Data warehouses are widely used in businesses for analytical purposes and decision-making in divisions like Sales, Marketing, and Finance that frequently use reports and dashboards. However, the development of digital technologies over the last few years has altered the data warehouse landscape, according to Microsoft. To consolidate systems, scale operations, and enable mobile self-service, businesses are moving more applications, including CRM, ERP, and HR, to the cloud.  

This means that data warehouses must now have the ability to ingest data from SaaS applications in the cloud. Data points have been expanded to include social media data, Internet of Things (IoT) sensor data, weather information, or even image, audio, and video data. But data warehouses are lagging.   

Data scientists separately process and analyze this cloud-based data. That’s why companies are moving from on-premises to the cloud to modernize their data warehouses and take advantage of high speed, security, scalability, cost savings, and performance.  

What is the need for organizations to move their data to the cloud?  

Many companies are migrating their data warehouses to the cloud to improve data capabilities, such as implementing Machine Learning in their operations. Moving to the cloud also allows for more seamless integration with newer technology systems and provides greater data security. The benefits of moving your data warehouse to the cloud go far beyond on-premises options, including:  

1. Performance  

You can more effectively optimize your data warehouse with the cloud. The overall functionality of the hardware and software used to process your data is improved by having your data warehouse in the cloud. You can make your solution work more intelligently.  

2. Speed  

Data warehousing processes require data ingestion, transformation, and analysis, cleansing, aggregation, and integration. It then visualizes and reports those results. These steps take time. The process is also complex, with each step dependent on the other. This dependency means that a problem or an increase in the amount of data can stop the entire process. With an agile cloud data warehouse, you have the critical information you need to make timely decisions.  

 3. Flexibility  

Customer demand and business operations can change at any time. Around certain times of the year, there may be a spike and a lull. With cloud elasticity, you can easily increase and decrease the capacity of your data warehouse to suit your needs without jeopardizing the availability, stability, performance, or security of your infrastructure.  

4. Cost-effective  

Using a cloud-based data warehouse has many advantages, one of which is cost flexibility. Your expenses will fluctuate in accordance with your changing needs. On-premise legacy solutions, on the other hand, demand a financial investment for servers, hardware, networking, the actual storage space, electricity, cooling, and IT personnel. With a cloud solution, none of those up-front expenses are necessary.  

5. Scalability  

A solution that can expand along with your business is essential. Given that the amount of historical data in your data warehouse will grow over time, using the cloud allows you to add resources as your business’s needs, workloads, and data increase.  

 6. Managed Infrastructure  

Data is typically stored in cloud solutions in cutting-edge data centers that the business owns and operates. As a result, your team is free to focus their skills and resources on other, more strategically important parts of your company, since this task has been taken off their plate. Additionally, your team can concentrate on using the insights that your cloud-based data provides without having to worry about managing the infrastructure themselves.  

Challenges of migrating data warehouse to the cloud  

Migrating your data warehouse to the cloud can present different challenges. Before migrating your data warehouse to the cloud, understand some of the challenges associated with migrating on-premise data warehouses to the cloud.  

• Replanning data model  

Following a denormalization approach is frequently advised when moving your data warehouse to the cloud in order to improve performance. Nevertheless, maintaining compatibility between your old and new data models while migrating can make the denormalization process more challenging.  

• Handling stored procedures  

In essence, stored procedures are statements that aid in the preservation of data and information unique to the inner workings of your data warehouse. Unfortunately, most cloud data warehouses lack the ability to use already existing stored procedures or to create new ones, which is problematic for data warehouse users. This means that organizations moving their data warehouses to the cloud may need to use another platform for process orchestration and other related tasks. It’s important to keep this in mind and conduct research on cloud data warehouses to make sure that your organization’s efficiency is not compromised in the event that your cloud data warehouse does not support stored procedures.  

• Establishing connectivity 

There may be some difficulty in connecting your custom applications to your cloud data warehouse. While some custom applications may be connected and integrated with your cloud data warehouse simply by making a few minor SQL statement changes, you may need to dig a little deeper to find other connectivity issues. These problems frequently appear as inaccurate or inconsistent data, which can be problematic if not found in time. 

Top 5 Cloud Data Warehouse Best Practices for Migration Success  

1. Involve all departments  

The vast majority of businesses think that the IT team is solely responsible for data migration. This is a mistake because it means that the human element is not taken into account and the process can be difficult and fraught with uncertainty. Behavior management and bringing together all the parties involved at every stage of the process is the best way to make sure that the migration process goes smoothly. 

2. Plan extensively

Consider the data migration process as a continuum of moving parts that work together to improve organizational performance and competitive edge rather than as a singular event that involves moving data from point A to point B. To put it simply, more work needs to be put into the “planning” stage to make sure that businesses benefit from an end-to-end, repeatable process flow, understand the best-associated methodology and approach for data migration, and engage in a cost-benefit analysis to incorporate a pay-as-you-go model rather than paying for additional infrastructural costs.  

3. Engage in data profiling

Data profiling is essential for two reasons. First, it helps to detect risks early. Second, it allows companies to gain a holistic view of data. In addition, using the aggregated analysis results, stakeholders can engage in various aspects, such as root cause analysis to identify business process gaps in the data, empirical discussions on the state of the data, and discussions to identify necessary versus “favorable” requirements. The priorities are to identify and analyze data discrepancies between the recorded state and the real state of the environment and to measure the level of effort required to ensure the suitability of the data for the priority objectives.  

4. Create a data governance strategy

Technology is only one component of data migration; people and procedures are equally important. Before delving too deeply into the data migration process, consider the following queries to address the human factor of the data migration aspect:  

• Which resources are required to be involved?  
• How should a data quality exception be fixed?  
• In the event that a decision cannot be reached, what happens next?  
• How should we handle the initial surge in remediation requirements as opposed to the steady state?  
• How can data quality be guaranteed after going live?  

5. Be flexible

As organizations gain greater data visibility, each step in the data migration process may require iteration. This reduces risk and improves the quality of the final product. It also serves as an option to add cleanup rules in each iteration. Typically, organizations should repeat as needed. The ultimate goal is to make it as easy as possible to accept change.  

Conclusion  

The global public cloud market is expected to spend more than $480 million by 2023, and data migration will continue to pick up steam in that time. It is obvious that failing to create a roadmap for the data migration journey already guarantees that the organization is doomed to failure. The process of transferring data from a legacy system to the cloud is not straightforward or linear.  

With more than 12 years of experience in cloud-managed services, CloudArmee has been offering top-notch cloud services to companies in a variety of industries, organizations can benefit from CloudArmee’s expertise by moving their data warehouse to the cloud.