Friday, August 2, 2019

Everything You Need to Know About Migrating Data to the Cloud



The Five Phases of Data Migration

Data systems are integral to business success, generating insights that drive sales and marketing. Migrating data systems to the cloud offers many benefits. Cloud architectures improve refresh speed, consolidate resources, and improve data uniformity. But for companies with terabytes, or even petabytes, of data, migrating to the cloud can seem daunting. Data migration also requires two production environments to run in parallel. Carefully navigating multiple logistical challenges requires a deeply considered plan.

We have over a decade’s experience enabling cloud migrations for Fortune 500 companies. In this article, we will break down the data migration process we have spent years refining. Our cloud migrations take place across five phases:

1.  Discovery  
2.  Analysis and Design 
3.  Planning and Approval  
4.  Execution 
5.  Verification  

1. Discovery

During the discovery phase, we evaluate our client’s existing data system. Every client has distinct needs.

To ensure we get a full picture of client requirements, we look at the data itself, sources, size, scale, refresh frequencies, and source relationships.

2. Analysis and Design

We divide our analysis and design phase into three steps:

1.  Research potentially applicable technologies  
2.  Read through relevant documentation to determine which technologies we want to use 
3.  Perform a series of proofs of concept to determine technology suitability, cost benefit, and comparative features  

3. Planning and Approval

During the planning and approval phase, we propose our solution and receive feedback from the client.
Example: our most recent client tasked us with resolving four primary challenges:

1.  High data refresh and process times  
2.  Server dependencies among multiple streams 
3.  Difficulty managing and maintaining infrastructure  
4.  Difficulty patching infrastructure 

After analyzing the best options for this client, we presented solutions that resolved their specific queries. This included:

1.  Implement real-time data refresh pipelines for real-time reporting  
2.  To improve processing speed, leverage the distributed processing power of the cloud 
3.  Implement a real-time job and asset monitoring framework  
4.  To ease infrastructure maintenance, leverage the serverless, scalable, and distributed architecture of the cloud 
5.  Enable data publishes based on source availability using an intelligent job monitoring and validation framework  

If you want to read more into the specifics of how we enabled our client’s move to the cloud, check out our case study on cloud migrations.

4. Execution

During the execution phase, we implement the approved architecture. We decide which technologies to use for storage, process, publishing, and visualization.

  Storage technology example: Azure Data Lake Storage 
  Process technology example: Azure Databricks 
  Publishing technology example: tabular models, SQL 
  Visualization technology example: Power BI, Excel 

We then break down the project into stages. In general, we use a seven-step process:

1.  Stage the data from upstream  
2.  Transfer the data 
3.  Process the data  
4.  Move the processed data back for downstream users and into storage to create reporting views 
5.  Undertake independent processing and refreshes using an intelligent job monitoring and validation framework  
6.  Process the tabular model (if using one)  
7.  Visualize reports  

5. Verification

During the verification phase, we conduct user acceptance testing (UAT) through numerous user acceptance sessions. UAT allows technical users, business users, and operations team members to become familiar with the new data system as we gradually move the old system offline.

Benefits of Cloud Data Migration

Migrating to the cloud reduces data latency and improves data availability. Cloud-based data offers the latest data infrastructure and real-time reporting benefits. A single source of truth across all reporting layers translates into unlimited scalability. Data can be processed more quickly and, in case of disaster, recovered more easily thanks to geo-replication. Overall, cloud infrastructures reduce data costs; their maintenance is cheaper and easier to scale up or down based on company needs.

Our data migration process balances overcoming logistical challenges with achieving business objectives. At each of the five phases, we make sure to balance technology possibilities with business needs, customizing our solution to our client.


Do you want to migrate your company’s data to the cloud? Find out more about our cloud transformation expertise, or contact our sales team on Sales@MAQSoftware.com