The Automated, No-Code Data Stack
Learn how Astera Data Stack can simplify and streamline your enterprise’s data management.
Data Migration to Cloud Best Practices for 2024 and Beyond!
While migrating to the cloud can provide businesses with a sustainable competitive advantage, strategy and implementation are where they can make mistakes. Over a $100 billion migration spend waste is expected in three years. This waste is majorly a result of unnecessary cost and delays. If you are planning a data migration project for your business, here are 7 data migration to cloud best practices to help you develop an effective cloud migration strategy.
1. Research and plan your cloud migration journey
Research and planning are one of the first steps that organizations take. Still, rushing cloud adoption without due diligence is one of the biggest mistakes businesses make. It is best to do an assessment and have a plan. It is not as simple as, “Hey, let’s migrate all our data and applications to the cloud; I heard it’s pretty easy with Snowflake.” In fact, Kent Graziano, a thought-leader in the cloud data warehousing space, says, “the contracts are not even that easy because there is proper contract planning that you need to undertake before you begin the migration project.”
Businesses need to understand the current state and, more importantly, the desired future state they want to reach. The migration plan should be able to answer the questions that are on every data steward’s mind, “what business problems will a cloud-based data warehouse solve?”, “will there be security and compliance issues in the cloud that we currently do not have?”. “Are there business problems we cannot solve with an on-prem infrastructure?”.
Maybe you want to get rid of the leases on your data centers or change the staffing to be more remote. The real question to ask is, why are you doing this? Identify those problems and prioritize them.
Ensure your migration plan incorporates all the factors.
Another key factor to consider at the planning stage is the necessity of legacy systems. As Kent has highlighted, in many cases, research can reveal unnecessary systems and processes that have already been superseded by newer source systems and data, yet they are still running. Nobody even turned them off because they had already been paid for, and no alerts were set up for shutting these systems down. Anyone could go and shut them off, and no one would notice.
Undertaking proper planning and conducting a thorough assessment will help prevent all these obsolete source systems and data from becoming a part of your new data architecture.
2. Don’t do a big bang; adopt a phased approach
While many organizations prioritized the big bang approach to development in the early days of data warehousing, these drawn-out implementations should be avoided. Unfortunately, this is one of the major mistakes businesses are still making, and this is where a lot of their budget is being wasted. People think it is a simple lift and shift where all the data, and tools, in fact, the entire on-prem infrastructure, are ported to the cloud. How hard could it be, right?
The fact of the matter is that on-prem infrastructure relies on tools powered by older technologies, and these tools are not compatible with the cloud environment. So, there is much more that needs to be done; in fact, many of these legacy tools need to be replaced. Therefore, businesses should consider the incremental approach instead.
The ability to perform tests quickly and economically since cloud solutions function on the on-demand model is one of the biggest reasons businesses should consider the incremental approach. Instead of buying additional compute resources, as in the case of an on-prem infrastructure, all the users have to do on the cloud is sign up for the relevant services and test them out for a few days to see if they perform accordingly. This saves a lot of time, effort, and resources.
3. Know where your data is
Besides knowing that you need to move to the cloud, you have to know what you have. Not only do you need to be clear on the business problem that you are trying to solve, but you also need to be aware of the dangers that could arise from a proliferation of data silos.
Ensure you know what data you have and where it lies.
So, it is important to ensure that enterprise data is only moved to the relevant destination and does not end up where it should not be. This is also why lift and shift is not the correct answer in most circumstances unless we are talking about small businesses with a single, well-documented on-prem data warehouse.
Moreover, it is essential to fully understand the data map when there are, on average, 115 data sources feeding your data pipeline before starting with the data migration project.
4. Migrate only what needs to be migrated
Resolving data quality issues before transitioning to the cloud is one of the key data migration to cloud best practices. Businesses should be able to define what data quality means to them and also measure it. For example, when 1,000 rows are onboarded from a source system to the cloud, and all the rows have migrated to the cloud, you have an excellent data quality process. However, if you only see, say, 800 rows, that’s probably a problem. In essence, without even looking at the data, you have a data quality problem.
So, it is crucial to set guiding principles that define what data quality means to the business. It is equally important to understand that migrating to the cloud itself does not solve any data quality issues. If there are data quality issues with your on-prem infrastructure, they must be resolved before migrating to the cloud environment.
Ensure you resolve all data quality issues before embarking on your cloud migration journey.
According to Koen Verbeeck, it’s mostly crap in, crap out, as far as data quality is concerned. This essentially means that most data quality issues arise at the source level, and that is where they should be resolved to ensure that your data migration project does not carry poor quality data to your new data warehouse.
With the cloud and current technologies, businesses expose their data faster than ever. There has to be a data quality feedback loop with the business to ensure that the data exposed through reports meets business requirements.
5. Leverage automation for agility
Automation is becoming a key ingredient for migration to the cloud and even the hybrid data infrastructure. Because the lift and shift approach rarely makes sense, businesses should take the incremental approach or design the solution from scratch, i.e., opt for a greenfield development.
As part of the data migration to cloud best practices, automation helps with the change in mindset that is necessary to embrace the cloud. Your data teams can be more agile since cloud environments allow for easy testing and mitigate the fear of making mistakes. For example, by automating data discovery modeling, teams enable the business to be mindful of everything it has on-prem so it can try out the cloud environment. Automation takes this information to the cloud, enabling businesses to see if it performs as expected.
Ensure you drive agility using automation.
Not only does automation help with schema discovery, but it also automates the building of joins for reading and data pipeline building for loading data.
Traditionally, data quality was treated as a separate process, which was not seen as a part of the data pipeline. Modern tools allow users to embed data quality rules as per business standards along the data pipeline, ensuring that only healthy data reaches the data warehouse. With automation, the entire process needs to be done once, and thereon, your data pipeline is set to consistently deliver high-quality data.
6. Determine if you need a persistent or a transient staging area
Data lakes and data warehouses are not technologies; they are concepts. There has always been the concept of a staging area in the data warehousing world. Some cases warrant the need for a persistent staging area (PSA) where the data history is retained. At the same time, other situations call for a transient staging area (TSA) where the data is wiped after being loaded into the data warehouse.
Ensure your staging areas deliver value to your business.
Many businesses onboard raw data from source systems to a data lake before transforming and loading it into the data warehouse. However, the important thing to consider is the value the business is looking to derive from this staging area. While there is always a cost involved, the return on investment and total cost of ownership are the most important metrics to consider.
One of the reasons why businesses find staging areas valuable, especially the PSAs, is because they can onboard raw data at a significantly faster rate than they can produce curated data. The data analysts can then dive deep into this raw data and identify not only what needs to go to the data warehouse but also what needs to be loaded first. Additionally, having a staging area offers auditability and traceability because most source systems do not maintain history.
If you are running a business in the financial or healthcare industry where the data has a significant impact, you need to be able to trace your decisions through the curated data back to the raw data. And, if you don’t have that raw data lake or the PSA, you might not be able to get back to the raw data that feeds your data pipeline. This means you will never figure out if one of the transformations applied was wrong.
7. Implement billing alerts to avoid unexpected costs
This cannot be stressed enough, but even if you are just testing out the cloud environment, ensure you set up billing alerts. Companies just start with the cloud and set up assets and services only to realize later that they need only minimal storage/compute resources or that it simply does not work for them. Often, companies do not realize that it keeps costing them money because they fail to shut down those cloud services properly. So, one of the first things to do while migrating to the cloud is to learn and implement billing alerts.
Deciding to migrate to the cloud is only a part of the entire journey — the easier part. It is the planning and execution where most businesses start to compromise their return on investment (ROI) and increase the total cost of ownership (TCO). The good news is, you can easily mitigate most of the risks and save on costs by following these data migration to cloud best practices.
Astera Centerprise – The End-to-End Cloud Migration Champion
Besides the ability to accelerate your cloud migration journey with automated data pipelines, Astera Centerprise offers the freedom of deploying your data warehouse to the platform of your choice. Centerprise streamlines the data mapping process with its intuitive point-and-click user interface. Data quality is embedded into the architecture to ensure that only healthy data reaches your new data warehouse.
Looks like you have made it to the end, and you are keen to learn more. Schedule a demo and see how implementing these data migration to cloud best practices can help your business embrace the cloud on budget and on time.
Authors:
Khurram Haider
