From Beginners to Experts, Data Wrangling for All

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The document discusses designing data preparation tools that can support users with different technical proficiencies, from non-technical users to expert users. It proposes using both visual "transform cards" and a script IDE mode to bridge the needs of different users. The tool would use progressive disclosure of scripting capabilities to ease non-technical users into more technical functions. A demo of the tool discussed implementing transform cards and ways to improve predictive data transformations through feedback.

Technology

Alon Bartur Joseph McKenneyJingshu Chen
Principal Product Manager User Experience Designer Senior Software Engineer
Hadoop Summit
June 9th, 2015
From Beginner to Expert:
Data Wrangling For All

DATA PRODUCT
Simplicity
DATA SOURCE
Complexity
People are the Bottleneck

2005 2007 2009 2011 2013 2015
(Abridged) History of Hadoop
YARN

2013 2014 2015
Recently Launched Hadoop Applications

““
LAUREN, DATA SCIENTIST
Preparing data for modeling or predictive analytics is painful, manual
and not repeatable across the organization. We need a more efficient
and reproducible way of preparing data for analysis.
““ I receive constant requests from the business for access to different data. I
need a faster way to produce the results they’re looking for in a
form that they can understand.
BEN, DATA PLATFORM ADMIN
Our Original Personas

$Abstractions Can Be Useful [a-z0-9!#$%&'*+/=?^_`{|}~-]+(?:.[a-z0-9!#$ %&'*+/=?^_`{|}~-]+)*@ (?:[a-z0-9](?:[a-z0-9-]*[a-z0-9])?.)+[a- z0-9](?:[a-z0-9-]*[a-z0-9])?$

$Abstractions Can Be Useful [a-z0-9!#$%&'*+/=?^_`{|}~-]+(?:.[a-z0-9!#$ %&'*+/=?^_`{|}~-]+)*@ (?:[a-z0-9](?:[a-z0-9-]*[a-z0-9])?.)+[a- z0-9](?:[a-z0-9-]*[a-z0-9])? {email}$

LevelofAbstraction
Technical Proﬁciency
MapReduce
Pig, Hive
/(2013-12-[0-9]+)/

Jingshu Chen
User Experience Designer
Designing for Multiple Personas

Start the design process from the extremes

Non
Technical
Start the design process from the extremes

Non
Technical
Super
Technical
Start the design process from the extremes

Start the design process from the extremes
Non
Technical
Super
Technical

Non Technical Super Technical
>
I have to learn how to script?
The technical guys do the scripting
“
”

Non Technical Super Technical
>
I have to learn how to script?
The technical guys do the scripting
“
”
Why would I learn a new product?
I could code this in two minutes
“
”

Non Technical Super Technical
transform data

Non Technical Super Technical
How might we empower users to
in a way that matches their mental model?
transform data

Non Technical Super Technical
Visual Transform Cards Script IDE Mode

Create consistent and seamless experience
Non Technical Super Technical

Create consistent and seamless experience
Non Technical Super Technical
Visual Transform Cards Script IDE Mode

Create consistent and seamless experience
Transform Builder
Progressive Script Disclosure
…
Predictive TransformationNon Technical Super Technical
Visual Transform Cards Script IDE Mode

Progressive Script Disclosure
Mini Full IDE

Building Flexible Interfaces
Joseph McKenney
Senior Software Engineer

LevelofAbstraction
Technical Proﬁciency
MapReduce
Pig, Hive
/(2013-12-[0-9]+)/
Predictive
Transformation

Interact
Predict
Preview
Improving Predictive Transformation

Improving Selections
Adjusting Selections Negative Selections Selection Management

Ofﬁces in SF & South Bay
We’re Hiring!
Stop by booth P04

Sign up for a free trial at trifacta.com/trial
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Specialized tools for machine learning development and model governance are becoming essential. MlFlow is an open source platform for managing the machine learning lifecycle. Just by adding a few lines of code in the function or script that trains their model, data scientists can log parameters, metrics, artifacts (plots, miscellaneous files, etc.) and a deployable packaging of the ML model. Every time that function or script is run, the results will be logged automatically as a byproduct of those lines of code being added, even if the party doing the training run makes no special effort to record the results. MLflow application programming interfaces (APIs) are available for the Python, R and Java programming languages, and MLflow sports a language-agnostic REST API as well. Over a relatively short time period, MLflow has garnered more than 3,300 stars on GitHub , almost 500,000 monthly downloads and 80 contributors from more than 40 companies. Most significantly, more than 200 companies are now using MLflow. We will demo MlFlow Tracking , Project and Model components with Azure Machine Learning (AML) Services and show you how easy it is to get started with MlFlow on-prem or in the cloud.

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Twitter's Data Platform is built using multiple complex open source and in house projects to support Data Analytics on hundreds of petabytes of data. Our platform support storage, compute, data ingestion, discovery and management and various tools and libraries to help users for both batch and realtime analytics. Our DataPlatform operates on multiple clusters across different data centers to help thousands of users discover valuable insights. As we were scaling our Data Platform to multiple clusters, we also evaluated various cloud vendors to support use cases outside of our data centers. In this talk we share our architecture and how we extend our data platform to use cloud as another datacenter. We walk through our evaluation process, challenges we faced supporting data analytics at Twitter scale on cloud and present our current solution. Extending Twitter's Data platform to cloud was complex task which we deep dive in this presentation.

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DataWorks Summit

At Comcast, our team has been architecting a customer experience platform which is able to react to near-real-time events and interactions and deliver appropriate and timely communications to customers. By combining the low latency capabilities of Apache Flink and the dataflow capabilities of Apache NiFi we are able to process events at high volume to trigger, enrich, filter, and act/communicate to enhance customer experiences. Apache Flink and Apache NiFi complement each other with their strengths in event streaming and correlation, state management, command-and-control, parallelism, development methodology, and interoperability with surrounding technologies. We will trace our journey from starting with Apache NiFi over three years ago and our more recent introduction of Apache Flink into our platform stack to handle more complex scenarios. In this presentation we will compare and contrast which business and technical use cases are best suited to which platform and explore different ways to integrate the two platforms into a single solution.

Event-Driven Messaging and Actions using Apache Flink and Apache NiFi

DataWorks Summit

Companies are increasingly moving to the cloud to store and process data. One of the challenges companies have is in securing data across hybrid environments with easy way to centrally manage policies. In this session, we will talk through how companies can use Apache Ranger to protect access to data both in on-premise as well as in cloud environments. We will go into details into the challenges of hybrid environment and how Ranger can solve it. We will also talk through how companies can further enhance the security by leveraging Ranger to anonymize or tokenize data while moving into the cloud and de-anonymize dynamically using Apache Hive, Apache Spark or when accessing data from cloud storage systems. We will also deep dive into the Ranger’s integration with AWS S3, AWS Redshift and other cloud native systems. We will wrap it up with an end to end demo showing how policies can be created in Ranger and used to manage access to data in different systems, anonymize or de-anonymize data and track where data is flowing.

Securing Data in Hybrid on-premise and Cloud Environments using Apache Ranger

DataWorks Summit

Advanced Big Data Processing frameworks have been proposed to harness the fast data transmission capability of Remote Direct Memory Access (RDMA) over high-speed networks such as InfiniBand, RoCEv1, RoCEv2, iWARP, and OmniPath. However, with the introduction of the Non-Volatile Memory (NVM) and NVM express (NVMe) based SSD, these designs along with the default Big Data processing models need to be re-assessed to discover the possibilities of further enhanced performance. In this talk, we will present, NRCIO, a high-performance communication runtime for non-volatile memory over modern network interconnects that can be leveraged by existing Big Data processing middleware. We will show the performance of non-volatile memory-aware RDMA communication protocols using our proposed runtime and demonstrate its benefits by incorporating it into a high-performance in-memory key-value store, Apache Hadoop, Tez, Spark, and TensorFlow. Evaluation results illustrate that NRCIO can achieve up to 3.65x performance improvement for representative Big Data processing workloads on modern data centers.

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Background: Some early applications of Computer Vision in Retail arose from e-commerce use cases - but increasingly, it is being used in physical stores in a variety of new and exciting ways, such as: ● Optimizing merchandising execution, in-stocks and sell-thru ● Enhancing operational efficiencies, enable real-time customer engagement ● Enhancing loss prevention capabilities, response time ● Creating frictionless experiences for shoppers Abstract: This talk will cover the use of Computer Vision in Retail, the implications to the broader Consumer Goods industry and share business drivers, use cases and benefits that are unfolding as an integral component in the remaking of an age-old industry. We will also take a ‘peek under the hood’ of Computer Vision and Deep Learning, sharing technology design principles and skill set profiles to consider before starting your CV journey. Deep learning has matured considerably in the past few years to produce human or superhuman abilities in a variety of computer vision paradigms. We will discuss ways to recognize these paradigms in retail settings, collect and organize data to create actionable outcomes with the new insights and applications that deep learning enables. We will cover the basics of object detection, then move into the advanced processing of images describing the possible ways that a retail store of the near future could operate. Identifying various storefront situations by having a deep learning system attached to a camera stream. Such things as; identifying item stocks on shelves, a shelf in need of organization, or perhaps a wandering customer in need of assistance. We will also cover how to use a computer vision system to automatically track customer purchases to enable a streamlined checkout process, and how deep learning can power plausible wardrobe suggestions based on what a customer is currently wearing or purchasing. Finally, we will cover the various technologies that are powering these applications today. Deep learning tools for research and development. Production tools to distribute that intelligence to an entire inventory of all the cameras situation around a retail location. Tools for exploring and understanding the new data streams produced by the computer vision systems. By the end of this talk, attendees should understand the impact Computer Vision and Deep Learning are having in the Consumer Goods industry, key use cases, techniques and key considerations leaders are exploring and implementing today.

Computer Vision: Coming to a Store Near You

DataWorks Summit

Whole genome shotgun based next generation transcriptomics and metagenomics studies often generate 100 to 1000 gigabytes (GB) sequence data derived from tens of thousands of different genes or microbial species. De novo assembling these data requires an ideal solution that both scales with data size and optimizes for individual gene or genomes. Here we developed an Apache Spark-based scalable sequence clustering application, SparkReadClust (SpaRC), that partitions the reads based on their molecule of origin to enable downstream assembly optimization. SpaRC produces high clustering performance on transcriptomics and metagenomics test datasets from both short read and long read sequencing technologies. It achieved a near linear scalability with respect to input data size and number of compute nodes. SpaRC can run on different cloud computing environments without modifications while delivering similar performance. In summary, our results suggest SpaRC provides a scalable solution for clustering billions of reads from the next-generation sequencing experiments, and Apache Spark represents a cost-effective solution with rapid development/deployment cycles for similar big data genomics problems.

Big Data Genomics: Clustering Billions of DNA Sequences with Apache Spark

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From Beginners to Experts, Data Wrangling for All

1. Alon Bartur Joseph McKenneyJingshu Chen Principal Product Manager User Experience Designer Senior Software Engineer Hadoop Summit June 9th, 2015 From Beginner to Expert: Data Wrangling For All

3. Data is Messy

4. Data is Messy

5. DATA PRODUCT Simplicity DATA SOURCE Complexity People are the Bottleneck

6. Predictive Transformation

7. 2005 2007 2009 2011 2013 2015 (Abridged) History of Hadoop YARN

8. 2013 2014 2015 Recently Launched Hadoop Applications

9. ““ LAUREN, DATA SCIENTIST Preparing data for modeling or predictive analytics is painful, manual and not repeatable across the organization. We need a more efficient and reproducible way of preparing data for analysis. ““ I receive constant requests from the business for access to different data. I need a faster way to produce the results they’re looking for in a form that they can understand. BEN, DATA PLATFORM ADMIN Our Original Personas

10.

11.

12.

13. /(2013-12-[0-9]+)/

14. Abstractions Can Be Useful

15. Abstractions Can Be Useful [a-z0-9!#$%&'*+/=?^_`{|}~-]+(?:.[a-z0-9!#$ %&'*+/=?^_`{|}~-]+)*@ (?:[a-z0-9](?:[a-z0-9-]*[a-z0-9])?.)+[a- z0-9](?:[a-z0-9-]*[a-z0-9])?

16. Abstractions Can Be Useful [a-z0-9!#$%&'*+/=?^_`{|}~-]+(?:.[a-z0-9!#$ %&'*+/=?^_`{|}~-]+)*@ (?:[a-z0-9](?:[a-z0-9-]*[a-z0-9])?.)+[a- z0-9](?:[a-z0-9-]*[a-z0-9])? {email}

17. ““ LAUREN, DATA SCIENTIST Preparing data for modeling or predictive analytics is painful, manual and not repeatable across the organization. We need a more efficient and reproducible way of preparing data for analysis. ““ I receive constant requests from the business for access to different data. I need a secure way of pointing them directly to the raw data so they can explore and prepare it themselves. BEN, DATA PLATFORM ADMIN Today’s Personas

18. ““ LAUREN, DATA SCIENTIST Preparing data for modeling or predictive analytics is painful, manual and not repeatable across the organization. We need a more efficient and reproducible way of preparing data for analysis. ““ I receive constant requests from the business for access to different data. I need a secure way of pointing them directly to the raw data so they can explore and prepare it themselves. BEN, DATA PLATFORM ADMIN ““ JOHN, DATA ANALYST I want direct access to the raw data so I can actually see the content of different datasets to define my analytic requirements. Today’s Personas

19. LevelofAbstraction Technical Proﬁciency

20. LevelofAbstraction Technical Proﬁciency MapReduce Pig, Hive /(2013-12-[0-9]+)/

21. LevelofAbstraction Technical Proﬁciency MapReduce Pig, Hive ? /(2013-12-[0-9]+)/

22. Supporting Multiple Personas is Hard

23. Supporting Multiple Personas is Hard

24. Supporting Multiple Personas is Hard

25. Supporting Multiple Personas is Hard

26. So why try?

27. So why try? …to speak a common language

28. Jingshu Chen User Experience Designer Designing for Multiple Personas

29. Start the design process from the extremes

30. Start the design process from the extremes

31. Non Technical Start the design process from the extremes

32. Non Technical Super Technical Start the design process from the extremes

33. Non Technical Super Technical Start the design process from the extremes ?

34. Non Technical Super Technical Start the design process from the extremes

35. Non Technical Super Technical Start the design process from the extremes

36. Non Technical Super Technical Start the design process from the extremes

37. Start the design process from the extremes Non Technical Super Technical

38. Start the design process from the extremes Non Technical Super Technical

39. Start the design process from the extremes Non Technical Super Technical

40. Start the design process from the extremes Non Technical Super Technical

41. Start the design process from the extremes Non Technical Super Technical

42. Non Technical Super Technical

43. Non Technical Super Technical

44. Non Technical Super Technical >

45. Non Technical Super Technical >

46. Non Technical Super Technical > I have to learn how to script? The technical guys do the scripting “ ”

47. Non Technical Super Technical > I have to learn how to script? The technical guys do the scripting “ ” Why would I learn a new product? I could code this in two minutes “ ”

48. Non Technical Super Technical transform data

49. Non Technical Super Technical How might we empower users to in a way that matches their mental model? transform data

50. Non Technical Super Technical Visual Transform Cards Script IDE Mode

51. Visual Transform Cards

52. Visual Transform Cards

53. Visual Transform Cards

54. Non Technical Super Technical Visual Transform Cards Script IDE Mode

55. Script IDE Mode

56. Script IDE Mode

57. Script IDE Mode

58. Script IDE Mode

59. Script IDE Mode

60. Create consistent and seamless experience Non Technical Super Technical

61. Create consistent and seamless experience Non Technical Super Technical Visual Transform Cards Script IDE Mode

62. Create consistent and seamless experience Non Technical Super Technical Visual Transform Cards Script IDE Mode

63. Create consistent and seamless experience Non Technical Super Technical Visual Transform Cards Script IDE Mode

64. Create consistent and seamless experience Transform Builder Progressive Script Disclosure … Predictive TransformationNon Technical Super Technical Visual Transform Cards Script IDE Mode

65. Transform Builder

66. Transform Builder

67. Transform Builder

68. Progressive Script Disclosure

69. Progressive Script Disclosure Mini

70. Progressive Script Disclosure Mini Full

71. Progressive Script Disclosure Mini Full IDE

72. Progressive Script Disclosure Mini Full IDE

73. LevelofAbstraction Technical Proﬁciency

74. LevelofAbstraction Technical Proﬁciency MapReduce Pig, Hive /(2013-12-[0-9]+)/

75. LevelofAbstraction Technical Proﬁciency MapReduce Pig, Hive /(2013-12-[0-9]+)/

76. LevelofAbstraction Technical Proﬁciency MapReduce Pig, Hive /(2013-12-[0-9]+)/