Update data-lake-storage-best-practices.md

Fixed typo per acrolinx scorecard.

Author: v-shils

articles/storage/blobs/data-lake-storage-best-practices.md

@@ -84,7 +84,7 @@ Consider pre-planning the structure of your data. File format, file size, and di
 
 ### File formats
 
-Data can be ingested in various formats. Data can be appear in human readable formats such as JSON, CSV, or XML or as compressed binary formats such as `.tar.gz`. Data can come in various sizes as well. Data can be composed of large files (a few terabytes) such as data from an export of a SQL table from your on-premises systems. Data can also come in the form of a large number of tiny files (a few kilobytes) such as data from real-time events from an Internet of things (IoT) solution. You can optimize efficiency and costs by choosing an appropriate file format and file size.
+Data can be ingested in various formats. Data can appear in human readable formats such as JSON, CSV, or XML or as compressed binary formats such as `.tar.gz`. Data can come in various sizes as well. Data can be composed of large files (a few terabytes) such as data from an export of a SQL table from your on-premises systems. Data can also come in the form of a large number of tiny files (a few kilobytes) such as data from real-time events from an Internet of things (IoT) solution. You can optimize efficiency and costs by choosing an appropriate file format and file size.
 
 Hadoop supports a set of file formats that are optimized for storing and processing structured data. Some common formats are Avro, Parquet, and Optimized Row Columnar (ORC) format. All of these formats are machine-readable binary file formats. They're compressed to help you manage file size. They have a schema embedded in each file, which makes them self-describing. The difference between these formats is in how data is stored. Avro stores data in a row-based format and the Parquet and ORC formats store data in a columnar format.