In our previous blog, we discussed cloud migration caveats looming over institutions. Getting through the next quarter century of cloud success and steering clear of these challenges requires a digital reckoning. New digital mediums will require more than just clever coding and mass collaboration. It is not just ‘internet of information’ but ‘internet of value’ that application leaders should focus while moving legacy systems to the cloud. Businesses can benefit in profound ways if they can restructure the vertically integrated hierarchies for greater value creation and innovation.
For good reasons, there is an unquenchable desire to move applications to cloud or adopt cloud-based technologies. However, the cloud-based structures are constantly being re-invented for shifting paradigms and these changes often blur the vision of reaching the halcyon age.
Let’s take the example of Internet of Things (IoT). Where a large number of smart things will be collecting data involved in manufacturing, hospitality, eCommerce, supply chain, etc. There will be hidden insights in this data that can be leveraged to explore new opportunities. Business leaders need to envision a new IT model that in-turn will create an appetite for processing this colossal amounts of data.
It is about time that companies should adopt an integrated enterprise architecture that performs polyglot programming for:
Winning or losing with cloud computing heavily depends upon how it is approached. Successful enterprise architects follow some ground rules while hosting or rehosting applications on cloud. Following these guidelines can help in streamlining application lifecycle, keeping the process collaborative, and avoiding legacy system integration pitfalls.
Overcoming Legacy System Integration Challenges with Best Practices
Setting up a Centralized Environment: Previously, developers used to develop flows and send them over to testing teams for validation. The testing team used to raise flags in case of errors and send back the flows to development teams. This approach is not ideal where teams are working with wide team structures, different underlying technologies, architectures, and code bases. Development teams struggled as a lot of coding was required to build just few workflows.
Collective computing in a central environment can reduce this coding effort by 90% and help teams in creating modular design systems. Developers can see their codes in real time which they are writing. This is how:
That’s why, application leaders need to set up an environment that allows engineers to switch environments where several stove-piped and cloud-based applications run parallelly. Such an environment allows the development team to pull objects locally without even the need of accessing GitHub. In a centralized environment, teams can collaborate effectively, solve problems faster and accelerate development faster. Teams can bring together composite systems, people, and services and structure deployment deadline effectively. Users can move the processes and systems wherever they need and respond faster to changes. Moreover, both new and old paradigms can run parallelly and the old system can be disposed once new application is setup. In this way, organizations can achieve 300 times more deployments, 50X faster recovery, and 30% rework.
Tackling Data Loads and Latency Issues: Data Loads and latency issues are underlying problems and reducing them is essential for accomplishing core business objectives. Organizations need to consider a legacy system integration plan. Data integration tools should be used for migrating petabyte-scale data warehouses. A next-generation integration tool can simplify large data processing from new source systems at minimal cost. On top of that, scalability can be achieved through dynamic capabilities for full extract with de-duplication logic.
Organizations should have a good understanding of source data formats. Full data audit and data validation can minimize code amendments by 80%. Application leaders should audit data at every end and address potential problems at an early stage. This will ensure that no data is left behind during migration and all reports have been moved properly with integrity. For example, legacy & untouched data might use different currencies than Euro. Such data needs to be converted to Euros when it is moved from old systems to new systems.
Minimizing Downtime Issues: There is hardly any organization which can continue business operations despite of downtime. Modern-day migrations need heavy lifting capabilities to reduce downtimes. A step by step by approach to data migration is always preferred instead of short time window approach that completes a migration in fell swoop. A phased approach ensures zero downtime that today’s mission-critical applications require.
Smarter organizations allow real-time processes to safely migrate data. The processes successfully maintain changes to the data and pass it safely to the systems. The data can be constantly migrated. The cloud migration process can be divided into a number of stages:
For simplifying this part, organizations can collaborate with an outsourcing partner who can allow in extracting data and reduce network downtime. Efficient cloud migration systems provide flexibility to move data from any source to any target and minimize cutover window times.
Creating a Pathway for Data Transformation: Transforming the extracted data in a comprehensive format is the next big challenge in cloud migration. Companies spend a lot of money and time while converting data from one format to another during data movement. Due to this, organizations fail to leverage data for big data and analytical purposes.
Data quality can be improved if it is represented in semantically-enriched form for big data and analytical purposes. A resource description framework should be prepared for identifying the data conversion needs and preparing data for analytical needs. The focus should be more on making the transformation user-friendly. Error-free conversion ensures zero data loss, better decisions, and better business outcomes.
Data Security: Everything boils down to security for organizations hosting their applications on the cloud. Web application security throws entirely new challenges before organizations. The key to secure cloud IP addresses is making them malleable and tangled. Dense layers successfully hide cloud data from threat actors. Threat actors primarily target static IP addresses and break the firewall. However, flexible IP addresses can safely store data when it is disguised in public cloud among thousands of other IPs. The IPs can also be changed if it is discovered by hackers.
Another best practice is to layer the data with multi-layer authentication. The same applies to IoT as well. And IoT can be further strengthened if organizations ensure the following things while selecting IoT devices:
Organizations which believe that public cloud is absolutely safe are putting their head in the sand. Patching the weak endpoints requires a continuous understanding of joining networks. Regular audits should be conducted to update security procedures in place. Organizations will also need orchestration between IT tools to pre-empt security threats in early stages. Security information and event management (SIEM) plans must be prepared to take out risks and explore cloud opportunities with confidence.