Merge pull request ClickHouse#525 from ClickHouse/DanRoscigno-patch-7

Update why-clickhouse-is-so-fast.md

docs/en/faq/general/why-clickhouse-is-so-fast.md

@@ -21,15 +21,15 @@ It was designed to be fast. Query execution performance has always been a top pr
 
 ### Architecture choices
 
-ClickHouse was initially built as a prototype to do just a single task well: to filter and aggregate data as fast as possible. That’s what needs to be done to build a typical analytical report and that’s what a typical [GROUP BY](../../sql-reference/statements/select/group-by.md) query does. ClickHouse team has made several high-level decisions that combined made achieving this task possible:
+ClickHouse was initially built as a prototype to do just a single task well: to filter and aggregate data as fast as possible. That’s what needs to be done to build a typical analytical report, and that’s what a typical [GROUP BY](../../sql-reference/statements/select/group-by.md) query does. The ClickHouse team has made several high-level decisions that, when combined, made achieving this task possible:
 
-**Column-oriented storage:**   Source data often contain hundreds or even thousands of columns, while a report can use just a few of them. The system needs to avoid reading unnecessary columns, or most expensive disk read operations would be wasted.
+**Column-oriented storage:**   Source data often contain hundreds or even thousands of columns, while a report can use just a few of them. The system needs to avoid reading unnecessary columns to avoid expensive disk read operations.
 
-**Indexes:**  ClickHouse keeps data structures in memory that allows reading not only used columns but only necessary row ranges of those columns.
+**Indexes:**  Memory resident ClickHouse data structures allow the reading of only the necessary columns, and only the necessary row ranges of those columns.
 
-**Data compression:**   Storing different values of the same column together often leads to better compression ratios (compared to row-oriented systems) because in real data column often has the same or not so many different values for neighboring rows. In addition to general-purpose compression, ClickHouse supports [specialized codecs](../../sql-reference/statements/create/table.md/#specialized-codecs) that can make data even more compact.
+**Data compression:**   Storing different values of the same column together often leads to better compression ratios (compared to row-oriented systems) because in real data a column often has the same, or not so many different, values for neighboring rows. In addition to general-purpose compression, ClickHouse supports [specialized codecs](../../sql-reference/statements/create/table.md/#specialized-codecs) that can make data even more compact.
 
-**Vectorized query execution:**  ClickHouse not only stores data in columns but also processes data in columns. It leads to better CPU cache utilization and allows for [SIMD](https://en.wikipedia.org/wiki/SIMD) CPU instructions usage.
+**Vectorized query execution:**  ClickHouse not only stores data in columns but also processes data in columns. This leads to better CPU cache utilization and allows for [SIMD](https://en.wikipedia.org/wiki/SIMD) CPU instructions usage.
 
 **Scalability:**   ClickHouse can leverage all available CPU cores and disks to execute even a single query. Not only on a single server but all CPU cores and disks of a cluster as well.