Snowflake Migration
Other / April, 13 2021

Why and How Snowflake Migration Can Reveal the Difference

Are you looking for real-time data analytics?

Do you want to isolate storage from computing to save money?

Is your organization concerned about security and governance while being agile?

In recent times, organizations are in dire need to be data-driven. The priority in this journey is to make it easy for data teams to store and use data.  Snowflake migration can ease the data management process as it connects to a plethora of tools and supports most structured and semi-structured data types. The cloud data warehouse market is flooded with many players. But the Snowflake Cloud Data platform has changed the game.

Can Snowflake migration resolve the data migration challenges better than any other player? Let us further dwell on the details.

A few Data Migration Challenges

As per Gartner, “Between now and next year, more than 50% of data migration initiatives will exceed their budgets and timelines because of improper execution and strategy.”

The top challenges faced when migrating from legacy are:

Improper data governance – Data migration does not have any value without governance. Access to the right data by the right people for the right purpose can only maximize the value.

Data quality issues – Poor quality of data imposes significant maintenance cost post-migration, and it is the foremost priority to ensure a successful migration.

Unrealizing the value of data analysis – Legacy systems have disparate and hidden information sources. At times, incomplete, inaccurate, and outdated information can be transferred to the cloud with inappropriate data analysis.

How does Snowflake Migration add Value to Organizations?

The Total Economic Impact report from Forrester Consulting revealed that Snowflake’s platform can deliver a customer ROI of 612% over 3 years.

The value from Snowflake migration can be decoded as:

  • Snowflake’s Cloud data platform can simplify provisioning, ingestion, transformation, data processing, and administration thereby reducing the time to launch new products by 50%.
  • Data access on a self-service basis can reduce the efforts of IT service teams by 75%.
  • Organizations can save around 30% on compute with instant provisioning and per-second billing*.

*Results vary across industries and organizations.

What is so Unique About Snowflake’s Platform?

The Real Differentiator, the Snowflake Architecture – Are you looking for flexibility for your big data? Then Snowflake is unique to cater to this need. The architecture has 3 layers:

1. Storage Layer

2. Compute Layer

3. Cloud Services Layer

a.) Storage Layer – Unlike many other data warehouses, Snowflake does not support partitions and indexes. Instead, it automatically divides tables into micro-partitions so that data is internally optimized and compressed. Data is also stored in a shared-disk model ensuring simplicity in data management. The storage is also elastic, and it is charged as per usage per TB every month.

b.) Compute Layer – Do you have challenges about a lot of processing for certain workloads while no processing for others? Do you want a lot of use cases on that data?

Snowflake uses a unique concept “Virtual Warehouse” to run queries. Multiple Virtual Warehouses can be created based on the workload and depending on the requirements and storage layer. Each has its independent cluster and does not interact with others.

A few advantages of Virtual Warehouse:

  • Highly scalable and can be started or stopped without impact the query that is running.
  • Auto-suspend and auto-resume feature when idle and to reduce compute costs
  • Data is centralized, it eases to do Dev test and QA.
  • Data in multiple formats like JSON, XML, etc. can be accessed by multiple workloads with a quite different cost-optimized compute resource. 

c.) Cloud Services Layer – This layer coordinates the entire system and eliminates the need for manual data warehouse management. Authentication, security, metadata management, query optimization happens in this layer.

Scale-out for concurrency and Scale-up for performance – ‘CapSpeciality’ a leading provider of specialty insurance in the US achieved a 200x improvement in query performance while analyzing their 10 years data in 15 minutes.

Snowflake has a unique ability to upgrade and downgrade clusters automatically. Snowflake also supports easy adjustments to the Virtual Warehouse size to handle the workload.

In general, Snowflake supports same size clusters for concurrency. But it can scale out in a more controlled way than legacy systems. In short, it provides on-demand elasticity that can improve performance for many concurrent users.

Zero-copy cloning – Another fantastic Snowflake feature that can help you in optimizing storage. This is about duplicating an object without creating a physical copy. This does not contribute to the data storage unless operations are performed to modify it.

Snowflake data sharing also allows you to do it seamlessly with Snowflake users or third parties without any additional storage costs (no copying or extracts).

Data governance and security at its best – Snowflake provides the true source of data as you can manage the data from a centralized platform. Understanding the data becomes easy with Snowflake and its partners. Governance can be simplified with this.

Snowflake’s authorized access to data can help in providing role-based access control, Comprehensive Data Protection, and tokenization to mitigate business risks.

Snowflake has partnered with many players to ensure that you can unify your security and governance.

It has end-to-end encryption. Snowflake supports the Single sign-on (Okta, Microsoft ADFS, SAML) option through Federal Authentication. It also uses Hierarchical Key Model Encryption and the highest security accreditations including SOC 2 Type, HIPAA, PCI-DSS, HITRUST CSF.

Partner ecosystem – Snowflake has a comprehensive partner ecosystem for ETL / ELT, Security and Governance, Business Intelligence / Reporting, Machine Learning, and Data Science. Snowflake connectivity also works through an extensive network of connectors, drivers, and programming languages including JDBC, ODBC, Kafka, Python, .Net, etc.

A few Best Practices for Snowflake Migration

Effective planning for cost management – One of the biggest differentiators for Snowflake is its cost structure. But if proper planning is not done before migration costs will multiply too. For example, if you give a separate workload for each team rather than a use case, the compute costs will increase manifold.

A few considerations that need to be included are:

  • Who will have access and what will their privileges be?
  • Typical analysis of workloads, scenarios, and cost management plan for storage and compute
  • How to ensure business as-is, and optimize data needs?
  • Risk analysis and mitigation plans

Robust validations for effective data migration – The best is that Snowflake migration can happen in stages and not in a single go. There should be thorough validation at each stage to ensure that the data is copied properly.

How to monitor and measure the Snowflake migration process?

For any process to be successful effective monitoring and measurement should be in place. Monitoring the needs of people while also looking at the performance of the new system is crucial to ensure a smooth transition.

Identifying the KPIs to verify the success will not only help in success but also for improvements for the future.

Plan your partners and coordination tools – Planning for the right partners and tools is equally important in the Snowflake migration process. A plethora of options is available in the ecosystem.

Snowflake migration not only requires DBAs but also a good consulting partner to ensure that you save those dollars from your IT investment.

A partner like Saxon, who has their unique expertise in data and consulting can help you leap through many benefits. We at Saxon, also have a partnership with Data Dog to monitor Snowflake.

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