I started reading the book called "Spark definitive guide-big data processing made simple" to learn Spark. While I was reading I saw a line saying "A DataFrame is the most common Structured API and simply represents a table of data with rows and columns." I am not able to understand why are RDDs and DataFrames being called APIs?

They're called APIs because they're essentially just different interfaces to exactly the same data. DataFrame can be built on top of RDD and RDD can be extracted from DataFrame. They just have different sets of functions defined on that data, main differences are semantics and the way you work with data, RDD being lower level API and DataFrame being higher level API. For example you can use Spark SQL interface with DataFrame which provides all common SQL functions, but if you decide to use RDDs, you would need to write SQL functions yourself using RDD transformations.

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From the user/developer point of view, RDDs & DataFrames are just like any other objects you might deal with in your scripts, but behind the scenes those "objects" are functioning as APIs - they're interfaces through which your program can dynamically interact with specific chunks of data that lives elsewhere (and is probably distributed across multiple external linked sources).

The field of computer science is experiencing a transition from computation-intensive to data-intensive problems, wherein data is produced in massive amounts by large sensor networks, new data acquisition techniques, simulations, and social networks.Efficiently extracting, interpreting, and learning from very large datasets requires a new generation of scalable algorithms as well as new data management technologies.

In this course we explore key data analysis and management techniques, which applied to massive datasets are the cornerstone that enables real-time decision making in distributed environments, business intelligence in the Web, and scientific discoveryat large scale. In particular, we examine the map-reduce parallel computing paradigm and associated technologies such as distributed file systems, no-sql databases, and stream computing engines. Additionally we review machine learning methodsthat make possible the efficient analysis of large volumes of data in near real time.

This course is highly interactive and based on the problem-based learning philosophy; students are expected to make use of said technologies to design highly scalable systems that can process and analyze Big Data for a variety of scientific, social,and environmental challenges.

The course is divided into three main core topics: (1) Introduction to the Big Data problem. Current challenges, trends, and applications. (2) Algorithms for Big Data analysis. Mining and learning algorithms that have been developed specifically to dealwith large datasets.(3) Technologies for Big Data management. Big Data technology and tools, special consideration made to the Map-Reduce paradigm and the Hadoop ecosystem.

At the end of this course, the student will become familiar with the fundamental concepts of Big Data management and analytics; will become competent in recognizing challenges faced by applications dealing with very large volumes of data as wellas in proposing scalable solutions for them; and will be able to understand how Big Data impacts business intelligence, scientific discovery, and our day-to-day life.

Participation is the barometer of the class. Based on it I can determine if the pace of the course is too fast or too slow, it helps me to spot pitfalls and misconceptions, and it helps you to reinforce the material you learned.

The student can expect to have simple exercises frequently. Some of these daily assignments will be done in groups specified by the instructor and they will account for the participation grade of the course. Make up assignments will be allowed only if the instructor or TA were informed of a documented absence before the quiz took place.

There will be a series of coding homework during the semester. For every homework students will turn in a two-page report and well documented code throughUNM Learn only, no emailed assignments will be graded and no late assignments will be accepted.

Exams are this course's formal evaluation tool. In the exams students will be tested with respect to the learning goals of this course. Exams will comprise a mix of practical exercises and concepts. There will be only one midterm exam at around 3/4 of the semester. The exam is open notes but only handwritten notes are allowed.

The final project is entirely to the discretion of the student (upon instructor approval). Students are free to explore a problem of their interest and propose their own solution. The project has the following deliverables:

Grades will be based on your earned points, following this grade scale. You need to get the specified number of points or more to obtain the grade from the same column. Scores will be rounded to the closest integer value.

Unless otherwise specified, you must write/code your own homework assignments. You cannot use the web to find answers to any assignment. If you do not have time to complete an assignment, it is better to submit your partial solutions than to get answers from someone else. Cheating students will be prosecuted according to University guidelines. Students should get acquainted with their rights and responsibilities as explained in the Student Code of Conduct

Instances of plagiarism include, but are not limited to: downloading code and snippets from the Internet withoutexplicit permission from the instructor and/or without proper acknowledgment, citation, or license use; using code from a classmate or any other past or present student; quoting text directly or slightly paraphrasing from a source without proper reference; any other act of copying material and trying to make it look like it is yours.

The best way of avoiding plagiarism is to start your assignments early. Whenever you feel like you cannot keep up with the course material, your instructor is happy to find a way to help you. Make an appointment or come to office hours, but DO NOT plagiarize; it is not worth it!.

Attendance to class is expected (read mandatory) and note taking encouraged. Important information (about exams, assignments, projects, policies) may be communicated only during lecture time. We may also cover additional material (not available in the book or in slides) during the lecture.

If you miss a lecture, you should find what material was covered and if any announcement was made. If you have unexcused absences, this may result in participation points being deducted. Excused absences include sickness, attending conferences, job interviews, and similar. Even if your absence is excused, it is your responsibility to find out what material you missed. The professor is happy to answer specific questions regarding the lecture, but cannot go through all of the missed material on a one-to-one basis.

In order to facilitate interaction between students and to promote a broader participation, I created a Piazza group. Use the Piazza public group to askgeneral questions about homework, exams, projects, and lectures. You can also paste small snippets of code to clarify an idea. Students are encouraged to answer each others questions. Recall that your thoughtful participation in this forum accounts through your final grade. Use Piazza private posts to ask for excused absences and other personal matters. Always cc the class TA in those cases.Piazza is a discussion forum for the class and members are expected to conduct themselves with respect by posting comments and replies only in the context of the course.

I value student's opinions regarding the course and I will take them into consideration to make this course as exciting and engaging as possible. Thus, through the semester I will ask students formal and informal feedback. Formal feedback includes short surveys on my teaching effectiveness, preferred teaching methods, and the pace of the class. Informal feedback will be in the form of polls or in-class questions regarding learning preferences. You can also leave anonymous feedback in the form of a note in my departmental mailbox, under my office door, or using this form. Remember that it is in the best interest of the class if you bring up to my attention if something is not working properly (e.g the pace of the class is too slow, the projects are boring, my teaching style is not effective) so that I can make the corrective steps.

In accordance with University Policy 2310 and the Americans with Disabilities Act (ADA), academic accommodations may be made for any student who notifies the instructor of the need for an accommodation. If you have a disability, either permanentor temporary, contact Accessibility Resource Center at 277-3506 for additional information.

Internally, it works as follows. Spark Streaming receives live input data streams and dividesthe data into batches, which are then processed by the Spark engine to generate the finalstream of results in batches.

Spark Streaming provides a high-level abstraction called discretized stream or DStream,which represents a continuous stream of data. DStreams can be created either from input datastreams from sources such as Kafka, and Kinesis, or by applying high-leveloperations on other DStreams. Internally, a DStream is represented as a sequence ofRDDs.

This guide shows you how to start writing Spark Streaming programs with DStreams. You canwrite Spark Streaming programs in Scala, Java or Python (introduced in Spark 1.2),all of which are presented in this guide.You will find tabs throughout this guide that let you choose between code snippets ofdifferent languages.

flatMap is a one-to-many DStream operation that creates a new DStream bygenerating multiple new records from each record in the source DStream. In this case,each line will be split into multiple words and the stream of words is represented as thewords DStream. Next, we want to count these words. 152ee80cbc

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