Fixed broken links

Author: rolyon

articles/azure-databricks/what-is-azure-databricks.md

@@ -18,7 +18,7 @@ Azure Databricks is an Apache Spark-based analytics platform optimized for the M
 
 ![What is Azure Databricks?](./media/what-is-azure-databricks/azure-databricks-overview.png "What is Azure Databricks?")
 
-Azure Databricks is a fast, easy, and collaborative Apache Spark-based analytics service. For a big data pipeline, the data (raw or structured) is ingested into Azure through Azure Data Factory in batches, or streamed near real-time using Kafka, Event Hub, or IoT Hub. This data lands in a data lake for long term persisted storage, in Azure Blob Storage or Azure Data Lake Storage. As part of your analytics workflow, use Azure Databricks to read data from multiple data sources such as [Azure Blob Storage](../storage/blobs/storage-blobs-introduction.md), [Azure Data Lake Storage](../data-lake-store/index.md), [Azure Cosmos DB](../cosmos-db/index.yml), or [Azure SQL Data Warehouse](../sql-data-warehouse/index.yml) and turn it into breakthrough insights using Spark.
+Azure Databricks is a fast, easy, and collaborative Apache Spark-based analytics service. For a big data pipeline, the data (raw or structured) is ingested into Azure through Azure Data Factory in batches, or streamed near real-time using Kafka, Event Hub, or IoT Hub. This data lands in a data lake for long term persisted storage, in Azure Blob Storage or Azure Data Lake Storage. As part of your analytics workflow, use Azure Databricks to read data from multiple data sources such as [Azure Blob Storage](../storage/blobs/storage-blobs-introduction.md), [Azure Data Lake Storage](../data-lake-store/index.yml), [Azure Cosmos DB](../cosmos-db/index.yml), or [Azure SQL Data Warehouse](../sql-data-warehouse/index.yml) and turn it into breakthrough insights using Spark.
 
 ![Databricks pipeline](./media/what-is-azure-databricks/databricks-pipeline.png)
 

articles/data-lake-store/index.yml

@@ -97,7 +97,7 @@ landingContent:
           - text: REST API
             url: data-lake-store-get-started-rest-api.md
           - text: Python
-            url: data-lake-store/data-lake-store-get-started-python.md
+            url: data-lake-store-get-started-python.md
 
   # Card
   - title: Filesystem operations
@@ -107,7 +107,7 @@ landingContent:
           - text: .NET SDK
             url: data-lake-store-data-operations-net-sdk.md
           - text: Java SDK
-            url: data-lake-store/data-lake-store-get-started-java-sdk.md
+            url: data-lake-store-get-started-java-sdk.md
           - text: REST API
             url: data-lake-store-data-operations-rest-api.md
           - text: Python

articles/hdinsight/hadoop/apache-hadoop-on-premises-migration-best-practices-storage.md

@@ -87,7 +87,7 @@ For more information, see the following articles:
 
 Azure Data Lake Storage Gen2 is the latest storage offering. It unifies the core capabilities from the first generation of Azure Data Lake Storage with a Hadoop compatible file system endpoint directly integrated into Azure Blob Storage. This enhancement combines the scale and cost benefits of object storage with the reliability and performance typically associated only with on-premises file systems.
 
-ADLS Gen 2 is built on top of [Azure Blob storage](../../storage/blobs/storage-blobs-introduction.md) and allows you to interface with data using both file system and object storage paradigms. Features from [Azure Data Lake Storage Gen1](../../data-lake-store/index.md), such as file system semantics, file-level security, and scale are combined with low-cost, tiered storage, high availability/disaster recovery capabilities, and a large SDK/tooling ecosystem from [Azure Blob storage](../../storage/blobs/storage-blobs-introduction.md). In Data Lake Storage Gen2, all the qualities of object storage remain while adding the advantages of a file system interface optimized for analytics workloads.
+ADLS Gen 2 is built on top of [Azure Blob storage](../../storage/blobs/storage-blobs-introduction.md) and allows you to interface with data using both file system and object storage paradigms. Features from [Azure Data Lake Storage Gen1](../../data-lake-store/index.yml), such as file system semantics, file-level security, and scale are combined with low-cost, tiered storage, high availability/disaster recovery capabilities, and a large SDK/tooling ecosystem from [Azure Blob storage](../../storage/blobs/storage-blobs-introduction.md). In Data Lake Storage Gen2, all the qualities of object storage remain while adding the advantages of a file system interface optimized for analytics workloads.
 
 A fundamental feature of Data Lake Storage Gen2 is the addition of a [hierarchical namespace](../../storage/data-lake-storage/namespace.md) to the Blob storage service, which organizes objects/files into a hierarchy of directories for performant data access. The hierarchical structure enables operations such as renaming or deleting a directory to be single atomic metadata operations on the directory rather than enumerating and processing all objects that share the name prefix of the directory.