7 Best Practices For Data Migration To Cloud

Get your cloud migration sorted.

7 Best Practices For Data Migration To Cloud

A botched migration isn't a "better luck next time" scenario. It's a fiasco that interrupts business continuity. Think about what happens if your data is inaccessible for a day or, worse, goes missing. You're not just looking at technical glitches; you're looking at lost revenue, shaken customer trust, and a dent in your brand reputation that could take years to repair. So yeah, this is one of those "measure twice, cut once" situations. You can't afford not to get it right the first time.

Here are 7 best practices for data migration to the cloud that you should incorporate.

1. Pre-Migration Assessment

Migrating to the cloud isn't something you dive into without due diligence. Let's unpack why pre-migration assessments are the blueprint for your entire operation.

Importance of a Health Check for Your Data

Think of a health check as your initial reconnaissance mission. You can't migrate what you don't understand. Running a full-scale audit of your existing data ecosystem allows you to identify the kind of data you hold, its volume, and its structure. Are there redundancies? Maybe certain data clusters haven't been accessed in years. The information you gather sets the course for what gets migrated, what gets archived, and what can be securely deleted.

To put it in concrete terms, your health check should answer the following questions:

  • What types of data are you holding? (e.g., transactional, operational, historical)
  • How much data do you have?
  • How is the data structured or unstructured?
  • Where is the data stored?
  • How frequently is the data accessed or modified?

Getting answers to these questions ensures that you're not flying blind. It minimizes risks and sets the stage for an efficient migration process by clarifying what needs to be moved and what doesn’t.

Identifying Data Dependencies and Choke Points

Data doesn't exist in a vacuum; it's a web of dependencies. Understanding these dependencies is key to a smooth migration. If one dataset relies on another, you need to know this beforehand to prevent any unpleasant surprises during the migration project. Identify which databases, applications, or services are interlinked and map out these relationships comprehensively.

For example, you might have customer data in one database that is frequently pulled by your CRM and your billing systems. Migrating that data without considering these dependencies can lead to functional disruptions in both systems.

Choke points are another concern. These are bottlenecks that can slow down or halt your migration process, causing delays and possibly increasing costs. This could be anything from bandwidth limitations to API rate limits. Identifying these potential choke points ahead of time allows you to allocate resources more efficiently, and perhaps more importantly, manage stakeholder expectations about the migration timeline.

2. Choose the Right Migration Strategy

Lift-and-Shift vs. Re-architecting

When it comes to migration strategies, you've essentially got two main routes. Lift-and-Shift is the equivalent of moving your entire office to a new building, desk junk and all. It's the faster route, but be prepared for operational inefficiencies; what didn't work well in your old environment won't magically improve in the cloud.

Re-architecting, on the other hand, is like custom-building that new office space. You'll spend more time upfront designing it, but the end result is a space that's optimized for your team's actual needs. It's the longer path but can pay off in operational savings and performance gains.

Budget and Timeline Considerations

Your choice between lift-and-shift and re-architecting isn't just a technical decision; it's also a business one. Lift-and-shift typically has a shorter timeline and may require less upfront investment. However, your operational costs could be higher in the long term. Re-architecting demands more upfront time and costs but could yield operational efficiencies that translate into long-term savings.

So, as you decide, align the strategy with both your technical requirements and your business constraints—budget, timeline, and operational goals. It's about finding the right balance for your specific needs.

3. Data Cleansing

Why Dirty Data Is Bad

We've all heard the saying "garbage in, garbage out," and it rings especially true for cloud migration. Unclean data—think duplicate records, outdated entries, and inconsistencies—will only pollute your shiny new cloud environment. Migrating bad data not only skews analytics and decision-making but also magnifies errors across applications that rely on that data. It's like tracking mud into a new house; it spoils the whole experience. The cleansing isn't just a good-to-have; it's a must.

Tools for Cleansing Your Database

Now, how do you go about giving your data that much-needed scrub?

  1. SQL Queries: For straightforward issues like duplicates or null values, a few SQL commands can work wonders.
  2. ETL Pipelines: For more complex cleansing needs, an ETL (Extract, Transform, Load) pipeline can transform your data as it moves from source to destination.
  3. Specialized Tools: For large-scale operations, consider tools designed for data quality, like Trifacta or Talend. These offer more comprehensive features like data profiling, validation, and enrichment.

4. Stakeholder Communication

Keeping the C-suite in the Loop

When it comes to migration, visibility at the executive level is not just courteous; it's essential. The C-suite holds the purse strings and will want to know the ROI of migrating to the cloud. Let's not kid ourselves: Metrics speak louder than words. Before you even start the migration, outline key performance indicators (KPIs) that matter—cost savings, scalability benefits, expected upticks in efficiency, you name it. As you progress, keep the C-suite updated with dashboards or regular briefs that show how well you're meeting, or hopefully exceeding, these KPIs.

Why Your DevOps Team Needs to Know

Your DevOps team isn't just labor; they're your field generals. They'll be the ones in the trenches, ensuring that the migration happens smoothly, securely, and with minimal downtime. Loop them in early—like, "yesterday" early. Doing so equips them to anticipate challenges and pre-emptively problem-solve, which directly affects the success of the migration. Their expertise can help refine your strategy, troubleshoot issues, and essentially make the whole process more streamlined. Neglecting to involve DevOps from the get-go is akin to setting sail without a compass. Not advisable.

5. Regulatory Compliance

Understanding Legal Requirements

Regulatory compliance isn't a hurdle; it's a necessity. It's not enough to have an excellent technical plan for migration if you don't account for laws and regulations like GDPR, HIPAA, or whatever is applicable to your industry. Non-compliance is a minefield you don't want to step into—it comes with hefty fines and potential brand damage.

It's essential to understand which data protection and privacy laws impact your data. Are you holding personal data on European citizens? You'll need to comply with GDPR. Dealing with healthcare data in the United States? HIPAA has your name written all over it. Get your legal team involved to identify what regulations are relevant and how they shape your migration strategy.

Conducting a Compliance Audit

Before you push the big red "migrate" button, conduct a comprehensive compliance audit. This isn't just a formality; it's your safety net. An audit should confirm that all the data you're migrating, as well as the processes and technologies involved, meet the compliance standards you've identified. It's about ticking all the legal boxes, but more importantly, it's about ensuring that you're not unwittingly setting yourself up for liabilities down the road.

Here's where specialized compliance tools can make life a whole lot easier. Tools like Varonis or McAfee Total Protection can automate the audit process, scanning for sensitive data and flagging non-compliance issues.

6. Pilot Testing

Importance of Testing

Alright, so you've done your homework, but before you pull the trigger on a full-scale migration, you absolutely need to run a pilot test. Why? Because testing in a controlled environment is your safety net. It’s your chance to identify kinks in the system, troubleshoot errors, and validate that the data migration process won't implode once deployed on a larger scale. If you skip this step, you're essentially winging it, and let's face it, nobody wants to be in a high-stakes situation without a safety net.

Benefits of Phased Rollouts

Once the pilot test gives you the green light, don't just deploy everything all at once. Opt for a phased rollout. Start with less critical data and systems to get a real-world sense of how the migration is going to play out. It's like a soft opening for a restaurant—you get to troubleshoot in real-time but on a smaller, more manageable scale. Plus, a phased approach allows your DevOps team to fix issues without the pressure of the whole operation hanging in the balance.

7. Monitoring and Optimization Post-Migration

Key Metrics

So, you've successfully migrated your data to the cloud. Time to sit back and relax, right? Wrong. The migration itself might be over, but this is where the long-term relationship with your data starts. You need to keep an eye on key metrics like latency, error rates, and uptime. These are your barometers for how well the new environment is performing.

For example, latency should ideally be low, indicating faster data retrieval. Any spikes in error rates are a red flag that something needs immediate attention. And uptime—well, the closer to 100%, the better. Monitoring these metrics post-migration isn't a "nice to do"; it's a "need to do."

Continuous Optimization Strategies

Once the initial metrics are looking stable, don't fall into complacency. The cloud environment is dynamic, and staying optimized requires ongoing effort. You've got to continually fine-tune configurations, scale resources up or down based on demand, and possibly even implement automated solutions for real-time adjustments.

Think of it like a high-performance sports car; you don't just get it tuned once and forget about it. The same goes for your cloud environment. Use monitoring tools that allow real-time insights and set alerts for any metrics that veer off course.

Subscribe to the Newsletter

About us

We write about all the processes involved when leveraging data assets: from the modern data stack to data teams composition, to data governance. Our blog covers the technical and the less technical aspects of creating tangible value from data.

At Castor, we are building a data documentation tool for the Notion, Figma, Slack generation.

Or data-wise for the Fivetran, Looker, Snowflake, DBT aficionados. We designed our catalog software to be easy to use, delightful and friendly.

Want to check it out? Reach out to us and we will show you a demo.

New Release

Get in Touch to Learn More

See Why Users Love CastorDoc
Fantastic tool for data discovery and documentation

“[I like] The easy to use interface and the speed of finding the relevant assets that you're looking for in your database. I also really enjoy the score given to each table, [which] lets you prioritize the results of your queries by how often certain data is used.” - Michal P., Head of Data