Cloud Analytics Migration Strategies

Dmitry Anoshin
Published in
4 min readMar 26, 2019


Nowadays, speed and timing matter. This agility combined with innovation has proven to be a game-changer for several organizations across various industries.

Technology innovations can improve data warehousing and analytics with regard to availability, simplicity, cost, and performance.

Amazon Web Services, Microsoft Azure, Google Cloud Platform — are leading cloud providers. The properties of the cloud make it particularly well-suited for data warehousing. There are some benefits of Cloud Analytics:

Unlimited resources — Cloud infrastructure delivers near unlimited resources, on demand, and within minutes or seconds. Organizations pay only for what they use, making it possible to dynamically support any scale of users and workloads without compromising performance.

Save money, focus on data — Companies that choose a cloud-built solution to avoid the costly, up-front investment of hardware, software, and other infrastructure, and the costs of maintaining, updating, and securing an on-premises system. They instead focus on analyzing data.

Natural integration point By some estimates as much as 80 percent of data you want to analyze comes from applications outside your company’s data center. Bringing that data together in the cloud is dramatically easier and cheaper than building an internal data center.

How to Start with Cloud Analytics Migration Project?

You don’t need to “invent a bicycle” for your cloud analytics migration project. There are multiple migration strategies available and depend on your needs and goals. Some of them fast, other slower.

Usually, the migration process includes five phases:

  1. Evaluate Opportunity — analyze the cost and benefits associated with migration to the cloud.
  2. Discover and Analyze — access cloud migration portfolio and formulate a migration plan.
  3. Plan and Design
  4. Migrate, Integrate and Validate
  5. Operate and Optimize
Migration Process

Cloud Analytics Migration Strategies

There are six commonly used strategies for analytics migration to the cloud:

  1. Lift and Shift
  2. Lift and Reshape
  3. Drop and Shop
  4. Re-write/Decouple applications
  5. Retire/Decommission
  6. Retain/Not Moving

It is important to note that most migration projects employ multiple strategies, and there are different tools available for each strategy. The migration strategy will influence the time it takes to migrate and how the applications are grouped for the migration process.

Lift and Shift

This approach allows us to do migration fast. We simply lift our application and shift to the cloud. There are minimal changes to the resources during the migration process. This strategy fast, predictable, repeatable, and economical.

For example, you have Oracle Data Warehouse, you might launch EC2 instance in AWS and install Oracle there, then just copy your entire database. It is fast and it works, but you don’t get cloud advantage and you will still use outdated data warehouse solution.

Lift and Reshape

This approach is similar to the previous one. But you will also deploy the last version of the software. For example, you may have Oracle 10 on-premise and you will install Oracle 12c on AWS.

It is important to perform UAT after re-platforming and validate post-migration of operational efficiency.

Drop and Shop

This approach allows you to replace your application with a new one. For example, you might have a legacy BI tool at your on-premise analytics solution, like Crystal Reports. During cloud migration, you will buy Tableau Software and migrate your reports to the modern visual analytics platform.

Or you may think about sending your on-premise MPP solution to the Snowflake.

As a result, this strategy may involve re-factoring and re-architecting.

Re-write/Decouple applications

This approach is about changing application binaries before migrating to the cloud. This could be applicable to the custom and open-source solutions. For example, some organization prefers to use Apache Airflow and Apache NiFi as data integration tools.

Another example could be the replacement of on-premise technology with Cloud Managed Services. For example, you may use Apache Kafka as a streaming engine, but you may use managed cloud alternative such as Azure Stream Analytics or AWS Firehose.


This approach will decommission your application on-premise.

For example, you might replace your custom reporting solutions, Crystal reports, VBA spreadsheets with a single visual analytics platform like Tableau.

Or, you may think about using the modern ELT tool like Matillion, that will replace several traditional on-premise tools because it is a powerful enterprise ELT and provide efficient self-service capabilities for end users.

Retain/Not Moving

This approach allows you to leave your solution on-premise. There are many cases when you couldn’t migrate to the cloud. It could legacy and unsupported solution. Or you have some compliance restrictions.

As a result, you may maintain connectivity with the cloud environment. For example, you may collect data from the on-premise database and load into a cloud data warehouse.


We covered six different cloud migration strategy. But all that you need to know — there are two major techniques:

  1. Lift and Shift — just copy as is with limited changes
  2. Split and Flip — split solution into logical functional data layers. Match the data functionality with the right technology. Leverage the wide selection of tools on the cloud to best fit the need. Move data in phases — prototype, learn and perfect.

Rock Your Data — Rock Your Data is a consulting and technology company with a focus on secure and scalable cloud analytics solutions using top-tier cloud vendors.

Our mission is to provide high-quality analytic solutions for companies and help them drive business with informed decisions by leveraging powerful and modern cloud capabilities.



Dmitry Anoshin

Chief Data Engineering and Analytics Instructor