Fix typos in docs

linkedin · Feb 21, 2020 · ad31b55 · ad31b55
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diff --git a/docs/architecture/metadata-serving.md b/docs/architecture/metadata-serving.md
@@ -9,7 +9,7 @@ There are four types of Data Access Object ([DAO]) that standardize the way meta
 This section describes each type of DAO, its purpose, and the interface. 
 
 These DAOs rely heavily on [Java Generics](https://docs.oracle.com/javase/tutorial/extra/generics/index.html) so that the core logics can remain type-neutral. 
-However, as there’s no inheritance in [Pegasus], the generics often fallback to extending [RecordTemplate] instead of the desired types (i.e. [entity], [relationship], metadata [aspect] etc). Additionl runtime type checking has been added to the DAOs to avoid binding to unexpected types. We also cache the type checking reulst to minimize runtime overhead.
+However, as there’s no inheritance in [Pegasus], the generics often fallback to extending [RecordTemplate] instead of the desired types (i.e. [entity], [relationship], metadata [aspect] etc). Additional runtime type checking has been added to the DAOs to avoid binding to unexpected types. We also cache the type checking result to minimize runtime overhead.
 
 ## Key-value DAO (Local DAO)
 

diff --git a/docs/how/data-source-onboarding.md b/docs/how/data-source-onboarding.md
@@ -1,6 +1,6 @@
 # How to onboard a new data source?
 
-In the [metadata-ingestion](https://github.com/linkedin/datahub/tree/master/metadata-ingestion), Data Hub provides various kinds of metadata sources onboarding, including [Hive](https://github.com/linkedin/datahub/tree/master/metadata-ingestion/hive-etl), [Kafka](https://github.com/linkedin/datahub/tree/master/metadata-ingestion/kafka-etl), [LDAP](https://github.com/linkedin/datahub/tree/master/metadata-ingestion/ldap-etl), [mySQL](https://github.com/linkedin/datahub/tree/master/metadata-ingestion/mysql-etl), and generic [RDBMS](https://github.com/linkedin/datahub/tree/master/metadata-ingestion/rdbms-etl) as ETL scripts to feed the metadata to the [GMS](https://github.com/linkedin/datahub/blob/master/docs/what/gms.md).
+In the [metadata-ingestion](https://github.com/linkedin/datahub/tree/master/metadata-ingestion), DataHub provides various kinds of metadata sources onboarding, including [Hive](https://github.com/linkedin/datahub/tree/master/metadata-ingestion/hive-etl), [Kafka](https://github.com/linkedin/datahub/tree/master/metadata-ingestion/kafka-etl), [LDAP](https://github.com/linkedin/datahub/tree/master/metadata-ingestion/ldap-etl), [mySQL](https://github.com/linkedin/datahub/tree/master/metadata-ingestion/mysql-etl), and generic [RDBMS](https://github.com/linkedin/datahub/tree/master/metadata-ingestion/rdbms-etl) as ETL scripts to feed the metadata to the [GMS](https://github.com/linkedin/datahub/blob/master/docs/what/gms.md).
 
 ## 1. Extract
 [WIP]

diff --git a/docs/roadmap.md b/docs/roadmap.md
@@ -11,7 +11,7 @@ Below is DataHub's roadmap for the short and medium term. We'll revise this on a
 - Link datasets to jobs & flows
 ### Schemas as an entity
 - Make schemas searchable
-- Support GraphQL shcmeas
+- Support GraphQL schemas
 ### Compliance management for datasets
 - Simple tag-based compliance metadata
 ### Strongly consistent local index
@@ -50,5 +50,5 @@ Below is DataHub's roadmap for the short and medium term. We'll revise this on a
 ### Rewrite midtier in Node
 - TypeScript-only frontend development
 ### gRPC + protobuf
-- Modeling in protobuf + serveing in gRPC
+- Modeling in protobuf + serving in gRPC
 ### UI for metadata graph exploration
diff --git a/docs/what/gma.md b/docs/what/gma.md
@@ -1,6 +1,6 @@
 # What is Generalized Metadata Architecture (GMA)?
 
-GMA is the backend infrastrcuture for DataHub. Unlike existing architectures, GMA leverages multiple storage technologies to efficiently service the four most commonly used query patterns
+GMA is the backend infrastructure for DataHub. Unlike existing architectures, GMA leverages multiple storage technologies to efficiently service the four most commonly used query patterns
 - Document-oriented CRUD 
 - Complex queries (including joining distributed tables)
 - Graph traversal

diff --git a/docs/what/gms.md b/docs/what/gms.md
@@ -4,4 +4,4 @@ Metadata for [entities](entity.md) [onboarded](../how/entity-onboarding.md) to [
 
 While a GMS is completely free to define its public APIs, we do provide a list of [resource base classes](../../metadata-restli-resource/src/main/java/com/linkedin/metadata/restli) to leverage for common patterns.
 
-GMA is designed to support a distributed fleet of GMS, each serving a subset of the [GMA graph](graph.md). However, for simplicity we include a single cenetralized GMS ([datahub-gms](../../gms)) that serves all entities.
+GMA is designed to support a distributed fleet of GMS, each serving a subset of the [GMA graph](graph.md). However, for simplicity we include a single centralized GMS ([datahub-gms](../../gms)) that serves all entities.
Original file line number	Diff line number	Diff line change
Expand Up		@@ -4,4 +4,4 @@ Metadata for [entities](entity.md) [onboarded](../how/entity-onboarding.md) to [

		While a GMS is completely free to define its public APIs, we do provide a list of [resource base classes](../../metadata-restli-resource/src/main/java/com/linkedin/metadata/restli) to leverage for common patterns.

		GMA is designed to support a distributed fleet of GMS, each serving a subset of the [GMA graph](graph.md). However, for simplicity we include a single cenetralized GMS ([datahub-gms](../../gms)) that serves all entities.
		GMA is designed to support a distributed fleet of GMS, each serving a subset of the [GMA graph](graph.md). However, for simplicity we include a single centralized GMS ([datahub-gms](../../gms)) that serves all entities.