Migrating On-Premises Data Warehouses to Snowflake Data Warehouse: Challenges and Benefits

Vikas Agarwal

Data Warehouse

Today, we are in the age of digital transformation and rapid advancements in technology. Businesses are now becoming aware of the necessity to go through on-premises data warehousing to cloud-based solutions that can be used to meet the demands of modern analytics and data processing.

There are a number of drawbacks to employing traditional data warehouses, such as on-premises data warehouses, including restricted access, high maintenance costs, lack of flexibility, and inability to scale efficiently. Due to these difficulties, substitutes had to be created that could both mitigate the hazards and satisfy the requirements of modern analytics & data processing.

Snowflake is a cloud-based data warehouse that has gained many users, as it gives the opportunity to benefit from the features like scalability, flexibility, and high performance offered in cloud computing. Nevertheless, each step of this migration process has both advantages and drawbacks

What is a Data Warehouse?

A data warehouse is a sizable central repository designed to hold vast amounts of diverse data. It stores information from several sources to support analytics, reporting, and BI. You can utilize the data you keep in the data warehouse for reporting and data analysis.

How is the Data Warehouse Architected?

A data warehouse architecture consists of 3 tiers:

Top Tier
The top-tier front-end client uses data mining, reporting, and analysis capabilities to present results.
Middle Tier
A middle-tier analytic engine is utilized to retrieve and analyze the data.
Bottom Tier
Data is loaded and stored on the database server, which is the lowest tier. Two methods are used to store data in the bottom tier:

SSD drivers are examples of extremely fast storage that holds data that is accessed often.

Data that is accessed infrequently is kept in an inexpensive object store, such as Amazon S3.

What is an Open-Premises Data Warehouse?

On-premise software and hardware need a significant upfront investment in software licensing and hardware, and its management and operation require specialized personnel and access to your local network.

What is a Snowflake Data Warehouse?

Snowflake is a cloud-based data warehouse that operates on either Amazon Web Services or Microsoft Azure. It’s ideal for businesses that don’t want to spend resources on the setup, maintenance, and support of their own servers because there is no hardware or software to select, install, configure, or administer.

Snowflake distinguishes itself through its design and data interchange capabilities. Customers can use and pay for storage and computing separately, thanks to the Snowflake design, which allows storage and computation to scale independently. Furthermore, the sharing capability allows businesses to rapidly share restricted and secure data in real-time.

How Does Snowflake Data Warehouse Work?

Snowflake has a lot of unique features, but one of the most striking is its capacity to construct an infinite number of virtual warehouses (each effectively its own MPP cluster).

Additionally, each warehouse may be grown in milliseconds from a single-node extra-small cluster to a massive 128-node monster. Users won’t have to settle for less-than-optimal performance because the machine’s capacity can be adjusted throughout the day to accommodate changing workloads. Finally, besides scaling up for higher data volumes, it can also automatically scale out to accommodate enormous numbers of users.

Migrating On-Premises Data Warehouses to Snowflake Data Warehouse: Best Practices

Now, before we dive into the actual benefits and challenges, let’s look at the best practices of migrating on-premises data warehouse to snowflake data warehouse.

The migration of on-premises data warehouses to the Data Cloud of Snowflake turns out to be a strategic move that is ready for scale-up, cost optimization, increased performance, ease of use, and advanced security benefits. Yet, the migration process is all about the strategic allocation of resources to guarantee that the transfer will be carried out within a reasonable time and with minimal drawbacks. Here are some best practices to consider when migrating to Snowflake. Here are some best practices to consider when migrating to Snowflake:

Review Your Current Data Infrastructure Model
Review your current data architecture, database, and data sources to determine the degree of compatibility with Snowflake. Ascertain which data sources shall be transmitted and look for potential problems during the migration process.
Choose the Correct Snowflake Edition
Choose a Snowflake version with better performance, larger reservations, or a virtual data warehouse that best fits your organization’s demand and preferences. Data storage, security, and access are some factors you should take into account to make your choices conform to your business goals.
Develop a Comprehensive Migration Plan and Timeline
Create a detailed migration plan that outlines the steps and timeline for completion. Tasks such as data extraction, transformation, loading, testing, and validation of the new infrastructure should be included in the plan.

Through the application of these best practices, organizations can ensure a smooth migration to Snowflake, capitalizing on its power without suffering any disruption of operations.

Benefits of Migrating On-Premises Data Warehouses to Snowflake

Scalability and Flexibility
Snowflake’s cloud-based solution is characterized by unbounded scalability as it empowers the organization to quickly address data growth requirements without suffering any performance degradation and extra expenditures. In the same way, Snowflake’s capacity to manage data that is both structured and semi-structured brings an element of flexibility that helps in handling different data types, promoting more in-depth analytics and understanding.
Enhanced Performance and Speed
Snowflake’s own innovative architecture, which separates data storage and processing, creates such high performance and speed, being the best option for those more demanding analytical tasks. The flexibility of the platform to handle complex queries and rich datasets becomes a catalyst for quick results and on-time insight that is invaluable.
Improved Security and Reliability
Snowflake includes advanced security that utilizes encryption for data at rest, thus providing increased safeguarding for private information. Through using Snowflake technology security properties, organizations increase data honesty, ensure adherence to regulative requirements, and decrease data theft risk.
Cost Efficiency
The pay-per-use model and database resource allocation supplied by Snowflake build in cost optimization when organizations are leveraging the advanced technology data warehouse. This technique makes businesses expand only when it is necessary so that they can make a rightful decision concerning their infrastructure capacity expansion. This can result in a company achieving better savings on investment in its data infrastructure
Streamlined Maintenance and Managemen
By means of Managed Service from Snowflake, IT teams are now relieved of boring and routine infrastructure management tasks and have a chance to shift focus onto strategic growing points. This transition towards a slimmer and smarter, but more effective and innovative management model liberates organizations for better focus and higher productivity levels through targeted resource allocation.
Advanced Analytics Capabilities
With the native facility of advanced analytics and the machine learning implementation provided by Snowflake, organizations can now derive deep insights and get everyone involved in making key business decisions. Through Snowflake’s powerful analytics tool, company leaders can gain new insights while on their way to tapping the full potential of big data and ultimately controlling the competition.

Challenges of Migrating On-Premises Data Warehouses to Snowflake

Data Migration Complexity
Data migration and transformation from on-site storage to the Snowflake clouds requires vigorous preparation and implementation exercises to maintain the intactness of data veracity and completeness. The full complexity of this process extends more to ETL and the highest level of validation to minimize the danger of any data corruption or loss.
Integration with Existing Tools and Processes
The Snowflake services may require upgrades of the currently existing BI tools along with ETL pipelines as well as migration of some applications; the main obstacle here will be to ensure that the services are compatible and efficient enough. Seamless integration is the main concern and requests to continue the same way of data processing and analytics post-migration will be required.
Performance Optimization
New workflows and query optimization and the development of new requirements for Snowflake’s architecture and practices in the new cloud environment should have a complete understanding. The institution has to put up a good effort in developing performance tuning and optimizations in order to make the shift to Snowflake not obstructed by operational setbacks.
Data Governance and Compliance
Implementation of these guidelines should be monitored at every stage of data migration, including as part of the migration itself and even after the migration is complete, it is critical to protect valuable data. Organizations need to look out for security loopholes and vulnerabilities with the view of reducing data misuse and trust gaps.
Change Management and Training
Ensuring that the change is suitable for all the employees will be conducted by giving them complete training about the new workflows, tools, and techniques which also involves a comprehensive change management system. The realization of the communication and training programs that will define data management is a key aspect toward which adequate personnel are prepared to conduct work in a new data environment.
Downtime and Migration Strategy
The migration process must be planned with a reduction in the number of downtimes and the development of a migration strategy that can help the businesses maintain their continuation. A very well-developed plan containing contingency measures to be implemented during migration is of significant importance to eliminate or at least reduce the negative impact already done.
Unforeseen Technical Hurdles
Each migration project encountered has crafted problematic hurdles, for which diligent change of circumstances should be done to illustrate unforeseen complications. Systems must be ready with the appropriate fixes to technical problems that may crop up during the migration process.

Conclusion

The choice to replace on-premise data warehouses with Snowflake reflects a key step in the company’s modernization move and confidence belief in the power of cloud data warehousing. However, the advantages of this change are big and cannot be ignored. The fact that data migration, integration, performance optimization, and transition management need careful evaluation and an effective way of managing them is vivid.

Recognizing the impact of these migration elements and actively taking countermeasures allows organizations to streamline the migration process successfully and enjoy all the benefits of modern data warehousing, that is,innovation, agility, and competitive advantage. By involving proper planning along with robust execution and putting great emphasis on overcoming the challenges businesses of the snowflake can be the beneficiary of flaking to increase the cloud-based data management and analytics capabilities.

Vikas Agarwal

Vikas Agarwal is the Founder of GrowExx, a Digital Product Development Company specializing in Product Engineering, Data Engineering, Business Intelligence, Web and Mobile Applications. His expertise lies in Technology Innovation, Product Management, Building & nurturing strong and self-managed high-performing Agile teams.

Migrating On-Premises Data Warehouses to Snowflake Data Warehouse: Challenges and Benefits