CSED/DATA 516 - Final Project Presentations


1
2		CSED/DATA 516 - Final Project Presentations
3		December 5th 2017
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6	Presentation #	Time	Title	Presenter	Link to slides
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8	1	5:00	Data Ingestion in Amazon Redshift & Runtime between GMM and K-Mean in Spark with various Partitions	Zicong Liang	https://docs.google.com/presentation/d/1lmY96X2Tgh8udvBC8BD72ebkUXn9jHU80WjLIWZZk5w/edit#slide=id.g2a3565ada7_2_142
9	2	5:10	Parallel Processing Performance in Amazon Redshift/EMR and Apache Spark	Rex Thompson	https://docs.google.com/presentation/d/16vl1-21um8CXMbR4gHIBhH9L4o-ll-Ry9HVDglWX8GE/edit?usp=sharing
10	3	5:20	Analysis of Amazon Redshift Parallel Ingestion / Analysis of GMM Training Times Using Spark	Dane Jordan	https://drive.google.com/open?id=10-xMqyKkfSU4DolOVQuXDJuYBjpW4nxVNOn5hM6b_VM
11	4	5:30	Analysis of Query Run-Times on Distributed Platforms:Redshift, Hive and Spark	Samir Patel	https://drive.google.com/a/uw.edu/file/d/1nTiBYgwj5hWrsCTrhupnXOEPNmNNKfGR/view?usp=sharing
12	5	5:40		Diana Chenyu Zhang	https://docs.google.com/presentation/d/1mkPGaWidE2febuo4nJskNoShEAF30Li483fb1zckKKw/edit#slide=id.g2be3bb0631_0_34
13	6	5:50	Data Ingestion on Amazon Redshift & Compare python Spark	Runlai Zeng	https://docs.google.com/presentation/d/1yIz_X2oKaQC6u1uHUq_6u9IRvuuRK0NmP3XWe4iIwrI/edit#slide=id.gc6f9e470d_0_5
14	7	6:00	SQLWorkbenchJ vs Pycharm	Jahnavi Jasti	https://docs.google.com/presentation/d/1Od1qgLdn9bkxUtk0k2ZzHFdMAhzqi2ZpMWN_vQlhJaw/edit?usp=sharing
15		BREAK
16	8	6:20	Exploration of Query Optimization in Redshift and SparkSQL	Deepa Agrawal	https://docs.google.com/a/uw.edu/presentation/d/1faYvvCyL7GnleUptsSlKIbzVyt7wkZBIP4kwLhA5olY/edit?usp=sharing
17	9	6:30	Factors affect Data Ingestion and Query Execution on Amazon Redshift ; Apache Spark: RDD vs Dataframe	Angel Wang	https://docs.google.com/presentation/d/1d_S9sDtakUA_IwxOtuTnfDBBvZA2_hTl0fz9oGfVrxs/edit#slide=id.p14
18	10	6:40	Comparing Performance of Data Ingestion with Redshift and Cluster Model Training with Spark	Erin Orbits	https://docs.google.com/presentation/d/13k5ldHNebmGzyWj0BDWnzApRHjlZtmF0KqnAC5kCRAY/edit?usp=sharing
19	11	6:50	Experiment with Redshift, scikit-learn and MLlib	Jingyan Yang	https://docs.google.com/a/uw.edu/presentation/d/1g35ekIebuYT8zLFTLR4dej8SQrqo_NrwiFf7SI1ux24/edit?usp=sharing
20	12	7:00	Redshift Data Ingestion & Spark Data Processing	Haowen Ni	https://docs.google.com/presentation/d/1oKJ1EO5MPdXZDabcl9fdhIke9K-vgYhg7E-4CvNfGdI/edit#slide=id.p
21	13	7:10	Analysis of Parallel Data Ingestion on Amazon Redshift & Building A Recommendation System Using Elastic MapReduce and Spark	Mobing Zhuang	https://docs.google.com/a/uw.edu/presentation/d/14qjBeYiwVGqR5UCGYGh0_mqHgswc0iKCI1QMVllONvE/edit?usp=sharing
22	14	7:20		Yawen Li	TBD
23		BREAK
24	15	7:40	Impact of Dist/Sort Keys in Redshit, GMM vs KMeans vs Bisecting KMeans in Spark	Rajiv Veeraraghavan	https://docs.google.com/presentation/d/1d2ZOYKqCWaXfkEMtfPW1Y4G6ntgAHshrOeG6xQNm0VA/edit?usp=sharing
25	16	7:50	Performance of different types of queries on Redshift & Comparison of GMM performance on MLlib and Scikit-learn	Becky Wang	https://docs.google.com/presentation/d/16Xzte_9v6gL_4n6i7AYHI2VCMZpqVMjlbjlOXm_1W9Q/edit?usp=sharing
26	17	8:00	Scalable methods for recommending relevant products to users	Niharika Sharma	https://drive.google.com/open?id=1ojEGayyQ0fOS0BZsSBv8wny67KrmwFf5i7KacJkfHSo
27	18	8:10	Timing Tests with AWS	Gary Gregg	https://docs.google.com/presentation/d/1o0pGgD0i22AfEw4-ObNkLaotJKjTNCMt_GpZdr0xZiw/edit?usp=sharing
28	19	8:20	Scaling Redshift clusters in production+Video Game Vizualization is Spark	Abhishek Varma	https://docs.google.com/presentation/d/15sRM9P629P87gWi-s7QFjvkRUVM09viOQA4fCM3b9kk/edit?usp=sharing
29	20	8:30	Redshift, Spark and the Hive	Michael Grant	https://drive.google.com/file/d/1Fu8vw81tGgLVAkQDeOUdu9knMpEcDGnj/view?usp=sharing
30	21	8:40	Pyspark vs Python	Khyati Parekh	https://docs.google.com/presentation/d/1RHik2J8DEZtCc9CJFYF4rwJjxknmeKckSHZIDNohX8E/edit?usp=sharing
31		END

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December 5th 2017

4

5

6

Presentation #

Time

Title

Presenter

Link to slides

7

8

1

5:00

Data Ingestion in Amazon Redshift & Runtime between GMM and K-Mean in Spark with various Partitions

Zicong Liang

https://docs.google.com/presentation/d/1lmY96X2Tgh8udvBC8BD72ebkUXn9jHU80WjLIWZZk5w/edit#slide=id.g2a3565ada7_2_142

9

2

5:10

Parallel Processing Performance in Amazon Redshift/EMR and Apache Spark

Rex Thompson

https://docs.google.com/presentation/d/16vl1-21um8CXMbR4gHIBhH9L4o-ll-Ry9HVDglWX8GE/edit?usp=sharing

10

3

5:20

Analysis of Amazon Redshift Parallel Ingestion / Analysis of GMM Training Times Using Spark

Dane Jordan

https://drive.google.com/open?id=10-xMqyKkfSU4DolOVQuXDJuYBjpW4nxVNOn5hM6b_VM

11

4

5:30

Analysis of Query Run-Times on Distributed Platforms:Redshift, Hive and Spark

Samir Patel

https://drive.google.com/a/uw.edu/file/d/1nTiBYgwj5hWrsCTrhupnXOEPNmNNKfGR/view?usp=sharing

12

5

5:40

Diana Chenyu Zhang

https://docs.google.com/presentation/d/1mkPGaWidE2febuo4nJskNoShEAF30Li483fb1zckKKw/edit#slide=id.g2be3bb0631_0_34

13

6

5:50

Data Ingestion on Amazon Redshift & Compare python Spark

Runlai Zeng

https://docs.google.com/presentation/d/1yIz_X2oKaQC6u1uHUq_6u9IRvuuRK0NmP3XWe4iIwrI/edit#slide=id.gc6f9e470d_0_5

14

7

6:00

SQLWorkbenchJ vs Pycharm

Jahnavi Jasti

https://docs.google.com/presentation/d/1Od1qgLdn9bkxUtk0k2ZzHFdMAhzqi2ZpMWN_vQlhJaw/edit?usp=sharing

15

BREAK

16

8

6:20

Exploration of Query Optimization in Redshift and SparkSQL

Deepa Agrawal

https://docs.google.com/a/uw.edu/presentation/d/1faYvvCyL7GnleUptsSlKIbzVyt7wkZBIP4kwLhA5olY/edit?usp=sharing

17

9

6:30

Factors affect Data Ingestion and Query Execution on Amazon Redshift ; Apache Spark: RDD vs Dataframe

Angel Wang

https://docs.google.com/presentation/d/1d_S9sDtakUA_IwxOtuTnfDBBvZA2_hTl0fz9oGfVrxs/edit#slide=id.p14

18

10

6:40

Comparing Performance of Data Ingestion with Redshift and Cluster Model Training with Spark

Erin Orbits

https://docs.google.com/presentation/d/13k5ldHNebmGzyWj0BDWnzApRHjlZtmF0KqnAC5kCRAY/edit?usp=sharing

19

11

6:50

Experiment with Redshift, scikit-learn and MLlib

Jingyan Yang

https://docs.google.com/a/uw.edu/presentation/d/1g35ekIebuYT8zLFTLR4dej8SQrqo_NrwiFf7SI1ux24/edit?usp=sharing

20

12

7:00

Redshift Data Ingestion & Spark Data Processing

Haowen Ni

https://docs.google.com/presentation/d/1oKJ1EO5MPdXZDabcl9fdhIke9K-vgYhg7E-4CvNfGdI/edit#slide=id.p

21

13

7:10

Analysis of Parallel Data Ingestion on Amazon Redshift & Building A Recommendation System Using Elastic MapReduce and Spark

Mobing Zhuang

https://docs.google.com/a/uw.edu/presentation/d/14qjBeYiwVGqR5UCGYGh0_mqHgswc0iKCI1QMVllONvE/edit?usp=sharing

22

14

7:20

Yawen Li

TBD

23

BREAK

24

15

7:40

Impact of Dist/Sort Keys in Redshit, GMM vs KMeans vs Bisecting KMeans in Spark

Rajiv Veeraraghavan

https://docs.google.com/presentation/d/1d2ZOYKqCWaXfkEMtfPW1Y4G6ntgAHshrOeG6xQNm0VA/edit?usp=sharing

25

16

7:50

Performance of different types of queries on Redshift & Comparison of GMM performance on MLlib and Scikit-learn

Becky Wang

https://docs.google.com/presentation/d/16Xzte_9v6gL_4n6i7AYHI2VCMZpqVMjlbjlOXm_1W9Q/edit?usp=sharing

26

17

8:00

Scalable methods for recommending relevant products to users

Niharika Sharma

https://drive.google.com/open?id=1ojEGayyQ0fOS0BZsSBv8wny67KrmwFf5i7KacJkfHSo

27

18

8:10

Timing Tests with AWS

Gary Gregg

https://docs.google.com/presentation/d/1o0pGgD0i22AfEw4-ObNkLaotJKjTNCMt_GpZdr0xZiw/edit?usp=sharing

28

19

8:20

Scaling Redshift clusters in production+Video Game Vizualization is Spark

Abhishek Varma

https://docs.google.com/presentation/d/15sRM9P629P87gWi-s7QFjvkRUVM09viOQA4fCM3b9kk/edit?usp=sharing

29

20

8:30

Redshift, Spark and the Hive

Michael Grant

https://drive.google.com/file/d/1Fu8vw81tGgLVAkQDeOUdu9knMpEcDGnj/view?usp=sharing

30

21

8:40

Pyspark vs Python

Khyati Parekh

https://docs.google.com/presentation/d/1RHik2J8DEZtCc9CJFYF4rwJjxknmeKckSHZIDNohX8E/edit?usp=sharing

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