diff --git a/docs/querying/sql-data-types.md b/docs/querying/sql-data-types.md
index a98fca4a855..8427a8dd737 100644
--- a/docs/querying/sql-data-types.md
+++ b/docs/querying/sql-data-types.md
@@ -26,33 +26,25 @@ sidebar_label: "SQL data types"
 > Apache Druid supports two query languages: Druid SQL and [native queries](querying.md).
 > This document describes the SQL language.
 
-
-Columns in Druid are associated with a specific data type. This topic describes supported data types in [Druid SQL](./sql.md). 
+Druid associates each column with a specific data type. This topic describes supported data types in [Druid SQL](./sql.md).
 
 ## Standard types
 
 Druid natively supports the following basic column types:
-* LONG: (64 bit signed int)
-* FLOAT (32 bit float)
-* DOUBLE: (64 bit float)
-* STRING: (UTF-8 encoded strings and string arrays)
+
+* LONG: 64-bit signed int
+* FLOAT: 32-bit float
+* DOUBLE: 64-bit float
+* STRING: UTF-8 encoded strings and string arrays
 * COMPLEX: non-standard data types, such as nested JSON, hyperUnique and approxHistogram, and DataSketches
 * ARRAY: arrays composed of any of these types
 
-Timestamps (including the `__time` column) are treated by Druid as longs, with the value being the number of
+Druid treats timestamps (including the `__time` column) as LONG, with the value being the number of
 milliseconds since 1970-01-01 00:00:00 UTC, not counting leap seconds. Therefore, timestamps in Druid do not carry any
-timezone information, but only carry information about the exact moment in time they represent. See the
-[Time functions](sql-scalar.md#date-and-time-functions) section for more information about timestamp handling.
+timezone information. They only carry information about the exact moment in time they represent. See
+[Time functions](sql-scalar.md#date-and-time-functions) for more information about timestamp handling.
 
-Casts between two SQL types with the same Druid runtime type (see below table) have no effect, other than exceptions
-noted in the table. Casts between two SQL types that have different Druid runtime types generate a runtime cast in
-Druid. If a value cannot be cast to the target type, as in `CAST('foo' AS BIGINT)`, Druid either substitutes a default
-value (when `druid.generic.useDefaultValueForNull = true`, the default mode), or substitutes [NULL](#null-values) (when
-`druid.generic.useDefaultValueForNull = false`). NULL values cast to non-nullable types are also substituted with a
-default value. For example, if `druid.generic.useDefaultValueForNull = true`, a null VARCHAR cast to BIGINT is converted
-to a zero.
-
-The following table describes how Druid maps SQL types onto native types when running queries.
+The following table describes how Druid maps SQL types onto native types when running queries:
 
 |SQL type|Druid runtime type|Default value<sup>*</sup>|Notes|
 |--------|------------------|-------------|-----|
@@ -67,53 +59,56 @@ The following table describes how Druid maps SQL types onto native types when ru
 |SMALLINT|LONG|`0`||
 |INTEGER|LONG|`0`||
 |BIGINT|LONG|`0`|Druid LONG columns (except `__time`) are reported as BIGINT|
-|TIMESTAMP|LONG|`0`, meaning 1970-01-01 00:00:00 UTC|Druid's `__time` column is reported as TIMESTAMP. Casts between string and timestamp types assume standard SQL formatting, e.g. `2000-01-02 03:04:05`, _not_ ISO8601 formatting. For handling other formats, use one of the [time functions](sql-scalar.md#date-and-time-functions).|
-|DATE|LONG|`0`, meaning 1970-01-01|Casting TIMESTAMP to DATE rounds down the timestamp to the nearest day. Casts between string and date types assume standard SQL formatting, e.g. `2000-01-02`. For handling other formats, use one of the [time functions](sql-scalar.md#date-and-time-functions).|
-|ARRAY|ARRAY|`NULL`|Druid native array types work as SQL arrays, and multi-value strings can be converted to arrays. See the [`ARRAY` details](#arrays).|
+|TIMESTAMP|LONG|`0`, meaning 1970-01-01 00:00:00 UTC|Druid's `__time` column is reported as TIMESTAMP. Casts between string and timestamp types assume standard SQL formatting, such as `2000-01-02 03:04:05`, not ISO 8601 formatting. For handling other formats, use one of the [time functions](sql-scalar.md#date-and-time-functions).|
+|DATE|LONG|`0`, meaning 1970-01-01|Casting TIMESTAMP to DATE rounds down the timestamp to the nearest day. Casts between string and date types assume standard SQL formatting&mdash;for example, `2000-01-02`. For handling other formats, use one of the [time functions](sql-scalar.md#date-and-time-functions).|
+|ARRAY|ARRAY|`NULL`|Druid native array types work as SQL arrays, and multi-value strings can be converted to arrays. See [Arrays](#arrays) for more information.|
 |OTHER|COMPLEX|none|May represent various Druid column types such as hyperUnique, approxHistogram, etc.|
 
 <sup>*</sup> Default value applies if `druid.generic.useDefaultValueForNull = true` (the default mode). Otherwise, the default value is `NULL` for all types.
 
+Casts between two SQL types with the same Druid runtime type have no effect other than the exceptions noted in the table.
+
+Casts between two SQL types that have different Druid runtime types generate a runtime cast in Druid.
+
+If a value cannot be cast to the target type, as in `CAST('foo' AS BIGINT)`, Druid either substitutes a default
+value (when `druid.generic.useDefaultValueForNull = true`, the default mode), or substitutes [NULL](#null-values) (when
+`druid.generic.useDefaultValueForNull = false`). NULL values cast to non-nullable types are also substituted with a default value. For example, if `druid.generic.useDefaultValueForNull = true`, a null VARCHAR cast to BIGINT is converted to a zero.
+
 ## Multi-value strings
 
-Druid's native type system allows strings to potentially have multiple values. These
-[multi-value string dimensions](multi-value-dimensions.md) are reported in SQL as `VARCHAR` typed, and can be
-syntactically used like any other `VARCHAR`. Regular string functions that refer to multi-value string dimensions are
-applied to all values for each row individually. Multi-value string dimensions can also be treated as arrays via special
-[multi-value string functions](sql-multivalue-string-functions.md), which can perform powerful array-aware operations, but retain
-their `VARCHAR` typing and behavior.
+Druid's native type system allows strings to have multiple values. These [multi-value string dimensions](multi-value-dimensions.md) are reported in SQL as type VARCHAR and can be
+syntactically used like any other VARCHAR. Regular string functions that refer to multi-value string dimensions are applied to all values for each row individually.
 
-Grouping by a multi-value expression observes the native Druid multi-value aggregation behavior, which is similar to
-an implicit SQL `UNNEST`. Refer to the documentation on [multi-value string dimensions](multi-value-dimensions.md)
-for additional details.
+You can treat multi-value string dimensions as arrays using special
+[multi-value string functions](sql-multivalue-string-functions.md), which perform powerful array-aware operations, but retain their VARCHAR type and behavior.
 
-> Because multi-value dimensions are treated by the SQL planner as `VARCHAR`, there are some inconsistencies between how
-> they are handled in Druid SQL and in native queries. For example, expressions involving multi-value dimensions may be
-> incorrectly optimized by the Druid SQL planner: `multi_val_dim = 'a' AND multi_val_dim = 'b'` is optimized to
-> `false`, even though it is possible for a single row to have both "a" and "b" as values for `multi_val_dim`. The
-> SQL behavior of multi-value dimensions may change in a future release to more closely align with their behavior
-> in native queries, but the [multi-value string functions](./sql-multivalue-string-functions.md) should be able to provide
-> nearly all possible native functionality.
+Grouping by multi-value dimensions observes the native Druid multi-value aggregation behavior, which is similar to an implicit SQL UNNEST. See [Grouping](multi-value-dimensions.md#grouping) for more information.
+
+> Because the SQL planner treats multi-value dimensions as VARCHAR, there are some inconsistencies between how they are handled in Druid SQL and in native queries. For instance, expressions involving multi-value dimensions may be incorrectly optimized by the Druid SQL planner. For example, `multi_val_dim = 'a' AND multi_val_dim = 'b'` is optimized to
+`false`, even though it is possible for a single row to have both `'a'` and `'b'` as values for `multi_val_dim`.
+>
+> The SQL behavior of multi-value dimensions may change in a future release to more closely align with their behavior in native queries, but the [multi-value string functions](./sql-multivalue-string-functions.md) should be able to provide nearly all possible native functionality.
 
 ## Arrays
-Druid supports `ARRAY` types constructed at query time, though it currently lacks the ability to store them in
-segments. `ARRAY` types behave as standard SQL arrays, where results are grouped by matching entire arrays. This is in
-contrast to the implicit `UNNEST` that occurs when grouping on multi-value dimensions directly or when used with the
-multi-value functions. You can convert multi-value dimensions to standard SQL arrays either by explicitly by converting
-them with `MV_TO_ARRAY` or implicitly when used within the [array functions](./sql-array-functions.md). Arrays may
-also be constructed from multiple columns using the array functions.
+
+Druid supports ARRAY types constructed at query time. ARRAY types behave as standard SQL arrays, where results are grouped by matching entire arrays. This is in contrast to the implicit UNNEST that occurs when grouping on multi-value dimensions directly or when used with multi-value functions.
+
+You can convert multi-value dimensions to standard SQL arrays explicitly with `MV_TO_ARRAY` or implicitly using [array functions](./sql-array-functions.md). You can also use the array functions to construct arrays from multiple columns.
+
+You can use [schema auto-discovery](../ingestion/schema-design.md#schema-auto-discovery-for-dimensions) to detect and ingest arrays as ARRAY typed columns.
 
 ## Multi-value strings behavior
+
 The behavior of Druid [multi-value string dimensions](multi-value-dimensions.md) varies depending on the context of
 their usage.
 
-When used with standard `VARCHAR` functions which expect a single input value per row, such as `CONCAT`, Druid will map
+When used with standard VARCHAR functions which expect a single input value per row, such as CONCAT, Druid will map
 the function across all values in the row. If the row is null or empty, the function receives `NULL` as its input.
 
 When used with the explicit [multi-value string functions](./sql-multivalue-string-functions.md), Druid processes the
-row values as if they were `ARRAY` typed. Any operations which produce null and empty rows are distinguished as
+row values as if they were ARRAY typed. Any operations which produce null and empty rows are distinguished as
 separate values (unlike implicit mapping behavior). These multi-value string functions, typically denoted with an `MV_`
-prefix, retain their `VARCHAR` type after the computation is complete. Note that Druid multi-value columns do _not_
+prefix, retain their VARCHAR type after the computation is complete. Note that Druid multi-value columns do _not_
 distinguish between empty and null rows. An empty row will never appear natively as input to a multi-valued function,
 but any multi-value function which manipulates the array form of the value may produce an empty array, which is handled
 separately while processing.
@@ -122,12 +117,12 @@ separately while processing.
 > to determine how to properly process the value given its ambiguous usage. A multi-value string must be treated consistently within
 > an expression.
 
-When converted to `ARRAY` or used with [array functions](./sql-array-functions.md), multi-value strings behave as standard SQL arrays and can no longer
+When converted to ARRAY or used with [array functions](./sql-array-functions.md), multi-value strings behave as standard SQL arrays and can no longer
 be manipulated with non-array functions.
 
-Druid serializes multi-value `VARCHAR` results as a JSON string of the array, if grouping was not applied on the value.
-If the value was grouped, due to the implicit `UNNEST` behavior, all results will always be standard single value
-`VARCHAR`. `ARRAY` typed results will be serialized into stringified JSON arrays if the context parameter
+Druid serializes multi-value VARCHAR results as a JSON string of the array, if grouping was not applied on the value.
+If the value was grouped, due to the implicit UNNEST behavior, all results will always be standard single value
+VARCHAR. ARRAY typed results will be serialized into stringified JSON arrays if the context parameter
 `sqlStringifyArrays` is set, otherwise they remain in their array format.
 
 
@@ -167,10 +162,10 @@ Druid supports storing nested data structures in segments using the native `COMP
 
 You can interact with nested data using [JSON functions](./sql-json-functions.md), which can extract nested values, parse from string, serialize to string, and create new `COMPLEX<json>` structures.
 
-`COMPLEX` types have limited functionality outside the specialized functions that use them, so their behavior is undefined when:
+COMPLEX types have limited functionality outside the specialized functions that use them, so their behavior is undefined when:
 
 * Grouping on complex values.
 * Filtering directly on complex values, such as `WHERE json is NULL`.
 * Used as inputs to aggregators without specialized handling for a specific complex type.
 
-In many cases, functions are provided to translate `COMPLEX` value types to `STRING`, which serves as a workaround solution until `COMPLEX` type functionality can be improved.
+In many cases, functions are provided to translate COMPLEX value types to STRING, which serves as a workaround solution until COMPLEX type functionality can be improved.