Snowflake environment setup. Configure virtual warehouses, databases, security settings.
Step 4
Recreate database schemas and objects in Snowflake.
Step 5
Load data into Snowflake by leveraging Snowflake’s native tools like Snowpipe or third-party tools like Azure Data Factory.
Step 6
Validate the migration you performed. You could create test case scenarios to cover different queries and workloads.
Step 7
Work on optimization, review performance settings such as warehouse sizes, clustering keys, etc.
Step 8
Move from your old database system to the new Snowflake setup.
Step 9
This is an ongoing step. Here you monitor the system’s performance and make adjustments as needed.
Snowflake Migration Best Practices
Migrating to Snowflake is not rocket science and doesn’t have to be complicated. Here are some best practices to follow:
#1 Understand Your Data
Review your data and check if it has any inconsistencies. Arrange your data based on importance and analyze your data relationships.
#2 Build Your Migration Inventory
Identify your data sources and who their owners are. Figure out your database objects, ingestion pipelines, and data transformations.
#3 Craft Your Project Framework
Define user roles, schemas, and databases. Once you are into the Snowflake environment, create an access role for every data object and virtual warehouse.
#4 Use Modern Migration Tools
Consider using third-party ETL/ELT tools like Informatica, Fivetran, and other leading data movement platforms that automate the migration process.
#5 Connect with a Technology Implementation Provider
Choose a Snowflake migration services partner like LumenData. Our SaaS extensions reduce Snowflake deployment time by 2 months with 95% risk mitigation.
#6 Always Validate Your Migration
Validate and test the data you have migrated to Snowflake. Test Snowflake functionalities like query performance, data transformations, and more.
#7 Perform Post-Migration Optimization
Continuously fine-tune workloads and configurations after your Snowflake migration. You could use Snowflake’s resource monitors to control usage.
Best Practices for Oracle to Snowflake Migration
- Create a detailed migration plan for migrating to Snowflake from Oracle. It should have details like migration timelines and resource allocation.
- Assess your Oracle environment to define clear objectives for migrating to Snowflake.
- Use third-party ETL/ELT tools like Informatica, Fivetran, dbt for data extraction and loading.
- Leverage migration methods like direct database links or batch data exports.
- Convert Oracle PL/SQL to Snowflake SQL.
Best Practices for Teradata to Snowflake Migration
- Assess your existing Teradata environment to define clear goals of migrating to Snowflake.
- Redesign Teradata queries to align with Snowflake’s architecture and optimize performance.
- Leverage Teradata Parallel Transporter for data extraction and loading. You could also use third-party ETL tools that can support your Snowflake set up.
- Consider using the phased approach or direct data movement method for migration.
- Convert Teradata SQL script to Snowflake SQL.
Best Practices for Hadoop to Snowflake migration
- Assess existing Hadoop workloads to define clear objectives for migrating to Snowflake.
- Focus on data partitioning and clustering in Snowflake.
- If you have files like Parquet, Avro, or ORC – perform data transformation before migrating to Snowflake for optimized performance.
- Leverage Apache Sqoop for data extraction and loading.
- Use data migration tools like Informatica or Fivetran for seamless data movement.
Snowflake Migration Services with LumenData
LumenData is a proud Premier Snowflake AI Data Cloud Services partner. Our Snowflake migration services have enabled several large customers to modernize data warehousing, migrate their data to the cloud and grow their instances. If you want customizable offerings for data strategy, data warehouse modernization, data science, data visualization & graph analytics, AI/ML and managed services – we can be your all-in-one Snowflake migration partner.
Why LumenData for Snowflake
Expertise in Snowpipe for data ingestion, Snowpark for data transformation & ML, Snowflake Streams, data masking, Streamlit, & more.
75+ Snowflake certifications – SnowPro Advanced, Advanced Architect, Advanced Data Engineer, Technical Sales Pro.
Experience in deploying Snowflake + Informatica, Snowflake + Fivetran, and more.
Best-of-breed SaaS extensions for data governance, data warehouse modernization.
6–12-week QuickStart program tailored for industries like healthcare, financial services, retail, high-tech, manufacturing, and more.
Snowflake Migration by LumenData – Customer Success Story
LumenData has great experience in navigating complex data transformations for businesses looking to migrate to the Snowflake platform. We recently helped a globally renowned pharmaceutical company, in seamlessly migrating from a hyperscaler warehouse to Snowflake, using the Informatica Data Management Cloud platform.
"We focused on optimizing how each of our partner products worked collectively as one integrated solution in migrating our client from a hyperscaler warehouse to Snowflake"
Find the customer story here.
If you are already using Snowflake for your business and want to make the most of the platform – LumenData is here to assist. We have developed a modular alerting mechanism designed for Snowflake environments that can help you optimize performance and security. Read more about it here.
About LumenData:
LumenData is a leading provider of Enterprise Data Management, Cloud and Analytics solutions and helps businesses handle data silos, discover their potential, and prepare for end-to-end digital transformation. Founded in 2008, the company is headquartered in Santa Clara, California, with locations in India.
With 150+ Technical and Functional Consultants, LumenData forms strong client partnerships to drive high-quality outcomes. Their work across multiple industries and with prestigious clients like Versant Health, Boston Consulting Group, FDA, Department of Labor, Kroger, Nissan, Autodesk, Bayer, Bausch & Lomb, Citibank, Credit Suisse, Cummins, Gilead, HP, Nintendo, PC Connection, Starbucks, University of Colorado, Weight Watchers, KAO, HealthEdge, Amylyx, Brinks, Clara Analytics, and Royal Caribbean Group, speaks to their capabilities.
For media inquiries, please contact: marketing@lumendata.com.
Authors
Content Writer
Senior Consultant
