Best Practices on the Road from On-Premise to the Cloud – Part 2/2

In part 1 of this blog series, we explored the rising popularity of digital transformations that shift from on-premise data warehouses to the cloud. However, many technical challenges can arise along your migration journey. Studies indicate that the failure rate for data migration projects is 38% – that’s a significant risk of failure. Moreover, a failed migration can disrupt vital business processes impacting operations that handle customers. So it’s best to be well-prepared for your data migration journey with a clear idea of the stages of migration and the challenges that you’ll have to handle.

How to Tackle the Challenges on Your Migration Journey

Although these seem like 5 straightforward steps, there will likely be many technical challenges to overcome. Here are some insights on how to handle them.

1. Adjusting Your Data Model

Modern, cloud-friendly data warehouses require a different type of schema. This can necessitate adjusting your data model as data types may vary. For instance, AWS Redshift is PostgreSQL compatible, which has the most familiar range of data types. However, BigQuery, makes use of STRING instead of VARCHAR, and also utilizes REPEATED (arrays)  and RECORD (semi-structured objects) data types. Snowflake in turn provides OBJECT, ARRAY and VARIANT that support data that is semi-structured.

However, there are numerous data types that are not supported such as geographical coordinates and so on. Aside from data types, cloud data warehouses often require schema denormalization to provide enhanced performance. Thankfully the increased storage for denormalized data is fairly affordable. However, running JOINS on tables that happen to be stored on numerous distributed servers can be extremely expensive. It can get even more complex as you must keep the two data models in continuous sync as they change over the course of time. This means that the data model will most probably change during the course of migration.

2. Ensuring Security

While there are many advantages to large cloud providers’ government-level of security, the pressure for a rapid migration poses risks. The cloud providers’ security encompasses hardware and infrastructure. This does not mitigate the risks of access permissions – that’s in your hands. It is enticing to give all developers and data consumers permission to all cloud resources as a way to bypass tedious permissions management. However, this approach poses security risks and makes it tougher to implement stricter security permissions at a later stage.

So don’t put it off, consider mapping out permissions for all roles that will access cloud resources and plan security policies. Do this as soon as the migration gains organization-wide approval and the project begins. Take the time to carefully study your cloud provider’s functionality to manage roles and permissions. They are usually very comprehensive, so you can define policies based on your requirements. Configuring them properly will ensure security and a smoother migration journey.

3. Connecting Custom Data Applications

During migration another common challenge is the need to adjust interfaces that your custom data applications utilize to connect to the data warehouse. Although ODBC/JDBC drivers are supported actively, they rarely function identically. When you change an application’s database driver, you might also need to make several query adjustments. Different ODBC drivers could make minor data conversions regarding handling of NULL values, format of timestamps, and precision levels for floating point numbers.

It will take rigorous testing to detect these changes and data discrepancies. Consider using a larger team of engineers to overcome this challenge faster. Make sure to conduct knowledge sharing of adjustments across the entire team.

4. What About Stored Procedures?

Many organizations face a challenge with a common feature of on-premise data warehouses that gets overlooked – the capability to write and make use of stored procedures. Although most of the leading cloud data warehouses provide the ability to write user-defined functions (Python, SQL, or JavaScript), they are often not an adequate substitute for their on-premise counterpart. The fact is, the layer of stored procedures in an on-prem data warehouse practically amounts to a collection of mini data applications in their own right. They serve to eliminate a lot of manual effort and maintain knowledge that is organization-specific. A popular solution is to utilize an individual platform to schedule task orchestration or parametrized queries. However, certain data modernization platforms provide capabilities to this end.

Being prepared will help you execute a staged approach to your cloud migration and overcome some of the complex challenges that can arise. Make use of this knowledge for a smooth migration with minimal business disruption.

Why DataSwitch?

DataSwitch is a trusted partner for cost-effective, accelerated solutions for digital data transformation, migration and modernization through a Modern Database Platform. Our no code and low code solutions along with enhanced automation, cloud data expertise and unique, automated schema generation accelerates time to market.

DataSwitch’s DS Migrate provides Intuitive, Predictive and Self-Serviceable Schema redesign from traditional model to Modern Model with built-in best practices, as well as fully automated data migration & transformation based on redesigned schema and no-touch code conversion from legacy data scripts to a modern equivalent. DataSwitch’s DS Integrate provides self-serviceable, business-user-friendly, metadata based services, providing AI/ML driven data aggregation and integration of Poly Structure data including unstructured data. It consolidates and integrates data for domain specific data applications (PIM, Supply Chain Data Aggregation, etc.). DataSwitch’ s DS Democratize also provides intuitive, no code, self-serviceable, conversational AI Driven “Data as a Service” and is intended for various data and analytics consumption by leveraging next gen technologies like Micro Services, Containers and Kubernetes.

An automated data and application modernization platform minimizes the risks and challenges in your digital transformation. It is faster, highly cost-effective, eliminates error-prone manual effort and completes the project in half the typical time frame. Book a demo to know more.