6 Keys for a Seamless Cloud Migration

We’ve Seen a Lot of Cloud Migrations. Here Are Key Tips from Our Experience.

You’ve made the big decision: you want to move to a cloud data warehouse.

At Rivery, we see this every day. Your on-premise data warehouse is too expensive, inflexible, and difficult to maintain. Meanwhile, cloud data warehouses store, manipulate, and analyze massive amounts of data in a more efficient, scalable, and cost-effective way.

And you’re not alone. Just within our own customer base, we’ve seen BI teams, CIOs, marketing agencies, beverage companies, and even wastewater facilities switch to cloud data warehouses for the same reasons.

But migrating your company’s existing data into a cloud data warehouse is no small task. Cloud migrations can easily become complex undertakings. You need to find the right data integration solution, ensure data quality, and configure database syncs, just to name a few of the challenges.

Over the years, we’ve assisted our customers with tons and tons of cloud migrations. And although every migration is different, we have learned a few universal tips that can help anyone who wants to perform a successful cloud migration.

The six tips in this ebook are not mandatory, but they will point you in the right direction during the migration process.

Think of these tips as guideposts. They will serve as a way to orient your priorities as you plan, execute, and maintain your cloud data migration.

1. Choose ELT Over ETL

Data integration solutions streamline the migration of internal and external data sources into a cloud data warehouse. Many teams use data integration solutions to facilitate and expedite cloud data migrations. Your team will likely choose between two data integration technologies: ETL or ELT.



ETL (extract, transform, and load) extracts raw data from sources, transforms the data on a secondary processing server, and then loads the data into a target database. ETL is used when data must be transformed to conform to the standards of a target database.



ELT (extract, load, andtransform), unlike ETL, does not require datatransformations to take place before the loading process. ELT loads raw data directly into a target data warehouse, instead of moving it to a processing server for transformation. The data is transformed inside the cloud data warehouse itself.

ELT is generally more flexible, efficient, and scalable, and is ideal for:

  • ingesting large amounts of data
  • processing data sets that contain both structured and unstructured data
  • powering diverse business intelligence with raw data repositories

ETL is more suited for:

  • compute-intensive transformations
  • systems with legacy architectures
  • data workflows that require manipulation, such as PII erasure

ELT solutions have a big advantage over ETL solutions during cloud migrations. While ETL requires the data to pass through a secondary server for transformation,

ELT loads raw data directly into a cloud data warehouse. By bypassing the secondary server, ELT speeds up the data migration process significantly.

2. Eliminate Technical Grunt Work

Technical grunt work bogs down many data projects, and that’s especially true for cloud data migrations. The volume of data handled during a migration is often enormous. Mundane tasks, such as developing data pipelines for each individual database table, add up and waste your team’s limited time and resources.

Make your life easier

Here’s a checklist of features to consider:

  • Fully managed
  • No-code
  • Auto-scalable
  • Pre-built data connectors
  • Data process automation

Fully managed solutions handle everything on the back-end of the platform, eliminating maintenance tasks. Auto-scalability manages resource limits and system allocation automatically. No-code platforms require zero user- programming. Your developers have more important projects to work on.

Pre-built data connectors save a huge amount of time and energy. Many teams have to manage dozens of internal and external data sources, and building data connectors for each one is not feasible. Pre-built data connectors eliminate this unnecessary investment and allow your team to perform a cloud migration with plug-and-play functionality.

Automation also makes cloud migrations much more efficient. Automated features such as change data capture, Rivery’s Auto-Migration, pipeline scheduling, and automatic table merging make cloud migrations faster, smoother, and less prone to human error. This streamlines the migration process, while improving data integrity and data security.

3. Consult the Experts

There are plenty of shiny objects in the cloud marketplace. New players enter the space all the time, while industry leaders saturate every channel with sleek marketing. But before you decide to adopt a solution, find out if the hype is real. Consult unbiased experts who frequently use the product.

There are many online resources that provide unfiltered viewpoints from verified product users, for everything from cloud data warehouses, to data integration platforms. Some of the top resources include:

TrustRadius
Reviewers on TrustRadius represent a wide range of backgrounds and experience. Read confirmed reviews from end-users, implementers, consultants, business stakeholders, and decision-makers from companies of all sizes.

G2
With over 1 million reviews from confirmed product users, G2 offers an unfiltered view into leading technology solutions. Harness comparison matrices to assess competing services side-by-side.

Capterra
Capterra features validated user reviews and independent research across hundreds of software categories, with in-depth examinations of features and pricing. Over 3 million buyers use Capterra every month for software advice.

SoftwareReviews
Software Reviews aggregates high-level user sentiment towards the client-vendor relationship and product effectiveness with a novel Emotional Footprint score. This feature offers a unique window into how professionals actually feel about certain solutions.

IT Central Station
At IT Central Station, users can either post anonymously to freely express their views, or use their real names to promote their expertise. The option of anonymity encourages frankness in the reviews.

Of course, these reviews should act as one of many factors in your decision making process. But it never hurts to hear what real product users have to say about the solutions you’re considering.

4. Consider What the Future Holds

A cloud data migration is the task in front of you. But what about the projects you’ll tackle several years down the road? If you want to lay the groundwork for long-term success, you need a data integration solution that can perform beyond a single project. Ideally, you will enter a long-term technology partnership, one that grows over time.

When it comes to long-term strategy, input from other departments is key. The challenges your solutions will have to tackle depend on the trajectory of the company. Consult the CTO, CIO, BI directors, the CEO, or any other stakeholder to learn what’s on the horizon for the business.

Then ask yourself some forward-looking questions, such as:

  • What business challenges will our company face in the future?
  • How will our data operation have to respond to these challenges?
  • What solution allows us to meet these challenges while still staying nimble
  • Does the business plan to scale?
  • What is the projected increase in headcount?
  • Are offices opening in different geographic locations?
  • What databases will these new offices use?
  • How will our roles change in the future?
  • What solution can best position us for the team’s future responsibilities?

These types of questions can help you articulate some of the key challenges and opportunities on the horizon. You can identify the solutions that will provide long-term value to your team.

5. Confirm Data Quality

In order to ensure a successful cloud migration, you must confirm that the data between your database and your cloud DWH are identical.

Quilliup and other data quality solutions run exercises and tests to verify that the data matches, such as:

 

Integrity checks throughout the migration process Confirming data integrity throughout the migration process stops small errors from becoming big ones. Data quality solutions that check data in real-time enable you to avoid wasting resources on follow up migrations, and protect you from bad data that can lead to costly errors.

 

Alerts for data discrepancies Solutions with alerting systems for discrepancies allow you to monitor anomalies in the data from anywhere. Perhaps your cloud database is simply out of date. Or maybe a true error exists. Alerting flags irregularities and lets you deal with them on your own timeline.

 

Validation across all data Once a migration is complete, it’s important to run a final, database-wide validation. This will ensure that your data is 100% ready for your BI and analytics team.

Many companies run on-premise and cloud databases in parallel until all of these data quality measures are completed. Although many businesses continue to run cloud and on-premise databases side-by-side, some companies will find this unnecessary. Set a deadline to shut down the on-premise system if this is your objective.

6. Maintain Data Continuity

A continuous migration keeps your cloud data warehouse synced with your database. This enables you to manipulate and analyze up-to-date data in the cloud. Your team is more effective and efficient when the right data is at their fingertips.

Your team’s requirements will determine the type of continuous migration you use. Does your team need the data updated every hour? Every day? Every week? What if the data needs to be instantly synced?

For scenarios that do not require real-time data use a pipeline scheduling tool. Set the scheduler for a fixed time interval or for specific time(s) throughout the day. Example: A retail chain schedules the extraction of daily revenue data from each store at closing time to calculate overall revenue.

For scenarios that require real-time data change data capture instantly and automatically syncs a database with your cloud data warehouse. By analyzing a database’s binlog, change data capture detects data changes as they happen, and immediately syncs these changes with your cloud DWH. Example: a healthcare startup that licenses heart monitors oversees each patient’s vital signs in real-time using change data capture.

You Have the Tips. Now You Just Need to Start!

Cloud data migrations can be stressful, but if you approach the project on a piece-by-piece basis, the process is more than manageable. In this ebook, we’ve outlined key tips that we’ve learned from our own experience. This advice will steer you in the right direction as you look to migrate to the cloud.

However, every migration is different. What works for some organizations might not work for others. Flexibility is key, and you should not view the steps in this guide as must-dos. They are guideposts to help you along the way. Please reach out to Rivery if you have any questions about your specific use case.

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