Two parallel lines in a data flow diagram show where data is stored for later use

Two Parallel Lines? What They Mean on a Data Flow Diagram

Let me ask you something practical: when you see two parallel lines on a diagram, what do you think they’re saving for later? If you guessed “data store,” you’re spot on. In data flow diagrams (DFDs), those twin lines are a signal that data is being kept somewhere—waiting, ready for future use. It’s a tiny symbol with a big job: showing where data sits between actions and places data can be retrieved or updated.

What a data store actually does

Think of a data store as a pantry in a kitchen. A chef (that’s your process) might pull out flour to bake, or put stored sugar back for later. In a system, a data store holds information so it can be used again. It could be a database, a file, a cache, or even a simple log kept in memory. The important bit is that the data lives there between steps, rather than vanishing after a single action.

Visual cues in a DFD

DFDs use a few core symbols to map how data moves and evolves:

• Data store: two parallel lines. This is the “saved data” area.

• Process: a circle or rounded rectangle. This is where data is transformed, calculated, or updated.

• External entity: a rectangle. This is a source or destination outside the system you’re drawing.

• Data flow: an arrow. This shows the direction data moves between stores, processes, and external entities.

Two parallel lines sit quietly, but they’re telling you something essential: data is being stored for later use, not just passing through a process and disappearing.

A simple scenario to anchor the idea

Let’s sketch a tiny, everyday example. Imagine an online bookstore.

• Customer (external entity) places an order. A data flow carries order details to a process like “Validate Order.”

• The system checks the data and, if everything looks good, the process stores the order in a data store labeled “Orders.”

• Later, another process—say, “Ship Order”—pulls the saved data from that store to prepare the shipment, updating the store as needed.

See how the data store sits between steps? It’s the repository that makes the sequence reliable. Without it, you’d be left with a string of steps that can’t remember what happened earlier.

Why data stores matter in modeling

Data stores aren’t flashy, but they’re foundational. Here’s why they matter:

• Persistence: They reflect what data survives beyond a single action. That continuity is crucial for reports, audits, and later steps in a process.

• Traceability: By marking where data is kept, a diagram helps you trace data from its origin to its final use. This makes it easier to spot gaps, bottlenecks, or potential data quality issues.

• Reusability: Stored data can feed multiple processes, not just one. That reuse saves effort and keeps systems consistent.

• Documentation clarity: A well-placed data store helps everyone reading the diagram understand the real flow of information, not just the sequence of steps.

A quick peek at the neighbors

To keep the mental image intact, here’s how data stores relate to the other symbols:

• Processes (circles or rounded rectangles): They act on data. After a process completes, the resulting data often goes into a data store or moves along to another process.

• External entities (rectangles): They supply or receive data. They don’t store data inside the system’s own memory; they’re the outside world touching your diagram.

• Data flows (arrows): They connect the pieces. Arrows talk about movement and direction—where data goes, and where it comes from.

A tiny mindset shift that helps when you draw

Whenever you see two parallel lines, ask: “Is this data something we want to access later?” If yes, it probably sits in a data store. If not, you’re likely looking at a data flow or a transformation. It’s that simple, once you train your eye.

Common misconceptions worth clearing up

• Data store vs. data flow: A data store is not moving data; it holds it. A data flow is the path data takes as it moves between places.

• A store isn’t a database only: While databases are common data stores, the symbol can represent any place data is saved for later use—files, message queues, or even a memory cache.

• Stores don’t always show “how much” data: The symbol doesn’t specify size or capacity. It’s a qualitative cue about persistence, not a quantitative one.

Real-world takeaways: spotting data stores in real diagrams

If you’re looking at a diagram in a project document or a whiteboard sketch, a couple of tells help you identify a data store fast:

• Look for two parallel lines side by side. That’s your giveaway.

• Check for a label that includes “Store,” “Data store,” or something similar. Labels usually spell out what data is being kept (e.g., “Customer Details,” “Inventory Records”).

• See if arrows point into and out of the symbol from different processes. That pattern usually means data is being saved and later retrieved.

A bit of history and a nod to practice with purpose

DFDs have a long lineage in systems modeling. The idea is to map not just what happens, but how information flows through the whole ecosystem. A data store is the memory of that ecosystem—what the system remembers, what it can reuse, and what it safeguards for future actions. In a sense, it’s the backbone of data consistency across the diagram.

If you’ve ever built a small app or a tiny workflow, you know the feeling of needing to pause and save something for later. The data store symbol captures that instinct in a simple, universal way. It’s a reminder that, in complex systems, memory matters just as much as movement.

Bringing it all together: a concise mental checklist

• When you see two parallel lines, think data store.

• Ask: Is data supposed to be saved for later retrieval or use by another process?

• Look for connections from multiple processes. Stores often serve more than one downstream step.

• Don’t confuse stores with processes or external entities. They’re the quiet anchors that hold data steady.

A little flow, a lot of sense

Data flow diagrams can feel like a bunch of lines and shapes, but they’re really about clarity. The two parallel lines don’t scream for attention; they whisper, “data is here to stay.” That whisper helps engineers, analysts, and stakeholders understand how information lives and moves in a system.

If you’re new to this world, start with a simple sketch. Draw a couple of processes, one external actor, and a data store. Then add arrows that show how data moves. You’ll notice quickly how the store changes the rhythm of the diagram—how it makes the flow feel anchored, tangible, almost tangible as a memory you don’t want to lose.

A friendly nudge to keep exploring

The symbolism in DFDs is a compact language. Master it, and you gain a practical lens for looking at real-world systems without getting buried in jargon. As you model more flows, you’ll start spotting data stores in existing diagrams with ease. And when you do, you’ll appreciate the calm confidence that comes with knowing where data sits between actions and outcomes.

If you’re curious to see more symbols in action, try a small, real-world scenario—perhaps a library management system or a simple order-tracking app. Sketch the key components, label the data store clearly, and trace a few data flows. It’s a simple exercise, but it pays off in clarity and accuracy.

Final thoughts: the quiet power of two lines

Two parallel lines might seem understated, but they carry a powerful message. Data stores remind us that information isn’t just in motion; it’s kept, organized, and ready for the next step. In the grand tapestry of a data flow diagram, those lines are the thread that keeps the picture coherent, the memory that ensures a process never loses track of what came before, and what will come next.

If you’re building your understanding of data flow diagrams, pay attention to that symbol. Let it anchor your diagrams, and you’ll find your explanations become easier to follow, your diagrams more complete, and your designs more robust. After all, good diagrams aren’t just pretty pictures; they’re practical maps of how data actually lives in the systems we build. And sometimes, the simplest symbol—the two parallel lines—is all you need to tell the whole story.