editJingjing-Abhilash-bigdata

1 files changed, 2 insertions, 2 deletions

diff --git a/chapter/8/big-data.md b/chapter/8/big-data.md
index 111b3a8..f51198f 100644
--- a/chapter/8/big-data.md
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@@ -119,7 +119,7 @@ The SiteData example{%cite chambers2010flumejava --file big-data %} shows that a
 
 
 ### 1.1.3 Dryad
-Dryad is a more general and flexible execution engine that execute subroutines at a specified graph vertices. Developers can specify an arbitrary directed acyclic graph to combine computational "vertices" with communication channels (file, TCP pipe, shared-memory FIFO) and  build a dataflow graph. Compared with MapReduce, Dryad can specify an arbitrary DAG that have multiple number of inputs/outputs and support multiple stages. Also it can have more channels and boost the performance when using TCP pipes and shared-memory. But like writing a pipeline of MapReduce jobs, Dryad is a low-level programming model and hard for users to program, thus a more declarative model - DryadLINQ  {%cite yu2008dryadlinq --file big-data %} was created to fill in the gap. It exploits LINQ, a query language in .NET and automatically translates the data-parallel part into execution plan and passed to the Dryad execution engine. Like MR, writing raw Dryad is hard, programmers need to understand system resources and other lower-level details. This motivates a more declarative programming model: DryadLINQ - a querying language.
+Dryad is a more general and flexible execution engine than MapReduce? that execute subroutines at a specified graph vertices. Developers can specify an arbitrary directed acyclic graph to combine computational "vertices" with communication channels (file, TCP pipe, shared-memory FIFO) and  build a dataflow graph. Compared with MapReduce, Dryad can specify an arbitrary DAG that have multiple number of inputs/outputs and support multiple stages. Also it can have more channels and boost the performance when using TCP pipes and shared-memory. But like writing a pipeline of MapReduce jobs, Dryad is a low-level programming model and hard for users to program, thus a more declarative model - DryadLINQ  {%cite yu2008dryadlinq --file big-data %} was created to fill in the gap. It exploits LINQ, a query language in .NET and automatically translates the data-parallel part into execution plan and passed to the Dryad execution engine. Like MR, writing raw Dryad is hard, programmers need to understand system resources and other lower-level details. This motivates a more declarative programming model: DryadLINQ - a querying language.
 
 ### 1.1.4 Spark
 
@@ -461,7 +461,7 @@ Hence, in Spark SQL, transformation of user queries happens in four phases :
 ## 3. Big Data Ecosystem
 *Hadoop Ecosystem*  
 
-Apache Hadoop is an open-sourced framework that supports distributed processing of large dataset. It involves a long list of projects that you can find in this table https://hadoopecosystemtable.github.io/. In this section, it is also important to understand the key players in the system, namely two parts: the Hadoop Distributed File System (HDFS) and the open-sourced implementation of MapReduce model - Hadoop.
+Apache Hadoop is an open-sourced framework that supports distributed processing of large dataset. It involves dozens of projects, all of which are listed [here](https://hadoopecosystemtable.github.io/). In this section, it is also important to understand the key players in the system, namely two parts: the Hadoop Distributed File System (HDFS) and the open-sourced implementation of MapReduce model - Hadoop.
 
 <figure class="main-container">
   <img src="./hadoop-ecosystem.jpg" alt="Hadoop Ecosystem" />