Spark Summit EU talk by Johnathan Mercer

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Spark Summit

Solving the App Market with Only Grit, Hustle, and a Spark Cluster

Data & Analytics

Apptopia is a mobile app
intelligence company
6

7
Company Overview
>$4m raised from major investors like:
~40 employees
~6 years in business
Trusted by > 35,000 publishers
worldwide.

8
Standing “on the shoulders of giants”
Eli Sapir
Co-Founder & CEO
Jonathan Kay
Co-Founder & COO
Serge Balyuk
VP of Engineering

apptopia.com 9
We power mobile intel data for the best

Apptopia empowers mobile
stakeholders
10

apptopia.com 11
There is so much more to mobile than games

12
User demographics and behavior
3%97% 40% 60%
GoToWebinarWealthfront

14
Competitive intelligence
5M
10M
15M
20M
25M
Worldwide Downloads

15
Investment decisions
2M
4M
6M
8M
10M
12M
Monthly Active Users

Apptopia is on a mission to
solve the app market
17

19
We combine public and proprietary data
Public Data
Ranks
Ratings
Reviews
Price
Publisher
Descriptions
Screenshots
Versions
Historical
Performance of
>200K Apps

20
Public data is predictive of performance

21
Rank is the most predictive feature
Instagram

22
Rank is also the most complex (i.e., fun)
feature
App
. . .
. . .
Downloads Revenue

23
There are many categories and sub-categories
https://developer.apple.com/app-store/categories/

24
The constellation of ranks is complex and
constantly evolving

25
We build models with data from
connected apps
Connected Apps
All Apps

28
First generation training and scoring
Rserve

Slow scoring (~2 months)
29
This was not a viable path forward

Systematically compare
hundreds of models using
20x more data
32

Spark and other open source
tools have transformed us
33

The global app market is a
complex dynamical system
36

37
AppSimilarityBundled’nessCompetitiveness

First solve for the human
side of the equation
40

Avoidance should precede
optimization
41

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Since the invention of SQL and relational databases, data production has been about specifying how data is transformed through queries. While Apache Spark can certainly be used as a general distributed query engine, the power and granularity of Spark’s APIs enables a revolutionary increase in data engineering productivity: goal-based data production. Goal-based data production concerns itself with specifying WHAT the desired result is, leaving the details of HOW the result is achieved to a smart data warehouse running on top of Spark. That not only substantially increases productivity, but also significantly expands the audience that can work directly with Spark: from developers and data scientists to technical business users. With specific data and architecture patterns spanning the range from ETL to machine learning data prep and with live demos, this session will demonstrate how Spark users can gain the benefits of goal-based data production.

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Spark Summit

Have you imagined a simple machine learning solution able to prevent revenue leakage and monitor your distributed application? To answer this question, we offer a practical and a simple machine learning solution to create an intelligent monitoring application based on simple data analysis using Apache Spark MLlib. Our application uses linear regression models to make predictions and check if the platform is experiencing any operational problems that can impact in revenue losses. The application monitor distributed systems and provides notifications stating the problem detected, that way users can operate quickly to avoid serious problems which directly impact the company’s revenue and reduce the time for action. We will present an architecture for not only a monitoring system, but also an active actor for our outages recoveries. At the end of the presentation you will have access to our training program source code and you will be able to adapt and implement in your company. This solution already helped to prevent about US$3mi in losses last year.

Preventing Revenue Leakage and Monitoring Distributed Systems with Machine Le...

Spark Summit

Getting Ready to use Redis with Apache Spark is a technical tutorial designed to address integrating Redis with an Apache Spark deployment to increase the performance of serving complex decision models. To set the context for the session, we start with a quick introduction to Redis and the capabilities Redis provides. We cover the basic data types provided by Redis and cover the module system. Using an ad serving use-case, we look at how Redis can improve the performance and reduce the cost of using complex ML-models in production. Attendees will be guided through the key steps of setting up and integrating Redis with Spark, including how to train a model using Spark then load and serve it using Redis, as well as how to work with the Spark Redis module. The capabilities of the Redis Machine Learning Module (redis-ml) will be discussed focusing primarily on decision trees and regression (linear and logistic) with code examples to demonstrate how to use these feature. At the end of the session, developers should feel confident building a prototype/proof-of-concept application using Redis and Spark. Attendees will understand how Redis complements Spark and how to use Redis to serve complex, ML-models with high performance.

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Spark Summit

Here we present a general supervised framework for record deduplication and author-disambiguation via Spark. This work differentiates itself by – Application of Databricks and AWS makes this a scalable implementation. Compute resources are comparably lower than traditional legacy technology using big boxes 24/7. Scalability is crucial as Elsevier’s Scopus data, the biggest scientific abstract repository, covers roughly 250 million authorships from 70 million abstracts covering a few hundred years. – We create a fingerprint for each content by deep learning and/or word2vec algorithms to expedite pairwise similarity calculation. These encoders substantially reduce compute time while maintaining semantic similarity (unlike traditional TFIDF or predefined taxonomies). We will briefly discuss how to optimize word2vec training with high parallelization. Moreover, we show how these encoders can be used to derive a standard representation for all our entities namely such as documents, authors, users, journals, etc. This standard representation can simplify the recommendation problem into a pairwise similarity search and hence it can offer a basic recommender for cross-product applications where we may not have a dedicate recommender engine designed. – Traditional author-disambiguation or record deduplication algorithms are batch-processing with small to no training data. However, we have roughly 25 million authorships that are manually curated or corrected upon user feedback. Hence, it is crucial to maintain historical profiles and hence we have developed a machine learning implementation to deal with data streams and process them in mini batches or one document at a time. We will discuss how to measure the accuracy of such a system, how to tune it and how to process the raw data of pairwise similarity function into final clusters. Lessons learned from this talk can help all sort of companies where they want to integrate their data or deduplicate their user/customer/product databases.

Deduplication and Author-Disambiguation of Streaming Records via Supervised M...

Spark Summit

The use of large-scale machine learning and data mining methods is becoming ubiquitous in many application domains ranging from business intelligence and bioinformatics to self-driving cars. These methods heavily rely on matrix computations, and it is hence critical to make these computations scalable and efficient. These matrix computations are often complex and involve multiple steps that need to be optimized and sequenced properly for efficient execution. This work presents new efficient and scalable matrix processing and optimization techniques based on Spark. The proposed techniques estimate the sparsity of intermediate matrix-computation results and optimize communication costs. An evaluation plan generator for complex matrix computations is introduced as well as a distributed plan optimizer that exploits dynamic cost-based analysis and rule-based heuristics The result of a matrix operation will often serve as an input to another matrix operation, thus defining the matrix data dependencies within a matrix program. The matrix query plan generator produces query execution plans that minimize memory usage and communication overhead by partitioning the matrix based on the data dependencies in the execution plan. We implemented the proposed matrix techniques inside the Spark SQL, and optimize the matrix execution plan based on Spark SQL Catalyst. We conduct case studies on a series of ML models and matrix computations with special features on different datasets. These are PageRank, GNMF, BFGS, sparse matrix chain multiplications, and a biological data analysis. The open-source library ScaLAPACK and the array-based database SciDB are used for performance evaluation. Our experiments are performed on six real-world datasets are: social network data ( e.g., soc-pokec, cit-Patents, LiveJournal), Twitter2010, Netflix recommendation data, and 1000 Genomes Project sample. Experiments demonstrate that our proposed techniques achieve up to an order-of-magnitude performance.

MatFast: In-Memory Distributed Matrix Computation Processing and Optimization...

Spark Summit

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Spline: Apache Spark Lineage not Only for the Banking Industry with Marek Nov...

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Preventing Revenue Leakage and Monitoring Distributed Systems with Machine Le...

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Spark Summit EU talk by Johnathan Mercer

1. Solving the app market with only grit, hustle, and a Spark cluster Johnathan Mercer VP of Analytics mercer@apptopia.com

2. @Apptopia

3. 30 minutes is a lot to ask 3

4. 4 I want to fill your pockets

5. 5 Who are we?

6. Apptopia is a mobile app intelligence company 6

7. 7 Company Overview >$4m raised from major investors like: ~40 employees ~6 years in business Trusted by > 35,000 publishers worldwide.

8. 8 Standing “on the shoulders of giants” Eli Sapir Co-Founder & CEO Jonathan Kay Co-Founder & COO Serge Balyuk VP of Engineering

9. apptopia.com 9 We power mobile intel data for the best

10. Apptopia empowers mobile stakeholders 10

11. apptopia.com 11 There is so much more to mobile than games

12. 12 User demographics and behavior 3%97% 40% 60% GoToWebinarWealthfront

13. 13 Connecting the dots

14. 14 Competitive intelligence 5M 10M 15M 20M 25M Worldwide Downloads

15. 15 Investment decisions 2M 4M 6M 8M 10M 12M Monthly Active Users

16. 16 Advertising optimization

17. Apptopia is on a mission to solve the app market 17

18. has empowered us. 18

19. 19 We combine public and proprietary data Public Data Ranks Ratings Reviews Price Publisher Descriptions Screenshots Versions Historical Performance of >200K Apps

20. 20 Public data is predictive of performance

21. 21 Rank is the most predictive feature Instagram

22. 22 Rank is also the most complex (i.e., fun) feature App . . . . . . Downloads Revenue

23. 23 There are many categories and sub-categories https://developer.apple.com/app-store/categories/

24. 24 The constellation of ranks is complex and constantly evolving

25. 25 We build models with data from connected apps Connected Apps All Apps

26. 26 For any app, day, country

27. How 27 transformed us

28. 28 First generation training and scoring Rserve

29. Slow scoring (~2 months) 29 This was not a viable path forward

30. 30 Project Khaleesi Dev

31. 2 months to 2 days 31

32. Systematically compare hundreds of models using 20x more data 32

33. Spark and other open source tools have transformed us 33

34. 34

35. 35 Where do we go from here?

36. The global app market is a complex dynamical system 36

38. 38 Focus as we scale

39. Here’s 4 things we learned 39

40. First solve for the human side of the equation 40

41. Avoidance should precede optimization 41

42. Make big data small data 42

43. Edges are more important than nodes 43

44. Thank You

45. Solving the app market with only grit, hustle, and a Spark cluster Johnathan Mercer VP of Analytics mercer@apptopia.com

Spark Summit EU talk by Johnathan Mercer

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