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@@ -44,7 +44,7 @@ by: "Jingjing and Abhilash"
 
 The MapReduce model is simple and powerful, and quickly became very popular among developers. However, when developers start writing real-world applications, they often end up chaining together MapReduce stages. The pipeline of MapReduce forces programmers to write additional coordinating codes, i.e. the development style goes backward from simple logic computation abstraction to lower-level coordination management. In map reduce, programmers need to reason about data representation on disk or in storage services such as a database. Besides, developers need to clearly understand the map reduce execution model  to do manual optimizations[ref]. **FlumeJava** {%cite chambers2010flumejava --file big-data%} library intends to provide support for developing data-parallel pipelines by abstracting away the complexity involved in data representation and implicitly handling the optimizations. It defers the evaluation, constructs an execution plan from parallel collections, optimizes the plan, and then executes underlying MR primitives. The optimized execution is comparable with hand-optimized pipelines, so there's no need to write raw MR programs directly.
 
-An alternative approach to data prallelism is to construct complex, multi-step directed acyclic graphs (DAGs) of work from the user instructions execute those DAGs all at once. This eliminates the costly synchronization required by MapReduce and makes applications much easier to build and reason about. Dryad, a Microsoft Research project used internally at Microsoft was one such project which leveraged this model of computation.
+An alternative approach to data prallelism is to construct complex, multi-step directed acyclic graphs (DAGs) of work from the user instructions and execute those DAGs all at once. This eliminates the costly synchronization required by MapReduce and makes applications much easier to build and reason about. Dryad, a Microsoft Research project used internally at Microsoft was one such project which leveraged this model of computation.
 
 Microsfot **Dryad** {% cite isard2007dryad --file big-data %} abstracts individual computational tasks as vertices, and constructs a communication graph between those vertices. What programmers need to do is to describe this DAG graph and let Dryad execution engine construct the execution plan and manage scheduling and optimization. One of the advantages of Dryad over MapReduce is that Dryad vertices can process an arbitrary number of inputs and outputs, while MR only supports a single input and a single output for each vertex. Besides the flexibility of computations, Dryad also supports different types of communication channel: file, TCP pipe and shared-memory FIFO.
 
@@ -190,13 +190,15 @@ Other benefits include the scheduling of tasks based on data locality to improve
 ### 1.2 Querying: declarative interfaces
 MapReduce provides only two high level primitives - map and reduce that the programmers have to worry about. MapReduce takes care of all the processing over a cluster, failure and recovery, data partitioning etc. However, the framework suffers from rigidity with respect to its one-input data format (key/value pair) and two-stage data flow.
 Several important patterns like joins (which could be highly complex depending on the data) are extremely hard to implement and reason about for a programmer. Sometimes the code could be become repetitive  when the programmer wants to implement most common operations like projection, filtering etc.
-Non-programmers like data scientists would highly prefer SQL like interface over a cumbersome and rigid framework. Such a high level declarative language can easily express their task while leaving all of the execution optimization details to the backend engine. Also, these kind of abstractions provide ample opportunities for query optimizations.
+Non-programmers like data scientists would highly prefer SQL like interface over a cumbersome and rigid framework[ref]. Such a high level declarative language can easily express their task while leaving all of the execution optimization details to the backend engine. Hence, these kind of abstractions provide ample opportunities for query optimizations.
 
 Sawzall {% cite pike2005interpreting --file big-data%} is a programming language built on top of MapReduce. It consists of a *filter* phase (map) and an *aggregation* phase (reduce). User program can specify the filter function, and emits the intermediate pairs to external pre-built aggregators.
 
-Hive {% cite thusoo2009hive --file big-data %} is built by Facebook to organize dataset in structured formats and still utilize the benefit of MapReduce framework. It has its own SQL-like language: HiveQL which is easy for anyone who understands SQL. It has a component called *metastore* that are created and reused each time the table is referenced by HiveQL like the way traditional warehousing solutions do.
+Apart from Sawzal, Pig and Hive are the other major components that sit on top of Hadoop framework for processing large data sets without the users having to write Java based MapReduce code.
 
-Pig Latin {% cite olston2008pig --file big-data%} aims at a sweet spot between declarative and procedural programming. For advanced programmers, SQL is unnatural to implement program logic and Pig Latin wants to dissemble the set of data transformation into a sequence of steps.
+Hive {% cite thusoo2009hive --file big-data %} is built by Facebook to organize dataset in structured formats and still utilize the benefit of MapReduce framework. It has its own SQL-like language: HiveQL which is easy for anyone who understands SQL. Hive reduces code complexity and eliminates lots of boiler plate that would otherwise be an overhead with Java based MapReduce approach.  It has a component called *metastore* that are created and reused each time the table is referenced by HiveQL like the way traditional warehousing solutions do. The drawback to using Hive is programmers have to be familiar with basic techniques and best practices for running their Hive queries at maximum speed as it depends on the Hive optimizer. Hive requires developers  train the Hive optimizer for efficient optimization of their queries.
+
+Pig Latin {% cite olston2008pig --file big-data%} aims at a sweet spot between declarative and procedural programming. For advanced programmers, SQL is unnatural to implement program logic and Pig Latin wants to dissemble the set of data transformation into a sequence of steps. This makes Pig more verbose than Hive. However, Pig offers 
 
 The following subsections will discuss Hive, Pig Latin, SparkSQL in details.
 
@@ -258,7 +260,7 @@ output = FOREACH big_groups GENERATE
 
 *Debugging Environment* Pig Latin has a novel interactive debugging environment that can generate a concise example data table to illustrate output of each step.
 
-### 1.2.x SparkSQL - Where Relational meets Procedural :
+### 1.2.x SparkSQL  :
 Relational interface to big data is good, however, it doesn’t cater to users who want to perform
 
 - ETL to and from various semi or unstructured data sources.