Updating query section

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 - `Debugging and profiling` : There is no availability of debugging tools and developers find it hard to realize if a computation is happening more on a single machine or if the data-structure they used were inefficient.
 
 ### 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{% cite scaling-spark-in-real-world --file big-data%}. 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.
+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 equi-joins and theta-joins [http://www.ccs.neu.edu/home/mirek/papers/2011-SIGMOD-ParallelJoins.pdf] which could be highly complex depending on the data, require programmers to implement by hand. Hence, map reduce lacks many such high level abstractions  requiring programmers to be well versed with several of the design patterns like map-side joins, reduce-side equi-join etc. Also, java based code ( like in Hadoop framework) in map-reduce can sometimes become repetitive when the programmer wants to implement most common operations like projection, filtering etc. A simple word count program as shown in Figure X, can span up to 200 lines.
+
+*Why SQL over map reduce ?*
+
+SQL already provides several operations like join, group by, sort which can be mapped to the above mentioned map reduce operations. Also, by leveraging SQL like interface, it becomes easy for non map reduce experts/non-programmers like data scientists to focus more on logic than hand coding complex operations {% cite scaling-spark-in-real-world --file big-data%}. Such an high level declarative language can easily express their task while leaving all of the execution optimization details to the backend engine. 
+SQL also lessens the amount of code (code examples can be seen in individual model’s section) and significantly reduces the development time.
+Most importantly, as you will read further in this section, frameworks like Pig, Hive, Spark SQL take advantage of these declarative queries by realizing them as a DAG upon which the compiler can apply transformation if an optimization rule is satisfied. Spark which does provide high level abstraction unlike map reduce, lacks this very optimization resulting in several human errors as discussed in the Spark’s data-parallel section.
 
 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 emit the intermediate pairs to external pre-built aggregators.
 
 Apart from Sawzal, Pig  {%cite olston2008pig --file big-data %} and Hive  {%cite thusoo2009hive --file big-data %} 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.
 
-Hive 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  {%cite thusoo2010hive --file big-data %} 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.
+Hive 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  {%cite thusoo2010hive --file big-data %} 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.
 
 Relational interface to big data is good, however, it doesn’t cater to users who want to perform