Transform Your Data Foundation with Snowflake Migration

Introduction

Data, as a live repository of actions taken and targets achieved, is a competitive asset that is unique to an organization. As enterprises respond to geopolitical unpredictability with a greater focus on security, scalability, and agility, it is natural that they are moving this critical asset to the cloud as part of a strategic realignment.

To gain a competitive edge, data must reside on an advanced cloud platform that empowers business users. This is the core objective of data migration.

Source: Statista, Flexera

What is Data Migration?

Organizations are migrating their data to the cloud in line with a larger strategic shift. They seek sophisticated technology platforms to help unify structured and unstructured data, spread it across locations and clouds, and harness it within minutes to make decisions and serve customers.

As with any shift, data migration to the cloud has its challenges. Companies across verticals face common obstacles when moving data to the cloud. For many, decades' worth of data stored in traditional, appliance-based warehouses lack the flexibility and agility an AI-led digital world demands. However, additional obstacles compound these issues. Legacy systems, often built by teams no longer in their original roles, frequently suffer from insufficient documentation detailing data flows, storage protocols, and embedded business logic. Faced with this lack of clarity, data teams grapple with

Faced with this lack of clarity, data teams grapple with:

• Uncertainty about Impact: Fear of unknown consequences of migration on critical data.
• Decisions on Scope: Determining partial vs. complete migration.
• Business Implications: Concerns about adverse operational effects during or after migration.

These factors hold companies back despite clearly recognizing the advantages of moving data to the cloud.

Four Steps for Best-in-Class Data Migration

Data engineering teams can adopt a structured, strategic approach to overcome these hurdles. Here’s what to prioritize for a smooth and impactful migration from legacy systems to modern platforms.

1. Analysis and Discovery

Start with a thorough analysis of your legacy environment. Use visualization to map out data dependencies, lineage, and traceability, identifying key objects you want to prioritize for migration. Typically, 70-75% of the data objects drive most of the value, and focusing on these ensures efficient migration.

In addition to data dependencies, visualization helps your teams understand the current scenario of workflows and code bases. This complete picture will create a clear understanding among stakeholders of the strengths and downsides of the existing data landscape, streamlining their decision-making for an optimized migration strategy.

This initial analysis also needs to estimate the migration exercise's ROI, accounting for further downstream variables.

For example, legacy and modern environments usually run parallel during the final handover. Depending on the complexity of your systems, this overlap may last several weeks to months to ensure any gaps are addressed before fully decommissioning the old systems. This parallel approach minimizes risks and avoids disruptions. It is important to estimate this overlap period with reasonable accuracy. Migrating from legacy platforms to cloud platforms will mean ending expensive contracts for the former. Even a few weeks could be the difference between renewing or cancelling a years-long contract with your legacy provider.

Other variables include faster compute times, easier scaling, and training and maintenance costs. Approximating these accurately at the outset wins buy-in across the organization for the exercise.

With a time-bound, clearly defined strategy for cloud data migration, you are ready for the migration journey that typically extends from six months to a year. It comprises three steps.

2. Data Migration

You have already identified which data objects to retain and the target cloud Data Lake House for migration. To ensure continuity, historical data must be carefully transferred into the cloud warehouse, while incremental data entering legacy systems should be synchronized with minimum lag. Cloud Data Lake House platform enables scaling of both structured and unstructured data into unified architecture that delivers last mile business insights using AI and BI workload for various personas in the organization.

Why is this important?

Users need to see the value quickly—whether through real-time insights or seamless access to updated datasets.

Use streaming, micro-batching, or scheduled loads to ingest incremental data into the cloud. Tailor your approach to historical and incremental data migrating, avoiding a one-size-fits-all approach.

When you are sure you have a representative picture of the final system, share previews with users. This will accustom them to the upcoming change, paving the way for easier adoption.

3. Process Migration

This step tends to be the most complex of your data migration. It involves modernizing your existing processes and business logic, often embedded in your legacy technology stack. These include refurbishing code bases and activities carried out by legacy ETL (Extract, Transform, Load) and reporting tools. The objective is to adapt these processes to function efficiently and as expected in the new cloud environment.

Originally designed for the legacy environment where the infrastructure was already paid for, the run time of these processes did not impact costs significantly. However, this must be now optimized for the cloud where you are charged for compute power and time. This may involve a shift to cloud-friendly data extraction tools like Fivetran, OpenFlow (Snowflake new data integration tool in private preview) and DBT for data transformation.

For reporting, you may continue with the existing tools or move to new reporting tools for optimization on the cloud. In either instance, the generated reports may not be of the desired format and frequency due to hidden causes during the migration or in the new environment. It is imperative to identify these deviations and fix them.

Therefore, process migration is a highly intricate step with multiple layers of complexity that require detailed design, execution, and often, automation to ensure everything performs well in the cloud environment.

It can be broken down into:

• Convert and build: Where you decide on new tools and optimizations and set them up.
• Retrofit and optimize: Where you ensure there are no surprises on the cloud with the new tools and optimizations.

While you will have your strategy roadmap to refer to, you can expect to make adaptations in live situations - a factor of relevance right until the handover and even beyond.

4. Data Validation

This is the last, but the most critical step of your migration journey. Now that you have migrated the data and related processes to the cloud, you must hand the system over to your business users. While doing this, you must attend to validation. This involves verifying that the reports and data are correct in the cloud.

Accuracy would have been monitored throughout the migration, but the final lap with all the objects and reports is the most crucial. Here is one example of why validation is key. In your legacy environment, you might have been processing data on a nightly basis, so the data would typically be one day behind your ERP systems. But with the modernization, you could now be pulling data much faster, possibly in real time or several times a day. When comparing the new data and reports in your cloud environment to the legacy data, you may notice discrepancies. These discrepancies could be due to the different data refresh frequencies. But how does one explain this convincingly to the business users?

The answer is streamlining the validation process with automation. You can use AI-based or rule-based algorithms to compare between legacy and new environments. You can assess the data and reports at various levels: table by table, row by row, and column by column. MD5 hash mechanism can be employed to validate whether the elements match. Any mismatches will be reported in standardized ways, with explanations — such as differences in data refresh frequency or optimizations made in the new environment that result in more accurate metrics. If the mismatch is due to genuine defects in the new system, such as errors in the code, this must be speedily resolved, and the data reprocessed.

If, after all the migration work, you cannot convince your business users that the new cloud system is accurate and reliable, they will not adopt it putting your entire investment at risk. Hence, data validation is as much about change management as it is about validation, making it the most vital step in the success of your migration.

A Quick Guide for Successful Data Migration

Why Choose Snowflake?

An area of considerable deliberation in your data migration strategy is deciding which cloud warehouse platform is right for your organization. You need a platform that will best support your data and analytics needs, scale apace with your growth, and streamline spends.

Here are a couple of factors to consider in addition to those mentioned above:

• Ease of Use: Is it easy to use and manage, or does it require extensive training and a complex IT setup?
• Security: Does it provide robust security features to keep your data safe?
• Integration: Can it integrate with your existing IT environment?

A Comparative Analysis

A detailed comparison of major cloud data warehouse platforms, enumerating their features, market positioning, and technical capabilities.

More about Snowflake

Snowflake offers a powerful platform to unlock the full potential of your data. Founded in 2012 by Benoit Dageville and Thierry Cruanes (Oracle architects with a foundational knowledge of databases) and Marcin Żukowski (a query optimization and data processing expert), the platform pioneered embracing cloud-native architecture for data warehousing. Large enterprises like Capital One, JPMorgan Chase, and Office Depot have adopted it to reduce their data infrastructure costs and complexity.

Snowflake’s Unique Architecture

It consists of three key layers that help build this valuable data foundation that drives business value.

Database Storage

When data is loaded into Snowflake, it is reorganized into an optimized, compressed, columnar format. Snowflake stores this data in cloud storage and manages all aspects of how this data is stored. Snowflake now supports Iceberg open file format as an alternative to snowflake native file format. This allows interoperability of leveraging the data available in cloud service provider storage layer to process using Snowflake compute with any vendor lock-in concerns.

Query Processing

Query execution is performed in the processing layer. Snowflake processes queries using “virtual warehouses”. Each virtual warehouse is an independent compute cluster that does not share compute resources with other virtual warehouses. As a result, each virtual warehouse has no impact on the performance of other virtual warehouses.

Cloud Services

The cloud services layer is a collection of services coordinating activities across Snowflake. These services tie together all of the different components of Snowflake to process user requests, from login to query dispatch. The cloud services layer also runs on compute instances provisioned by Snowflake from the cloud provider.

Source: Snowflake Documentation

Why Snowflake Migration?

Snowflake migration helps unify diverse data and empowers business users to create tailored virtual warehouses and share data confidently with partners. It enables pay-as-you-go usage and easy scaling of analytics without new resources, unlocking the full potential of data without compromising on cost efficiency.

A Deep Dive into the On-the-Ground Benefits of Snowflake Migration

1. Support for Multiple Data Types

In legacy systems, you are often limited to processing and analyzing structured data, such as transactional data or product information. However, Snowflake allows you to ingest and process not only structured data but also semi-structured and unstructured data. This capability expands your ability to analyze various data formats, such as JSON, XML, and even unstructured documents like PDFs, exponentially enriching your insights.

2. AI/ML Capabilities

Legacy environments are often not built to run predictive or prescriptive algorithms, limiting their capacity for advanced analytics. Snowflake, however, integrates AI and machine learning capabilities, enabling you to apply advanced algorithms directly to your data, unlocking sophisticated scenarios and predictions.

3. Scalable Infrastructure

One of the significant pain points in legacy environments is the inability to scale the infrastructure continuously. When performance issues arise or more data is added, it requires extensive capacity planning and infrastructure upgrades. Snowflake eliminates this problem, as it is designed for automatic and seamless scaling. Storage and compute are separated, and Snowflake offers the flexibility to adjust each of these independently based on your needs, so you can optimize performance on the fly without worrying about underlying infrastructure management.

4. Pay-As-You-Go Pricing

Snowflake operates on a consumption-based pricing model, meaning you pay only for the storage and compute you use, with billing down to the second. This is a significant advantage over traditional systems, where you might be locked into high upfront costs for infrastructure or ongoing maintenance fees. For instance, with Snowflake, if you stop processing after a minute and 10 seconds, you are only charged for that time, providing you with substantial cost savings.

5. Automatic Data Recovery

In legacy systems, recovering accidentally deleted data can be lengthy challenging, and sometimes unsuccessful. With Snowflake, you can take advantage of its native

• Time travel (accessing changed or deleted data as long as it is within a defined period) and
• Fail-safe (ensuring historical data is protected during system failures, security breaches, and other such events)

capabilities. If data is accidentally deleted or corrupted, you can restore it instantly, minimizing downtime and ensuring data and analytics integrity.

6. Data Resiliency

Snowflake provides robust data resiliency features to ensure business continuity. For example, if your Snowflake environment is hosted in an AWS region (e.g., US East) and that region experiences an outage, Snowflake allows you to replicate your data to another AWS region or even another cloud provider, such as Azure or GCP. This ensures that your systems remain up and running, no matter what.

7. Simplified Data Sharing

One of Snowflake’s standout features is its data-sharing capability. Once your data is stored in Snowflake, you can easily share it internally with different business units or externally with partners, suppliers, or other stakeholders. The sharing process is straightforward, requiring just a few clicks, and Snowflake ensures proper permissions and data masking, so you don't have to worry about exporting data manually or using external file transfers for privacy reasons.

8. Built-in Security and Governance

So, your data stays secure as it is queried and shared; Snowflake brings together powerful security controls — which provide identity and access management across CSPs, networking, and encryption — with a unified governance model that enforces policies, tags, and lineage.

Source: Snowflake for Analytics | AI Data Cloud

9. No Infrastructure Management

Snowflake takes care of all infrastructure management. Hence, training and deploying a large team to maintain and upgrade your systems is not required. Unlike legacy environments, where you may have had to manage hardware, apply patches, or scale infrastructure manually, Snowflake automates these processes. This means you can focus on your core business and strategic initiatives, leaving the technical complexity to Snowflake and your implementation partner.

The Business Benefits of Snowflake Migration

The availability of up-to-date, accurate, and diverse data on a single platform powers insights-backed decision-making.

• Empowering Business Teams
  Since a Snowflake warehouse can combine structured data like sales figures with unstructured data such as economic and market analysis and facilitate advanced analytics, retail supply chain teams will be armed with accurate forecasts to meet demand across their markets throughout the year.
• Personalizing Customer Experience
  Consider an e-commerce company that needs to track customer preferences 24/7. Snowflake's easy, near-real-time merging of purchase and browsing history, chatbot interactions, and social media conversations can help the company gain these insights and tailor customer engagement with far greater precision.
• Boosting Process Efficiency
  Maximizing occupancy is one of the most challenging business problems for the hotel industry. Unifying structured data (like booking systems) and unstructured data (such as customer feedback or flight delay reports) on Snowflake will tackle this effectively. A hotel chain, for instance, can run its room allocation optimizations faster and with more granular inputs using centralized data, maximizing occupancy.
• Enhancing Governance
  A unified platform simplifies data governance. For instance, a financial services firm will find it easier to ensure data integrity, security, and compliance with all the relevant data in one place.

How Partners Can Increase Value with Data Migration Projects

Given the plethora of competencies required—technology strategy, domain knowledge, implementation experience, and change management support—it is not unusual for an internal cross-functional team to look for a seasoned partner for their data migration.

Typically, a partner with an established track record will:

• Provide end-to-end data migration and modernization services
• Offer dozens of accelerators, including industry-focused solutions and tools
• Provide well-honed industry use cases to solve entrenched problems fast
• Increase value with a migration center of excellence, partner orchestration, and program management
• Offer a co-innovation platform to develop and scale new processes

Source: Migrate to Modernize Your Data and Analytics

Tredence uses its expertise in cloud and data engineering to build services and accelerators created on native cloud components. Our data modernization services and accelerators assist clients with big data modernization and data migration to respective cloud platforms in a short period of time. Our solutions also help unlock greater value from the same data; automation tools and platform operations enable higher ROI and value realization up to the last mile while lowering your cost of implementation.

Source: Data Migration and Modernization Services | Tredence

Why Choose Tredence

At Tredence, we bring expertise and proven results to every step of your data migration journey.

• Proven Accelerators for the Entire Lifecycle:
  Our advanced accelerators streamline each stage, from discovery to data and process migration and data validation.
• Dedicated Migration Center of Excellence:
  A cross-functional group of experts focused on ensuring seamless migration and maximum value capture from your data.
• Strong Technology Alliances:
  We work with all industry-leading cloud providers and technology innovators to deliver cutting-edge solutions.
• Our Unique Delivery Methodology (The Tredence Way): A proven, structured approach designed to optimize outcomes and maximize ROI.
• Extensive Cloud Platform Expertise:
  Deep experience across leading cloud platforms ensures a smooth migration experience with best-in-class solutions.
• Focus on Last-Mile Adoption:
  We prioritize actionable insights and self-service capabilities, empowering your teams to make data-driven decisions effectively.

The Tredence Advantage

• 80% Reduction in discovery time, accelerating the start of your migration.
• 50% Reduction in data migration efforts, saving time and resources.
• 30% Productivity Improvement across the project lifecycle, driving efficiency.
• Up to 50% Improvement in self-service analytics, empowering users.
• 80% Cost Savings in platform expenses within the first year.

Success stories

A Global CPG Giant

Challenge: The client’s legacy codebase for supply chain initiatives was sub-optimally designed, leading to a sprawling and inefficient environment with frequent downtimes and requiring frequent system refreshes.

Solution: Leveraging Snowflake capabilities, Amazon EMR, Tableau, and Tredence accelerators in the AWS environment, Tredence modernized the supply chain system.

Impact:

• Achieved 30-35% cost savings in overall licensing and total cost of ownership (TCO).
• Delivered real-time, end-to-end supply chain visibility with over 200 KPIs, empowering 500+ active daily users to make faster, data-driven decisions.

A Global Leader in Hygiene Products

Challenge: The client relied on an on-premises Hadoop-based system built on Cloudera, which constrained their ability to scale efficiently. Reporting SLAs were frequently missed, and the system incurred escalating operational costs.

Solution: Tredence used its accelerators to migrate the client’s data ecosystem to Snowflake on Google Cloud, and Looker for BI.

Impact:

• Achieved $1 million in annual cost savings through efficient query optimization.
• Realized a 50% improvement in pipeline performance, ensuring faster and more reliable data processing.
• Enabled an 80% improvement in provisioning new datasets, dramatically enhancing responsiveness to business needs.

Tredence Accelerators

Here is a detailed look at some Tredence accelerators. These are boutique tools that have successfully streamlined many enterprise data migrations:

T-Discoverer

A database and ETL code reverse-engineering accelerator for analysis and discovery. It deconstructs legacy tech stacks, workflows, codebases, and interdependencies into visualizations, enabling deeper insights and optimized migration strategies.

T-Ingestor

A codeless, automated data migration accelerator with built-in quality, catalog, and metadata management features. Combined with other Tredence accelerators and external tools, it ensures systematic migration with minimal downtime, increased safety and compliance. These in-house accelerators, some of which are platform agnostic, have been shown to shorten the migration journey from data warehouses (Teradata, Netezza, Exadata, etc.) and data lakes (Hadoop, Cloudera, Hortonworks to Snowflake by more than 30%.

T-Converter

Handles code conversion during the Convert and Build phase of the key process migration stage. It accelerates code conversion for relational databases (SAP BW, HANA, etc.) by directly converting ~20-30% of the code on average while re-designing/re-engineering helps retire a lot of unused codebases.

T-Assurer

A testing framework created by Tredence for rapid testing and CI-CD integration. It tests data under extreme conditions and validates results so that the least expected inconsistency can be explained or fixed well before going live.

Work with a Leading Data Migration Consulting Partner

Use Tredence's data migration and modernization services to accelerate your cloud migration. We assist you in developing connected intelligence, providing teams with reliable insights, and enabling Snowflake's predictive and GenAI use cases. With +250 Snowflake-certified architects and +10 approved accelerators, we are an Elite Snowflake partner that offers scalable data platforms, AI/ML solutions, and seamless migrations that have a quantifiable impact on various industries.

Utilize the Snowflake AI Data Cloud to innovate further; collaborate with Tredence to update your data foundation and transform your data into a competitive advantage.

Unify and monetize your data with Tredence and Snowflake.