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Is your brand still creating tech packs in Illustrator or Excel? Don’t panic; you’re not alone. For the longest time, both Illustrator and Excel were the tried-and-true solutions for tech pack creation in the fashion and apparel industry — and rightfully so.
These programs have always been dependable, feature-forward tools used to manage product design and development. However, like any esteemed software, we find caveats to working in these programs as technology evolves.
The fact of the matter is, creating tech packs in these manual platforms limits your brand to operate using static data.
In the world of computer science and programming data is classified into two main types – static and dynamic. The key difference lies in how and when the data structures are allocated memory and whether their size can be modified during execution. Let’s take a closer look at what sets static and dynamic data apart.
What is Static Data?
Static data has a size and structure that is fixed at compile time, before the program is executed The program allocates memory for static data structures and arrays when it is compiled
Some key properties of static data are:
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Fixed size – The amount of memory allocated cannot be changed while the program runs.
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Stored in stack – Static data typically resides in the call stack.
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Lifetime – Static data exists for the duration of the program execution. Once allocated, the memory is not freed until the program terminates.
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Access – Static data allows random access to elements using indices. Access time is fast due to fixed size and location.
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Examples – Arrays, queues, stacks, hash tables with fixed size.
Static data works well for information that has predictable size requirements and is not expected to change frequently. It provides efficient access and memory usage when the data volume and behavior is known in advance.
What is Dynamic Data?
In contrast, dynamic data structures have a size that can be modified during runtime. The memory is allocated dynamically from the heap while the program executes.
Key properties of dynamic data:
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Variable size – Size can shrink or grow as needed during execution.
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Stored in heap – Created in heap memory which is less organized.
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Lifetime – Allocated and freed while program runs, exists as needed.
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Access – Direct access using indices is not possible. Requires pointer traversal.
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Examples – Linked lists, trees, hash tables.
The main advantage of dynamic data is flexibility. It can adapt to changing storage requirements. Dynamic data is preferred when the size or lifetime of the data is not known ahead of time.
Key Differences
| Factor | Static Data | Dynamic Data |
|---|---|---|
| Storage | Stack | Heap |
| Size | Fixed | Variable |
| Lifetime | Entire execution | Created/destroyed as needed |
| Access | Index-based, fast | Pointer traversal, slower |
| Memory allocation | At compile time | During execution |
| Use cases | Predictable size/behavior | Unknown size/behavior |
To summarize, static data works best when the size and behavior of the information is predictable, while dynamic data is better suited for flexibility when requirements change.
When to Use Static Data?
Here are some cases where static data structures are recommended:
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The size of data is known in advance and unlikely to change. For example, days in a week, months in a year.
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Fast indexed access to elements is required. For example, accessing pixel data in an image array.
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Tight control over memory usage is needed. Static allocations have lower overhead.
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Simplicity is important. Dynamic memory allocation requires error checking and management logic.
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Speed and performance are critical. Static data is faster to access than dynamic structures.
When to Use Dynamic Data?
Dynamic data structures are preferred in these scenarios:
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It is hard to predict data size. For example, user contacts in address book app.
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Data needs to expand and shrink on the fly. Like gaming levels or ecommerce carts.
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Insertions and deletions from arbitrary positions are frequent. Such as playlists or document edits.
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Flexibility outweighs small performance overhead. For non-critical applications.
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Custom memory management code is acceptable. To reuse memory and reduce fragmentation.
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Data has complex relationships like graphs and trees. Dynamic links help model connections.
Examples and Use Cases
To help illustrate real-world uses, here are some examples of static vs dynamic data:
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Hash table – Static hash tables allocate fixed buckets. Dynamic tables expand buckets on collisions.
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Strings – Static strings have fixed max length. Dynamic strings can append indefinitely.
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Arrays – Static arrays initiated with set size. Dynamic arrays resize as elements are added.
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Matrices – Storing pixel data uses static 2D arrays. Dynamic matrices allow variable rows/columns.
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Nodes – Static linked lists limit node quantity. Dynamic lists create nodes as required.
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Language dictionaries – Static hash tables for small fixed dictionaries. Dynamic hashes for expansive dictionaries.
Design Considerations
Some key points when deciding between static and dynamic data structures:
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Profile the data and access patterns upfront to pick appropriate structure.
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Consider future requirements. Will the size or usage change over time?
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Dynamic data has memory and performance overhead that may be unnecessary.
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Reevaluating requirements over the course of development can guide structure decisions.
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Language support and available libraries influence options for managing dynamic data.
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Resource constraints like memory or computing power may dictate static data only.
By understanding the inherent trade-offs, developers can intelligently choose the right data structures for their programs and use cases. The choice between static or dynamic affects flexibility, performance and complexity. Carefully weighing the options during design helps build robust and optimized applications.
Backbone Beats Manual Tech Pack Creation
Now that you know what separates static data vs dynamic data; let’s unravel this concept further and discuss how Backbone outmatches manual tech pack creation.
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What is Dynamic Data?
Whereas manual spreadsheets provide static data that remains the same after it is collected, Backbone provides dynamic data that continually changes throughout the product development cycle. Everything in Backbone’s system is interconnected, which means product insights such as size specs, component libraries, BOMs, line sheet reports, and entry-level summary data automatically populate throughout the system.
If spreadsheets are the VCR of product development, then Backbone is your favorite streaming app. Grab the remote and scroll through countless TV shows and movies tailored to your unique interests, and watch the system automatically update with the latest content every time you log in. Backbone operates similarly for designers and developers to organize and filter the exact information they need to see their products come to life. With the click of a button, product specifications update everywhere data points are connected, making it much easier to create templates and build or share tech packs with factories, your team members, or anyone else in minutes.
Built-in repositories connected to dynamic data allow brands to create components and utilize custom fields in numerous ways. Product developers can choose to bulk upload, search supplier names, add variants, and drill down specific component attributes. Once component-level information is uploaded into the system, it can be viewed in an Excel-type format that requires no copy-pasting, saving valuable time tracking leads and approvals.
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Whiteboard: Static vs Dynamic Data
What is the difference between static and dynamic data structure?
Static data structure is given a fixed area of memory which it can operate within. It is not possible to expand this fixed size in the run time. So that, locations of each element is fixed and known by the program. Dynamic data structure also has an area where it can operate.
What is static data?
Static data is a data set that remains the same after you record it. Static data is unlikely to change or may do so only rarely, and includes the names of continents, established mathematical principles and scientific terms. Sometimes, static data may refer to a data structure.
What is static data structure?
In Static data structure the size of the structure is fixed. The content of the data structure can be modified but without changing the memory space allocated to it. Example of Static Data Structures: Array What is Dynamic Data Structure?
Is dynamic data better than static data?
Static data doesn’t account for these changes. But, dynamic data does—updating in real time as these changes occur. With dynamic data, you can confidently analyze your customer base and create personalized experiences to ultimately increase customer lifetime value and reduce churn.
