Pivot and Unpivot Data

This part of the SQL and Databases path moves from knowing that Pivot and Unpivot Data exists to being able to use it deliberately. By the end, you should be able to explain the mechanism, build or configure a small example, verify the result, and diagnose the most common ways it fails.

In this lesson

• Place Pivot and Unpivot Data in the context of the Advanced SQL module rather than treating it as an isolated feature.
• Build a mental model for what happens before, during, and after the operation.
• Work through a reproducible example connected to the scenario: design and query an order-and-customer database while preserving data integrity.
• Inspect the result and distinguish evidence from assumption.
• Recognize failure modes, misleading shortcuts, and production constraints.
• Leave with a verification checklist and a practical exercise rather than a memorized snippet.

The technical core

• PivotTables aggregate records into interactive row, column, filter and value groupings.
• Source data quality and stable column headings matter more than visual formatting when building reliable pivots.
• Refreshing a PivotTable is necessary when its source changes, unless the workflow explicitly automates refresh.

Those points define the boundary of Pivot and Unpivot Data. The rest of the lesson turns them into observable behavior in SQLite/PostgreSQL and a SQL client.

A second example with a different shape

For a database developer, Pivot and Unpivot Data becomes useful when it changes a decision you can verify. Documentation often presents the API or syntax first because reference pages are written for lookup. A tutorial has a different job. Here the explanation begins with intent, then shows the smallest concrete implementation, then adds constraints. That order lets you understand why a setting or line exists before you are asked to remember its spelling. In this lesson's Pivot and Unpivot Data example, record the evidence you observed rather than treating the rule as a slogan; that note becomes useful when the next Advanced SQL exercise changes the conditions. In SQL and Databases lesson 33 — Pivot and Unpivot Data, use that observation as the checkpoint for this exact Advanced SQL topic rather than generalizing it beyond the evidence.

The practical question behind pivot and unpivot data is not simply whether the feature exists, but what behavior it gives you control over. At the intermediate stage, the goal is not to cover every advanced option. It is to establish the correct mental model and the verification habit that later pages can extend. Where the platform has version-specific behavior, prefer the current official documentation and check the version shown by your own tools before assuming an older screenshot or blog post is authoritative. The specific test here is about Pivot and Unpivot Data: change one relevant input, configuration value or boundary and make sure the result still matches the contract described above. In SQL and Databases lesson 33 — Pivot and Unpivot Data, use that observation as the checkpoint for this exact Advanced SQL topic rather than generalizing it beyond the evidence.

Common analytical mistakes

Before adding more syntax, make the state of the system observable. That habit matters especially when working with Pivot and Unpivot Data. Documentation often presents the API or syntax first because reference pages are written for lookup. A tutorial has a different job. Here the explanation begins with intent, then shows the smallest concrete implementation, then adds constraints. That order lets you understand why a setting or line exists before you are asked to remember its spelling. The specific test here is about Pivot and Unpivot Data: change one relevant input, configuration value or boundary and make sure the result still matches the contract described above. In SQL and Databases lesson 33 — Pivot and Unpivot Data, use that observation as the checkpoint for this exact Advanced SQL topic rather than generalizing it beyond the evidence.

There are usually several ways to accomplish the same visible result. The important skill is knowing which guarantees differ when you choose one form of Pivot and Unpivot Data over another. At the intermediate stage, the goal is not to cover every advanced option. It is to establish the correct mental model and the verification habit that later pages can extend. Where the platform has version-specific behavior, prefer the current official documentation and check the version shown by your own tools before assuming an older screenshot or blog post is authoritative. In this lesson's Pivot and Unpivot Data example, record the evidence you observed rather than treating the rule as a slogan; that note becomes useful when the next Advanced SQL exercise changes the conditions. In SQL and Databases lesson 33 — Pivot and Unpivot Data, use that observation as the checkpoint for this exact Advanced SQL topic rather than generalizing it beyond the evidence.

Questions to answer about Pivot and Unpivot Data

1. What is the smallest input or state that makes Pivot and Unpivot Data observable?
2. What does success look like, and how can you prove it without relying on a vague UI message?
3. Which configuration, permissions, types, versions or environment details can change the result?
4. Which failure is most likely for a beginner, and what evidence distinguishes it from a different failure?
5. What should remain true after the example is repeated, automated or moved to another environment?

Verification queries/checks

In the Advanced SQL part of this learning path, Pivot and Unpivot Data is deliberately introduced now because later lessons depend on the boundary it establishes. Documentation often presents the API or syntax first because reference pages are written for lookup. A tutorial has a different job. Here the explanation begins with intent, then shows the smallest concrete implementation, then adds constraints. That order lets you understand why a setting or line exists before you are asked to remember its spelling. Keep this point tied to Pivot and Unpivot Data. The same general engineering habit appears elsewhere, but the evidence and failure signals in this Advanced SQL lesson are specific to this mechanism. In SQL and Databases lesson 33 — Pivot and Unpivot Data, use that observation as the checkpoint for this exact Advanced SQL topic rather than generalizing it beyond the evidence.

A production system rarely fails at the exact line shown in a beginner example, so this section connects Pivot and Unpivot Data to the surrounding runtime and operational context. At the intermediate stage, the goal is not to cover every advanced option. It is to establish the correct mental model and the verification habit that later pages can extend. Where the platform has version-specific behavior, prefer the current official documentation and check the version shown by your own tools before assuming an older screenshot or blog post is authoritative. The specific test here is about Pivot and Unpivot Data: change one relevant input, configuration value or boundary and make sure the result still matches the contract described above. In SQL and Databases lesson 33 — Pivot and Unpivot Data, use that observation as the checkpoint for this exact Advanced SQL topic rather than generalizing it beyond the evidence.

Model the data before writing syntax

This section needs a different question from the earlier explanation: what would make Pivot and Unpivot Data fail specifically while working through Model the data before writing syntax? Choose one realistic boundary, reproduce it deliberately, and inspect the first useful diagnostic or intermediate value. The aim in Pivot and Unpivot Data is to recognize the mechanism under changed conditions, not to repeat the same successful path with different wording.

The practical question behind pivot and unpivot data is not simply whether the feature exists, but what behavior it gives you control over. At the intermediate stage, the goal is not to cover every advanced option. It is to establish the correct mental model and the verification habit that later pages can extend. Where the platform has version-specific behavior, prefer the current official documentation and check the version shown by your own tools before assuming an older screenshot or blog post is authoritative. For Pivot and Unpivot Data, apply this check in the context of the Advanced SQL workflow before carrying the assumption into later SQL and Databases work.

Evidence table

What you inspect	What it tells you	What it does not prove
Source/configuration for Pivot and Unpivot Data	What you asked the platform/runtime to do	That the request actually succeeded
Build/validation output	Whether static checks accepted the artifact	That production data and permissions behave correctly
Runtime/result output	What happened for this input	That every edge case is safe
Logs/diagnostics	Where the system spent time or failed	The root cause without interpretation
Repeat test	Whether behavior is reproducible	That the design is optimal

The shape of the input

Before adding more syntax, make the state of the system observable. That habit matters especially when working with Pivot and Unpivot Data. Documentation often presents the API or syntax first because reference pages are written for lookup. A tutorial has a different job. Here the explanation begins with intent, then shows the smallest concrete implementation, then adds constraints. That order lets you understand why a setting or line exists before you are asked to remember its spelling. Keep this point tied to Pivot and Unpivot Data. The same general engineering habit appears elsewhere, but the evidence and failure signals in this Advanced SQL lesson are specific to this mechanism.

There are usually several ways to accomplish the same visible result. The important skill is knowing which guarantees differ when you choose one form of Pivot and Unpivot Data over another. At the intermediate stage, the goal is not to cover every advanced option. It is to establish the correct mental model and the verification habit that later pages can extend. Where the platform has version-specific behavior, prefer the current official documentation and check the version shown by your own tools before assuming an older screenshot or blog post is authoritative. For Pivot and Unpivot Data, apply this check in the context of the Advanced SQL workflow before carrying the assumption into later SQL and Databases work.

Types, nulls and constraints

In the Advanced SQL part of this learning path, Pivot and Unpivot Data is deliberately introduced now because later lessons depend on the boundary it establishes. Documentation often presents the API or syntax first because reference pages are written for lookup. A tutorial has a different job. Here the explanation begins with intent, then shows the smallest concrete implementation, then adds constraints. That order lets you understand why a setting or line exists before you are asked to remember its spelling. The specific test here is about Pivot and Unpivot Data: change one relevant input, configuration value or boundary and make sure the result still matches the contract described above.

A production system rarely fails at the exact line shown in a beginner example, so this section connects Pivot and Unpivot Data to the surrounding runtime and operational context. At the intermediate stage, the goal is not to cover every advanced option. It is to establish the correct mental model and the verification habit that later pages can extend. Where the platform has version-specific behavior, prefer the current official documentation and check the version shown by your own tools before assuming an older screenshot or blog post is authoritative. For Pivot and Unpivot Data, apply this check in the context of the Advanced SQL workflow before carrying the assumption into later SQL and Databases work.

Worked example: Pivot and Unpivot Data

The following sql example is written specifically for this lesson. Read the requirement first, then predict the important result before running or reproducing it.

CREATE TABLE inventory (
  sku TEXT PRIMARY KEY,
  description TEXT NOT NULL,
  quantity INTEGER NOT NULL CHECK (quantity >= 0)
);

INSERT INTO inventory VALUES
('KB-100', 'Keyboard', 8),
('MS-200', 'Mouse', 3),
('HD-300', 'Headset', 12);

SELECT sku, description, quantity
FROM inventory
WHERE quantity < 10
ORDER BY quantity;

Expected observation

MS-200 | Mouse | 3\nKB-100 | Keyboard | 8

Read the example deliberately

• Line/construct 1: CREATE TABLE inventory ( — identify what state or contract this introduces, then trace where that state is consumed.
• Line/construct 2: sku TEXT PRIMARY KEY, — identify what state or contract this introduces, then trace where that state is consumed.
• Line/construct 3: description TEXT NOT NULL, — identify what state or contract this introduces, then trace where that state is consumed.
• Line/construct 4: quantity INTEGER NOT NULL CHECK (quantity >= 0) — identify what state or contract this introduces, then trace where that state is consumed.
• Line/construct 5: ); — identify what state or contract this introduces, then trace where that state is consumed.
• Line/construct 6: INSERT INTO inventory VALUES — identify what state or contract this introduces, then trace where that state is consumed.
• Line/construct 7: ('KB-100', 'Keyboard', 8), — identify what state or contract this introduces, then trace where that state is consumed.
• Line/construct 8: ('MS-200', 'Mouse', 3), — identify what state or contract this introduces, then trace where that state is consumed.
• Line/construct 9: ('HD-300', 'Headset', 12); — identify what state or contract this introduces, then trace where that state is consumed.
• Line/construct 10: SELECT sku, description, quantity — identify what state or contract this introduces, then trace where that state is consumed.

Do not stop at “it ran.” Change one meaningful value related to Pivot and Unpivot Data, predict the new result, run/reproduce the example again, and explain why the output changed. That mutation test is a stronger check of understanding than copying the original result.

Build a small trustworthy dataset

For a database developer, Pivot and Unpivot Data becomes useful when it changes a decision you can verify. Documentation often presents the API or syntax first because reference pages are written for lookup. A tutorial has a different job. Here the explanation begins with intent, then shows the smallest concrete implementation, then adds constraints. That order lets you understand why a setting or line exists before you are asked to remember its spelling. Keep this point tied to Pivot and Unpivot Data. The same general engineering habit appears elsewhere, but the evidence and failure signals in this Advanced SQL lesson are specific to this mechanism. In SQL and Databases lesson 33 — Pivot and Unpivot Data, use that observation as the checkpoint for this exact Advanced SQL topic rather than generalizing it beyond the evidence.

The practical question behind pivot and unpivot data is not simply whether the feature exists, but what behavior it gives you control over. At the intermediate stage, the goal is not to cover every advanced option. It is to establish the correct mental model and the verification habit that later pages can extend. Where the platform has version-specific behavior, prefer the current official documentation and check the version shown by your own tools before assuming an older screenshot or blog post is authoritative. Keep this point tied to Pivot and Unpivot Data. The same general engineering habit appears elsewhere, but the evidence and failure signals in this Advanced SQL lesson are specific to this mechanism.

Perform the core Pivot and Unpivot Data operation

In Perform the core Pivot and Unpivot Data operation, look at Pivot and Unpivot Data through the constraint that matters in this part of the lesson: make the relevant state visible before you change it, then compare the observed result with the contract you expected. In SQL and Databases, this prevents a local-looking edit from hiding an environment, data, permission, lifecycle or runtime assumption. Record the evidence from this step because the next decision in the Advanced SQL module should be based on what you measured rather than on a repeated rule of thumb.

This section needs a different question from the earlier explanation: what would make Pivot and Unpivot Data fail specifically while working through Perform the core Pivot and Unpivot Data operation? Choose one realistic boundary, reproduce it deliberately, and inspect the first useful diagnostic or intermediate value. The aim in Pivot and Unpivot Data is to recognize the mechanism under changed conditions, not to repeat the same successful path with different wording.

Failure-mode matrix

Symptom	Likely category	First evidence to collect
The Pivot and Unpivot Data behavior never occurs	configuration / control flow	verify the relevant code/configuration is actually reached
Build or validation fails	syntax / type / unsupported option	read the first meaningful diagnostic, not the last cascade message
Works locally but not elsewhere	environment / version / permission	compare runtime versions, identity, configuration and data
Result is valid but wrong	assumption / data shape / business rule	inspect intermediate values and boundary conditions
Intermittent behavior	concurrency / timing / external dependency	add timestamps, correlation IDs or deterministic reproduction

Read the result, not just the syntax

In the Advanced SQL part of this learning path, Pivot and Unpivot Data is deliberately introduced now because later lessons depend on the boundary it establishes. Documentation often presents the API or syntax first because reference pages are written for lookup. A tutorial has a different job. Here the explanation begins with intent, then shows the smallest concrete implementation, then adds constraints. That order lets you understand why a setting or line exists before you are asked to remember its spelling. For Pivot and Unpivot Data, apply this check in the context of the Advanced SQL workflow before carrying the assumption into later SQL and Databases work.

Now apply Pivot and Unpivot Data to the current Read the result, not just the syntax concern. Start from the smallest state that demonstrates the behavior, vary one input or configuration choice, and explain the result in terms of the SQL and Databases runtime or platform. If two outcomes look similar in the UI, use logs, return values, generated artifacts, query results, tests or another concrete signal to distinguish them.

Validate row counts and invariants

For this part of Pivot and Unpivot Data, move beyond the earlier mental model and ask how the behavior survives repetition. Run or reproduce the step twice, change the ordering or boundary case where safe, and verify that the same invariant still holds. A reliable Advanced SQL workflow is one that produces evidence you can compare, not one that succeeds only when the exact tutorial sequence is copied.

This section needs a different question from the earlier explanation: what would make Pivot and Unpivot Data fail specifically while working through Validate row counts and invariants? Choose one realistic boundary, reproduce it deliberately, and inspect the first useful diagnostic or intermediate value. The aim in Pivot and Unpivot Data is to recognize the mechanism under changed conditions, not to repeat the same successful path with different wording.

Edge cases that change the result

Before adding more syntax, make the state of the system observable. That habit matters especially when working with Pivot and Unpivot Data. Documentation often presents the API or syntax first because reference pages are written for lookup. A tutorial has a different job. Here the explanation begins with intent, then shows the smallest concrete implementation, then adds constraints. That order lets you understand why a setting or line exists before you are asked to remember its spelling. In this lesson's Pivot and Unpivot Data example, record the evidence you observed rather than treating the rule as a slogan; that note becomes useful when the next Advanced SQL exercise changes the conditions. In SQL and Databases lesson 33 — Pivot and Unpivot Data, use that observation as the checkpoint for this exact Advanced SQL topic rather than generalizing it beyond the evidence.

There are usually several ways to accomplish the same visible result. The important skill is knowing which guarantees differ when you choose one form of Pivot and Unpivot Data over another. At the intermediate stage, the goal is not to cover every advanced option. It is to establish the correct mental model and the verification habit that later pages can extend. Where the platform has version-specific behavior, prefer the current official documentation and check the version shown by your own tools before assuming an older screenshot or blog post is authoritative. The specific test here is about Pivot and Unpivot Data: change one relevant input, configuration value or boundary and make sure the result still matches the contract described above.

Performance and indexing/vectorization considerations

Now apply Pivot and Unpivot Data to the current Performance and indexing/vectorization considerations concern. Start from the smallest state that demonstrates the behavior, vary one input or configuration choice, and explain the result in terms of the SQL and Databases runtime or platform. If two outcomes look similar in the UI, use logs, return values, generated artifacts, query results, tests or another concrete signal to distinguish them.

A production system rarely fails at the exact line shown in a beginner example, so this section connects Pivot and Unpivot Data to the surrounding runtime and operational context. At the intermediate stage, the goal is not to cover every advanced option. It is to establish the correct mental model and the verification habit that later pages can extend. Where the platform has version-specific behavior, prefer the current official documentation and check the version shown by your own tools before assuming an older screenshot or blog post is authoritative. In this lesson's Pivot and Unpivot Data example, record the evidence you observed rather than treating the rule as a slogan; that note becomes useful when the next Advanced SQL exercise changes the conditions.

Transactions or reproducibility

In Transactions or reproducibility, look at Pivot and Unpivot Data through the constraint that matters in this part of the lesson: make the relevant state visible before you change it, then compare the observed result with the contract you expected. In SQL and Databases, this prevents a local-looking edit from hiding an environment, data, permission, lifecycle or runtime assumption. Record the evidence from this step because the next decision in the Advanced SQL module should be based on what you measured rather than on a repeated rule of thumb.

The practical question behind pivot and unpivot data is not simply whether the feature exists, but what behavior it gives you control over. At the intermediate stage, the goal is not to cover every advanced option. It is to establish the correct mental model and the verification habit that later pages can extend. Where the platform has version-specific behavior, prefer the current official documentation and check the version shown by your own tools before assuming an older screenshot or blog post is authoritative. In this lesson's Pivot and Unpivot Data example, record the evidence you observed rather than treating the rule as a slogan; that note becomes useful when the next Advanced SQL exercise changes the conditions.

Data-quality checks

This section needs a different question from the earlier explanation: what would make Pivot and Unpivot Data fail specifically while working through Data-quality checks? Choose one realistic boundary, reproduce it deliberately, and inspect the first useful diagnostic or intermediate value. The aim in Pivot and Unpivot Data is to recognize the mechanism under changed conditions, not to repeat the same successful path with different wording.

There are usually several ways to accomplish the same visible result. The important skill is knowing which guarantees differ when you choose one form of Pivot and Unpivot Data over another. At the intermediate stage, the goal is not to cover every advanced option. It is to establish the correct mental model and the verification habit that later pages can extend. Where the platform has version-specific behavior, prefer the current official documentation and check the version shown by your own tools before assuming an older screenshot or blog post is authoritative. Keep this point tied to Pivot and Unpivot Data. The same general engineering habit appears elsewhere, but the evidence and failure signals in this Advanced SQL lesson are specific to this mechanism.

A production-oriented walkthrough for Pivot and Unpivot Data

1. Establish the Pivot and Unpivot Data behavior

2. Inspect the Pivot and Unpivot Data behavior

3. Implement the Pivot and Unpivot Data behavior

Implement this step in the context of design and query an order-and-customer database while preserving data integrity. Keep the change small enough that you can state the expected result before executing it. Capture the relevant input, configuration or code, then record the observable result. If the result differs from the prediction, do not add more changes yet; narrow the mismatch using diagnostics appropriate to SQLite/PostgreSQL and a SQL client. The specific test here is about Pivot and Unpivot Data: change one relevant input, configuration value or boundary and make sure the result still matches the contract described above.

A useful variation is to introduce one boundary case that is plausible for Pivot and Unpivot Data: an empty value, a missing permission, an unexpected type, a repeated operation, an unavailable dependency, or a larger-than-normal input. The exact case depends on the technology, but the reasoning is the same—state the invariant you expect to remain true, then verify it explicitly. The specific test here is about Pivot and Unpivot Data: change one relevant input, configuration value or boundary and make sure the result still matches the contract described above. In SQL and Databases lesson 33 — Pivot and Unpivot Data, use that observation as the checkpoint for this exact Advanced SQL topic rather than generalizing it beyond the evidence.

4. Exercise the Pivot and Unpivot Data behavior

Exercise this step in the context of design and query an order-and-customer database while preserving data integrity. Keep the change small enough that you can state the expected result before executing it. Capture the relevant input, configuration or code, then record the observable result. If the result differs from the prediction, do not add more changes yet; narrow the mismatch using diagnostics appropriate to SQLite/PostgreSQL and a SQL client. The specific test here is about Pivot and Unpivot Data: change one relevant input, configuration value or boundary and make sure the result still matches the contract described above.

5. Challenge the Pivot and Unpivot Data behavior

A useful variation is to introduce one boundary case that is plausible for Pivot and Unpivot Data: an empty value, a missing permission, an unexpected type, a repeated operation, an unavailable dependency, or a larger-than-normal input. The exact case depends on the technology, but the reasoning is the same—state the invariant you expect to remain true, then verify it explicitly. Keep this point tied to Pivot and Unpivot Data. The same general engineering habit appears elsewhere, but the evidence and failure signals in this Advanced SQL lesson are specific to this mechanism.

6. Verify the Pivot and Unpivot Data behavior

7. Harden the Pivot and Unpivot Data behavior

This section needs a different question from the earlier explanation: what would make Pivot and Unpivot Data fail specifically while working through A production-oriented walkthrough for Pivot and Unpivot Data? Choose one realistic boundary, reproduce it deliberately, and inspect the first useful diagnostic or intermediate value. The aim in Pivot and Unpivot Data is to recognize the mechanism under changed conditions, not to repeat the same successful path with different wording.

8. Document the Pivot and Unpivot Data behavior

Mistakes that distort the Pivot and Unpivot Data mental model

Treating Pivot and Unpivot Data as syntax instead of behavior

If you can reproduce the syntax but cannot predict the state after it runs, the lesson is not finished. Rewrite the example in your own words and name the input, operation and observable result.

Copying a configuration from a different version

SQL and Databases tooling evolves. Compare the documentation version, runtime/tool version and project settings before assuming that a screenshot or command from another environment applies unchanged.

Verifying only the happy path

A successful first run proves one path. Add at least one negative or boundary case relevant to Pivot and Unpivot Data. The failure should be intentional and the diagnostic should make sense.

Hiding the important state behind too much abstraction

Abstraction is useful after the behavior is understood. During the first implementation of Pivot and Unpivot Data, keep the decisive state and control flow visible enough to debug.

Recovering from common Pivot and Unpivot Data failures

Use this order when Pivot and Unpivot Data does not behave as expected:

1. Reproduce the smallest failing case.
2. Confirm the actual version/toolchain/environment.
3. Capture the first meaningful diagnostic or unexpected value.
4. Verify identity, permissions and configuration if the operation crosses a service boundary.
5. Inspect intermediate state rather than only the final UI.
6. Change one variable and rerun.
7. Compare the corrected behavior with a negative case.
8. Record the final cause so the same failure is faster to diagnose next time.

Practice: change the constraint

Extend the worked scenario so that Pivot and Unpivot Data must handle one additional real constraint. Choose one: a second data shape, a failed dependency, an invalid input, a permission difference, a repeat operation, or a larger workload. Before implementing the change, write down the behavior you expect and the evidence that will prove it.

Your result is complete when another learner can reproduce the change from your notes, observe the expected behavior, and intentionally trigger at least one documented failure without damaging their environment. In this lesson's Pivot and Unpivot Data example, record the evidence you observed rather than treating the rule as a slogan; that note becomes useful when the next Advanced SQL exercise changes the conditions.

Check your understanding of Pivot and Unpivot Data

• Can you define Pivot and Unpivot Data without using the exact wording of an API/reference page?
• Can you identify the boundary where Pivot and Unpivot Data begins and where another concept takes over?
• Can you predict the result of the worked example before running it?
• Can you explain one failure from evidence rather than guessing?
• Can you name one production constraint that the beginner example intentionally simplifies?
• Can you repeat the example from a clean state?

What should stay with you

• Pivot and Unpivot Data is useful because it controls observable behavior, not because it adds another piece of syntax to memorize.
• Verification belongs in the workflow: build/check, run/reproduce, inspect, challenge, and repeat.
• The Advanced SQL module uses this lesson as a foundation for the next decisions in the SQL and Databases learning path.
• Official documentation is the source of truth for version-specific contracts; tutorials should teach you how to read and apply those contracts.

Primary references used for verification

The following primary documentation was used as a factual reference map for this lesson. ScrutnLearn's explanation is original synthesis rather than copied documentation prose.

• MySQL reference manual
• PostgreSQL SQL tutorial
• PostgreSQL current documentation
• SQL Server documentation
• SQLite documentation

Ready.