Added references, corrected sentences, strengthen arguments

1 files changed, 7 insertions, 7 deletions

diff --git a/chapter/8/big-data.md b/chapter/8/big-data.md
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@@ -142,9 +142,9 @@ 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[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.
+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.
 
-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.
+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.
 
@@ -256,7 +256,7 @@ Map Reduce doesn’t scale easily and is highly inefficient for iterative / grap
 
 Also graph algorithms require exchange of messages between vertices. In case of PageRank, every vertex requires the contributions from all its adjacent nodes to calculate its score. Map reduce currently lacks this model of message passing which makes it complex to reason about graph algorithms. One model that is commonly employed for implementing distributed graph processing is the graph parallel model.
 
-In the graph-parallel abstraction, a user-defined vertex program is instantiated concurrently for each vertex and interacts with adjacent vertex programs through messages or shared state. Each vertex program can read and modify its vertex property and in some cases adjacent vertex properties. When all vertex programs vote to halt the program terminates. Most systems adopt the bulk synchronous parallel model
+In the graph-parallel abstraction, a user-defined vertex program is instantiated concurrently for each vertex and interacts with adjacent vertex programs through messages or shared state. Each vertex program can read and modify its vertex property and in some cases adjacent vertex properties. When all vertex programs vote to halt the program terminates. Most systems adopt the bulk synchronous parallel model {% cite bulk-synchronous-model --file big-data%}.
 
 This model was introduced in 1980 to represent the hardware design features of parallel computers. It gained popularity as an alternative for map reduce since it addressed the above mentioned issues with map reduce<br />
 BSP model is a message passing synchronous model where -
@@ -378,7 +378,7 @@ Optimization logic consists of a chain of transformation operations such that ou
 - Predicate Pushdown - Predicates are pushed down to the scan so that rows are filtered as early as possible.
 - Partition Pruning - Predicates on partitioned columns are used to prune out files of partitions that do not satisfy the predicate.
 - Map Side Joins - In case the tables involved in the join are very small, the tables are replicated in all the mappers and the reducers.
-- Join Reordering - Large tables are streamed and not materialized in-memory in the reducer to reduce memory requirements.Some optimizations are not enabled by default but can be activated by setting certain flags. These include:
+- Join Reordering - Large tables are streamed and not materialized in-memory in the reducer to reduce memory requirements.Some optimizations are not enabled by default but can be activated by setting certain flags.
 - Repartitioning data to handle skew in GROUP BY processing.This is achieved by performing GROUP BY in two MapReduce stages - first where data is distributed randomly to the reducers and partial aggregation is performed. In the second stage, these partial aggregations are distributed on GROUP BY columns to different reducers.
 - Hash bases partial aggregations in the mappers to reduce the data that is sent by the mappers to the reducers which help in reducing the amount of time spent in sorting and merging the resulting data.
 
@@ -431,7 +431,7 @@ To satisfy different needs, big companies like Facebook and Yahoo developed addi
 
 ***Spark Ecosystem***
 
-Apache Spark's rich-ecosystem constitutes of third party libraries like Mesos{%cite hindman2011mesos --file big-data%}/Yarn{%cite vavilapalli2013apache --file big-data%} and several major components that have been already discussed in this articlelike Spark-core, SparkSQL, GraphX.
+Apache Spark's rich-ecosystem constitutes of third party libraries like Mesos{%cite hindman2011mesos --file big-data%}/Yarn{%cite vavilapalli2013apache --file big-data%} and several major components that have been already discussed in this article like Spark-core, SparkSQL, GraphX.
 In this section we will discuss the remaining yet very important components/libraries which help Spark deliver high performance.
 
 <figure class="main-container">
@@ -440,7 +440,7 @@ In this section we will discuss the remaining yet very important components/libr
 
 *Spark Streaming - A Spark component for streaming workloads*
 
-Spark achieves fault tolerant, high throughput data streaming workloads in real-time through a light weight Spark Streaming API. Spark streaming is based on Discretized Streams model. Spark Streaming processes streaming workloads as a series of small batch workloads by leveraging the fast scheduling capacity of Apache Spark Core and fault tolerance capabilities of a RDD. A RDD in here represents each batch of streaming data and transformations are applied on the same. Data source in Spark Streaming could be from many a live streams like Twitter, Apache Kafka, Akka Actors, IoT Sensors, Amazon Kinesis, Apache Flume, etc. Spark streaming also enables unification of batch and streaming workloads and hence developers can use the same code for both batch and streaming workloads. It supports integration of streaming data with historical data.
+Spark achieves fault tolerant, high throughput data streaming workloads in real-time through a light weight Spark Streaming API. Spark streaming is based on Discretized Streams model{% cite d-streams --file big-data%}. Spark Streaming processes streaming workloads as a series of small batch workloads by leveraging the fast scheduling capacity of Apache Spark Core and fault tolerance capabilities of a RDD. A RDD in here represents each batch of streaming data and transformations are applied on the same. Data source in Spark Streaming could be from many a live streams like Twitter, Apache Kafka, Akka Actors, IoT Sensors, Amazon Kinesis, Apache Flume, etc. Spark streaming also enables unification of batch and streaming workloads and hence developers can use the same code for both batch and streaming workloads. It supports integration of streaming data with historical data.
 
 
 *Apache Mesos*
@@ -450,7 +450,7 @@ Apache Mesos{%cite hindman2011mesos --file big-data%} is an open source cluster/
 
 *Alluxio/Tachyon*
 
-Alluxio/Tachyon is an open source memory-centric distributed storage system that provides high throughput writes and reads enabling reliable data sharing at memory-speed across cluster jobs. Tachyon can integrate with  different computation frameworks, such as Apache Spark and Apache MapReduce. In the big data ecosystem, Tachyon fits between computation frameworks or jobs like spark or mapreducce and various kinds of storage systems, such as Amazon S3, OpenStack Swift, GlusterFS, HDFS, or Ceph. It caches the frequently read datasets in memory, thereby avoiding going to disk to load every dataset. In Spark RDDs can automatically be stored inside Tachyon to make Spark more resilient and avoid GC overheads.
+Alluxio/Tachyon{% cite Tachyon --file big-data%} is an open source memory-centric distributed storage system that provides high throughput writes and reads enabling reliable data sharing at memory-speed across cluster jobs. Tachyon can integrate with  different computation frameworks, such as Apache Spark and Apache MapReduce. In the big data ecosystem, Tachyon fits between computation frameworks or jobs like spark or mapreducce and various kinds of storage systems, such as Amazon S3, OpenStack Swift, GlusterFS, HDFS, or Ceph. It caches the frequently read datasets in memory, thereby avoiding going to disk to load every dataset. In Spark RDDs can automatically be stored inside Tachyon to make Spark more resilient and avoid GC overheads.