Taylor McGrath
NOV 23, 2023
7 min read
Don’t miss a thing!
You can unsubscribe anytime

More companies are starting to realize the importance of migrating from one data warehouse to another. This strategic shift is driven by various factors, all aimed at optimizing data infrastructure and unlocking its full potential. From boosting performance and cutting costs to ensuring scalability, data quality, and ecosystem alignment, companies are embarking on a complex journey towards a future-proof data ecosystem. But successfully navigating the challenges of data warehouse migration requires careful planning and the right tools. In this blog post, we’ll cover the ins and outs of data warehouse migrations.

Why do companies migrate from one data warehouse to another?

Companies may migrate data warehouses for a variety of reasons as technology and business needs evolve over time. The reason for these migrations is usually one or more of the following:

Performance improvement 

As data volumes grow and query complexity increases over time, the existing data warehouse may struggle to deliver the required performance and meet SLAs. Migrating to a more modern and scalable solution can help improve query time and overall system performance.

Cost reduction 

As data needs expand in the cloud, companies may find that their current data warehouse solution is becoming cost-prohibitive. Migrating to a more cost-effective alternative can lead to significant savings depending on the use case. The possible cost savings may be performance related, ease-of-use related to save on expensive technical skill sets, or licensing related by switching to a solution that is consumption-only. Beyond just reducing immediate costs, companies may consider the long-term total cost of ownership (TCO) when migrating due to factors like maintenance, training, and future scalability. 


As a company grows, its data requirements grow with it. Scalability may mean data volume, data velocity, or data variety. Beyond just the data itself, types of use case needs may scale out as well – what starts as a business intelligence and reporting need may eventually become a machine learning need for another data engineering team. In these cases, it’s important for companies to have the technology to power many different types of use cases across the organization. 

Data quality and governance 

Migrating to a new platform may allow for better data governance practices and improved data quality controls. In regards to data warehouses with consumption-based pricing models, this is essential for cost and usage tracking as well, so that companies can control and manage their spend at granular levels.


The availability of a supportive user community and a rich ecosystem of tools and integrations are important factors in deciding to migrate data warehouses. In addition, integration needs will scale as businesses do. What worked for a maturing startup in terms of integrations may not work at scale, and may not be flexible enough to keep up with changes in the company’s data ecosystem. Changes in business strategy, partnerships, or acquisitions may require better integration between different data sources and systems. A migration can align the data warehouse with the company’s new integration requirements and ensure agility for future needs and changes.

What are the challenges of performing a data warehouse migration?

Once a company has decided to migrate from one data warehouse to another, the migration process itself can be complex and time consuming. 

Data warehouse migrations are complicated endeavors that require careful planning, significant resources, and thorough testing. The costs and pains associated with the migration process can vary widely depending on the complexity of the data environment, the scale of the migration, and the chosen destination platform. Companies should conduct a comprehensive assessment and develop a detailed migration plan to mitigate these challenges and ensure a successful transition. 

Top 3 challenges of completing a data warehouse migration

1. Financial costs 

Depending on the destination data warehouse platform, you may incur licensing or subscription costs. Provisioning and maintaining the necessary compute and storage resources in the new data warehouse environment can also be a significant expense, as well as the physical cost to transfer data in the cloud. In addition to the technology, hiring external experts for planning, executing and optimizing the migration can be costly. Moreover, if any downtime or disruption occurs due to the migration, negative business impact can result in loss of revenue or productivity. 

2. Data Complexity Challenges 

As part of the migration, data may need to be cleaned, transformed, or restructured to fit the new data warehouse’s schema or data model. This process can be time-consuming and error-prone. For data validation, extensive testing may be necessary to ensure data integrity after the migration. 

3. Technical Challenges 

Ensuring that existing data and applications are compatible with the new data warehouse may require code modifications and custom ETL development. Migrating ETL pipelines, data connectors, and integrations with other systems can be challenging, especially if the existing connectors are written in custom code or processes are currently carried out manually to load data. Data Security and Compliance

What approaches can you take to migrate data warehouses? 

First, let’s divide the migration process into parts. 

1. Replicating the ingestion loads from sources to point to the new DWH

In order to minimize any downtime, it’s recommended to run both the ‘old’ and ‘new’ architectures in parallel to conduct appropriate quality checks and end user testing. First, you’ll need to replicate the existing source loads into the new target data warehouse. This is getting through the ‘EL’ of the full ELT replacement to a new data warehouse. This requires understanding how your existing extract and load processes work, which sources and entities you need to replicate, and the tools and technologies available to make the replication. If the existing extract and load processes are manual and rigid, this could be a crucial point for exploring alternative solutions that would allow for faster and easier data ingestion for not just the migration, but all future ingestion needs. 

2. Migrating the ETLs to leverage the new DWH’s syntax and functionality

In addition to the source data replication to the new data warehouse, the data transformations, or business logic, will need to be migrated. Most of the time data transformations are written in SQL, or exist in the logic of an ETL tool. This part of the migration is usually the most taxing, as migrating SQL from one data warehouse to another can require changes in syntax and query structure. In addition, this can open a great opportunity for restructuring and optimizing existing data processes and ETLs in the new data warehouse. For example, imagine you are migrating from Redshift to Snowflake. This is a chance to assess if a lift-and-shift of existing data transformations make the most sense, or if there is a way to optimize based on Snowflake’s strength, such as handling semi-structured data.

3. Switch downstream (data consumer) systems to point to the new DWH

The final piece of the puzzle is to make the production ‘switch’ for end users. This requires updating the connections in downstream systems, such as Business Intelligence tools, to point to the new data warehouse instead of the old.

Note – a quick way for end users to see value (and not wait for the end of the entire migration process) is to replicate directly from the final tables produced in the ‘old’ data warehouse into the new one, so that this switch can happen before steps 1 and 2 are completed. This would require replicating data sets directly from the old data warehouse to the new one (such as Redshift to Snowflake). Some considerations in this case are tables versus views – if views are most commonly used by downstream systems, it may make the most sense to replicate the resulting data of the view itself temporarily, rather than recreate the logic of the view itself in the new warehouse, which would mean waiting for parts 1 and 2 of the migration above to be complete. 

How Rivery can help

If we take into consideration the parts of a migration listed above, all three can be sped up by an easy-to-use and flexible ELT platform. 

First, the ingestion processes to replicate can be self-service and simple with a tool like Rivery. In just a few clicks, you have your source data in your new data warehouse.

Check out this video to see how easy it is to replicate Google BigQuery data into your target destination:

You can learn all about the benefits of Google BigQuery as a source here.

In addition, as noted above, you can replicate your existing tables and views from the ‘old’ data warehouse directly into the new one, to speed up time to value for end users while you finish out the rest of the migration process and rebuild the transformations, etc., in the new data warehouse logic.

Ideally the migration and ongoing orchestration once migrated can happen all in the same place, so that your extract and load processes can coexist with the downstream transformations that are dependent on these loads.

If you’re just starting out or even in the midst of migrating from one data warehouse to another and need assistance, feel free to consult with us anytime 

Minimize the firefighting.
Maximize ROI on pipelines.

icon icon