Software and data dependencies are crucial for efficient programming with components. However, they can slow down agile teams and make deploying, updating, and migrating to new software difficult. Sometimes, applications and their data have many dependencies, making the problem even trickier.
Dependencies are like building blocks that provide the necessary functions for a main component. They get added to code using service dependency mapping tools like npm or Maven, stored in Git on platforms like GitHub, or put in containers on platforms like Docker Hub.
Application and data dependency mapping involves finding and listing the connections between different parts of an application and its data, their dependencies, and the underlying infrastructure. Creating this map is essential to recognize the complex structure of applications and know how they behave in different situations.
In this blog post, we explain dependencies, their role in application migration, and why they matter. We’ll also highlight real-life examples of companies that have managed application dependencies effectively.
Role of Applications in Migration
Applications are the heartbeat of any organization's tech setup, handling everything from data processing to business functions. Migrating from one system to another isn't just about shifting data—it's also about the smooth transition of a network of interconnected applications that comprise the core of a company's technological framework.
Applications contribute significantly to a successful application migration in a few ways:
- They not only contain data but also the rules according to which they are used. Clarifying these details is crucial for a smooth move without any loss of functionality.
- Applications are like buddies that rely on each other. During migration, spotting and managing these connections is crucial to avoid hiccups and keep the entire system intact.
- Users are used to these applications, so a good migration means they can switch to the new setup without too many complications. In short, applications aren't just tagalongs; they are active players in the migration, which must be carefully planned to make the move successful.
What is Application Dependency Mapping?
Forrester's report found that companies doing IT projects like virtualization and server consolidation hit roadblocks. Why? Well, 56% didn't see the full picture of dependencies, 36% weren't sure about the resources various applications needed, and 31% lacked the lowdown on all the applications used. That's a lot of blind spots.
That's why it's so important to have an overview of your business, including knowing how everything is connected, having solid data, and understanding the dependencies of applications—these are the most important pieces of the puzzle for today's IT.
Application dependency mapping is like putting together a jigsaw puzzle for IT managers. It helps them see the bigger picture of how everything works in their tech world. If something goes wrong, this mapping is like a treasure map that shows where things went wrong and how to fix it as quickly as possible.
Think of every application, from a fancy online store to a simple email setup, as a complex machine with many moving parts. When an application falters, dependency mapping shows you where the problem lies and how to fix it. And if an application isn't working, a good mapping tool is your compass, pointing out where things are disconnected or what needs swapping.
If your map is missing parts or outdated, good luck finding and fixing the problem. Maybe there's a hidden workload that is maxing out an application you rely on, and if you don't find the issue, you're screwed. But with a complete overview of your IT setup, a clear map of how everything is connected, and solid data, you'll be okay.
According to an EMA report, the best IT managers typically have some kind of application discovery and dependency mapping (ADDM) in place.
Different Types of Application Dependencies
There are three main types, each with its own role in how the whole system behaves.
Code Dependencies
What it is: This is like the connections between different parts of the source code of a program. It's about bits of code that rely on each other to fulfill their task.
Why it matters: Applications often use external helpers such as libraries or frameworks. Code dependencies show why it's important to keep track of these and ensure everything works well together when you update or move things.
Runtime Dependencies
What it is: It’s what happens when the application does its thing. It's about what the application needs to work properly while running.
Why it matters: Think about databases, external APIs, or certain server setups — knowing and handling these dependencies helps the application run smoothly and communicate with the outside world.
System Dependencies
What it is: This includes things outside the application's code, like the operating system, hardware, and network setups.
Why it matters: Understanding how the application fits into the bigger picture is essential. It must cope with different operating systems and work with various setups to be useful in different situations.
Impact of Application Dependencies on Migration
Here are the examples of application migration challenges caused by unaddressed dependencies.
Data Loss
Challenge: During application migration, some data might not make it across, and you could lose it for good.
Solution: Create a solid data backup plan before migrating. Testing during the process ensures all historical data survives, keeping your reporting and analysis intact.
Semantics Errors
Challenge: Even if data transitions well, there can be hiccups in how it's organized, leading to inaccurate reporting.
Solution: Conduct thorough data migration testing, especially with large volumes of existing data, to catch and fix these issues.
Extended Downtime
Challenge: If application migration takes longer than expected, your system stops working and you lose vital business insights.
Solution: Choose a migration partner committed to minimal downtime. Consider working with teams in different time zones for non-stop progress.
Data Corruption
Challenge: Unwanted data types in the new system can lead to system crashes or messed-up data.
Solution: Ensure your migration team audits data types and sources before diving into the project.
Application Performance
Challenge: The target platform might act up due to poor code, bugs, or issues with data migration tools.
Solution: Pick the right tech stack for migration. If your team lacks experience, bring in experts to choose tools aligned with your workload and goals.
Data Orchestration
Challenge: Without proper planning, you might lose track of data points, creating incomplete data migration.
Solution: Plan thoroughly, focusing on business processes and conducting audits before application migration.
Integration Problems
Challenge: Ineffective migration strategies can disrupt access to insights from integrated data sources.
Solution: Clearly communicate business goals and ensure the target system integrates seamlessly with other tools.
User Training
Challenge: Shifting to a new system without proper training can lead to human errors and incorrect data handling.
Solution: Conduct staff training to ensure a smooth transition and avoid disruptions caused by errors.
Data Security
Challenge: Migrating data exposes it to third parties, and the new system may have vulnerabilities.
Solution: Define security requirements, choose a system that meets your needs, and develop a robust data security strategy.
Data Quality
Challenge: Migrating low-quality data can impact system performance, leading to bugs and errors.
Solution: Cleanse data before application migration to ensure high quality and implement a thorough migration process.
Tackling data migration risks involves proactive planning, thorough testing, and strategic decision-making. From safeguarding against data loss to ensuring system performance, a well-prepared approach minimizes disruptions and maximizes the benefits of migration.
Real-World Examples of Successful Migrations with Application Dependency Management
Here are examples where proper application dependencies management led to successful data migration.
Salesforce to AWS Migration
Challenge: A leading global technology company with vast amounts of data stored in Salesforce needed to migrate to Amazon Web Services (AWS) for scalability and cost-effectiveness.
Solution: The successful migration involved thorough application dependency mapping between Salesforce objects and AWS services. Proper dependency management ensured that data integrity and relationships were maintained throughout the migration process.
Result: The company achieved improved performance, scalability, and cost savings on AWS.
Legacy System to Microservices Architecture
Challenge: A financial institution faced challenges with its legacy monolithic system, hindering agility and scalability. The decision was made to migrate to a microservices architecture.
Solution: Proper dependency management involved breaking down the monolithic application into smaller, manageable services while identifying and addressing dependencies. This ensured that each microservice could be developed, tested, and deployed independently.
Result: The migration allowed the institution to respond swiftly to market changes, improve system reliability, and scale components independently.
Google Apps to Microsoft 365 Migration
Challenge: A multinational corporation using Google Apps decided to migrate its email, collaboration, and productivity suite to Microsoft 365 for enhanced features and integration.
Solution: Comprehensive dependency mapping was carried out to understand the relationships between different Google Apps components. This information guided the migration plan to minimize disruptions and data inconsistencies during the transition.
Result: The successful migration improved collaboration and communication across the organization, leveraging the capabilities of the Microsoft 365 migration application.
In each case, successful data migration was attributed to meticulous dependency management, ensuring a thorough understanding of the relationships between different components before, during, and after the migration process. This approach minimizes risks, ensures data integrity, and facilitates a smoother transition to new technologies or platforms.