Migrating to the cloud offers multiple benefits. Organizations looking to modernize can reap enormous advantages that range from decreased licensing and storage costs to increased performance and scalability. Migrating to the cloud enables organizations to advance their data analytics and even drive new revenue streams.

Of course, detailed planning and preparation are a must if you want to increase the success of your data migration. And choosing the right data migration strategy is a key part of this process.

I’ve participated in countless data migrations. Below, I’ll overview the four approaches that I most often see, as well as their advantages and disadvantages. For additional help choosing the right strategy, be sure to check into a data migration assessment.

1. Lift and shift approach

The “lift and shift” is the most basic data migration strategy. Organizations use this approach when taking the same database technology that they’re using on premise—say, an Oracle database—and shifting it to the cloud.

The main advantage of this approach is its simplicity and speed and the availability of tools that can facilitate the migration. But keep in mind that when you lift and shift an existing database, you’re also moving all of its hidden problems, hacks, and band-aids to the cloud. The technical debt you had on premise will still be an issue in the cloud—sort of like moving out of a roach-infested house and taking the roaches with you to your new home.

2. Data lake approach

The data lake approach involves removing data from an on-premise database and storing it in a solution such as Amazon S3. For this approach, you can employ a variety of available technologies to access, analyze, and query the data. This approach also gives you the ability to load the data into your database of choice, such as Redshift. Plus, because there’s a middle ground where the data sits, you can load it into the target database in parallel.

This approach offers the flexibility to use any database. While it does require redesigning your database to some degree, you can make changes to the data along the way and increase the performance in your target database. Additionally, because you are building something new and modernizing your database application, you can exterminate those roaches.

Additionally, this approach enables you to separate storage from compute, so you can take advantage of newer compute technologies as they emerge. There are also cost advantages of separating data that is accessed seasonally from data that is accessed regularly. A data lake allows you use compute power to handle those spikes and shut if off when the data isn’t being used.

Of course, there are some tradeoffs. Direct queries against a data lake are typically slower than queries against a database. In situations where you have a higher performance requirement, it makes sense to load that data into a higher performing database. But you can still load your data from the data lake into a tool that allows that high performance. Another tradeoff is that data lakes introduce a new set of technologies to engineers and DBAs who must contend with a steep learning curve. This new domain of knowledge requires considerable ramp up.

3. Combo approach: Lift and shift + data lake

Sometimes, there is a pressing business need to migrate—such as a datacenter that is shutting down. The timeline is short, and the organization needs to get the database off premise quickly—before migrating to a new database solution.

In this hybrid approach, you can combine the above-mentioned strategies, first using the lift and shift approach to migrate the existing database to the cloud, then, taking the time to migrate to a new database such as Redshift or Aurora.

In addition to getting to the cloud quickly, this approach gives you breathing room to determine the best database to be used for your data. The disadvantage is that in some cases, it ends up creating more work in the long run, since you have to make your database work in the cloud. And then you have to migrate to a new database technology.

4. Hybrid streaming approach 

In some cases, an organization wants the benefits of a data lake in the cloud, but for business reasons must keep an existing transactional database on premise. Using change data capture (CDC), you can enable changes on premise to stream into the cloud in near real-time. By taking a streaming approach to getting data into the cloud, you can get benefits from the cloud without moving the database. On the downside, replication from the on-premise database is more prone to failure since typically the same redundancies aren’t in place.

Choose the right strategy with a data migration assessment

Choosing the right data migration strategy is critical to your overall success, and depends upon business requirements and drivers, pain points, performance needs, short-term plans, future goals, and multiple other factors.

A data migration assessment from Beyondsoft applies a holistic approach to preparing for a data migration and provides a sound foundation and blueprint for completing your database and application migration. Our proven execution model addresses areas such as business applications and workloads, technical operations, production support, and training to determine the best migration architecture and strategy. Our experts work with you to perform a detailed analysis of your existing database and application architecture and create a roadmap for your migration that includes business, functional, technical, and testing requirements as well as recommendations for addressing gaps. We also create an ROI model that demonstrates performance measures and the time it will take to recoup the cost of your migration.

Here at Beyondsoft, we’ve performed countless database migrations and big data projects for large enterprise customers around the globe leveraging technologies such as PostgreSQL, Amazon RDS, Amazon Redshift, Vertica, Presto, ConvergDB, and more. A data migration assessment is a worthwhile investment that will save you time and money down the road. To learn more, talk to one of our data migration experts.

Data migrations are a popular undertaking these days. In fact, according to the IDC, data migrations comprise around 60% of all large enterprise IT projects. Although the reasons can vary, data migrations are typically motivated by cost reduction, performance, or a combination of the two.

From a cost perspective, large databases and data warehouses can get expensive and licensing fees can escalate out of control. Meanwhile, migrating to less expensive cloud-based solutions can save substantial money.

From a performance standpoint, legacy platforms that are approaching end of life can be a forcing factor. Decoupling data from compute and leveraging cloud capabilities can increase agility and performance. Finally, some enterprise organizations are looking to monetize data and expand market share by taking advantage of more modern platforms.

Ultimately, enterprises that want to compete effectively and drive business growth must embrace modern, more agile solutions. Yet, for all its benefits, a data migration can be a complex and time-consuming undertaking, fraught with the unexpected. Plus, for the uninitiated, it can be hard to know where to start.

The good news is that the proper planning and preparation, as well as choosing the right migration tools, can reduce risk, streamline your data migration, and optimize performance and return on investment (ROI). In this article, I want to explore a few common data migration obstacles and pitfalls and outline steps you can take to minimize risk and increase the success and speed of your data migration.

Common obstacles 

One key obstacle to moving forward with a data migration is lack of internal expertise. Most organizations simply don’t have the skillsets to perform a migration in-house. Hiring dedicated internal experts can be cost-prohibitive. Furthermore, even if an organization has the in-house talent, they’re likely consumed with day-to-day, business critical tasks and don’t have the time to devote to a migration.

Many organizations don’t have methods to obtain a clear data migration path and ROI, which means they’re mostly focused on the price tag instead of increased performance and future cost savings. Additionally, many organizations haven’t pinpointed the desired outcomes and performance expectations of their data migration, which can set them up for headaches down the road.

Finally, many organizations simply haven’t had exposure to multiple platforms, making it difficult to choose the best solution. We’ve seen enterprise customers default to the platform that they’re most familiar with only to be disappointed with the results and forced to consider a second migration to a more suitable platform.  In this case it is better to understand future business requirements before solutioning.

Common pitfalls 

Without the proper planning and expertise, many things can go wrong. From a business standpoint, there is tremendous pressure on the product owner who can face financial and reputational risks caused by data losses, frequent or extended downtimes, and security issues. To minimize these kinds of issues, it’s critical to establish clear expectations and success criteria around delivery timeframes, expected downtimes, data validation, cost optimization, and long-term post-migration support.

From an ROI standpoint, many organizations fail to prioritize properly, leading to disappointing time to value. It’s important to remember that database migrations also have applications attached, and when there are a large number, the entire effort can be very time consuming, not to mention costly. Choosing the most profitable application sets as top-priority migration candidates can deliver short-run profit objectives and long-term benefits with cloud innovations.

Additionally, even with thorough planning, there will always be unknowns. When I talk about performing a data migration, I often use the metaphor of remodeling a house. While much of the remodeling work is obvious, it’s what’s behind the walls or under the floor joists that can be most challenging. Whether it’s faulty wiring, mold, or structural issues, once you begin tearing down walls, you’re sure to come across some unknowns that cost time and money to correct.

The same is true for a data migration. For example, ETL and the way in which data has been blended from multiple sources can be difficult to contend with. You may find stored procedures and business logic that is tied into the database that you weren’t expecting. Depending upon the age of the database, you may have had multiple DBAs doing the work, and you may find yourself unraveling what they’ve done, without any historical knowledge to understand the full impacts.

Finally, when migrating from one database or data warehouse to another, it’s critical to understand the business logic and stored procedures that exist between the two. There are tools-based solutions available that can identify these migration patterns for you and automate a substantial portion of the migration process. These tools can help speed up the migration, increase accuracy, and minimize risk of human error. However, without in-depth experience and knowledge of data migrations, it can be hard to know which tools to use and how to use them. And not having the right tool can add several months to a migration.

Why you need a data migration assessment

These obstacles and pitfalls can impact not only your budget and timeline, they can also impact the overall outcome. Which is why the first thing that any enterprise organization should do is to engage with an industry expert like Beyondsoft to perform a thorough data migration assessment.

A data migration assessment from Beyondsoft provides a solid foundation for completing your data, ETL workflow, reports, and applications migration. Our proven execution model addresses areas such as business applications and workloads, technical operations, production support, and training to determine the best migration architecture and approach—including the selection of tools to accelerate your migration.

Our experts work with you to understand your business needs and objectives and develop a clear business case for initiating a migration as well as expected outcomes. We work with you to define specific success criteria to enable on-time delivery, minimize customer disruptions, ensure data accuracy, optimize costs, and smooth the post-migration transition through support and training.

As part of our assessment, we perform a detailed analysis of your existing database and application architecture, including schema, objects, code, and data volume and create a clear roadmap for your migration that includes business, functional, technical, and testing requirements as well as recommendations for addressing identified gaps. Because we have experience with multiple migration patterns, we can quickly identify stored procedures and business logic and pinpoint the right tools-based solution to accelerate your migration.

We also work with you to prioritize migration candidates using a proven, best practice approach. We examine factors including application size, complexity, cost, savings, and overall effort to determine the candidates that deliver an ROI model that maximizes migration time to value while also setting you up for long term success.

As a trusted enterprise business partner, Beyondsoft has successfully delivered on countless database migrations and big data projects. Our experienced, global experts are deeply familiar with technologies such as PostgreSQL, Amazon RDS, Amazon Redshift, Vertica, Presto, ConvergDB, and more.

A data migration assessment is a worthwhile investment that will save you time, money, and frustration down the road. To learn more, talk to one of our experts.