Platfora Girl Geek Dinner

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Platfora sponsored Bay Area Girl Geek Dinner. Talks on big data, analytics, and career advice.

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  • Volume = Size of the Data The size of the data is relative to the organization as to what “big” means. And the target changes as advances in hardware get better. But if a dataset is growing exponentially (think tweets, sensor data, application logs) and it is too big to fit in memory on a single machine, then it is big data. Usually we’re talking terabytes or petabytes.Velocity = The time to process or query the dataFrom batch processing to real-time to streaming data. For some applications, such as fraud detection, 10 minutes is too late. Data must be used as it streams into your organization. Example: Process 5 million stock trades a day to detect potential fraud. Variety = Big data can be in any format – structured, unstructured – web logs, log files, sensor data, call records. New insights are found when analyzing all of this data together.Value = The unspoken V is value. Is the data useful and accurate and meaningful? Does it provide valid insights? This is something that requires human input – people need access to big data to determine its value.
  • 1997 – The term “big data” is first used in a paper published by Michael Cox and David Ellsworth called “Application-controlled demand paging for out-of-core visualization”.2004 - Google engineers Jeff Dean and Sanjay Ghemawat publish a paper describing Google File System and Google MapReduce, the proprietary technology that allowed Google to do exabyte-scale data management and parallel processing using commodity hardware.2006 – Engineers Doug Cutting and Mike Cafarella develop HDFS and MapReduce to support Yahoo search based on Google’s design and the Hadoop open source project is born. 2008 – Hadoop becomes a top-level Apache project and Facebook releases Hive, a SQL query interface for Hadoop. 2009 – The Hadoop Summit in its third year has 750 attendees. 2011 – Platfora is founded and begins development on a solution to simplify access to Hadoop data for the business users. Attendance at the Hadoop Summit triples in 2 years.2013 – Platfora goes GA and a new era of Big Data is born!
  • To understand the Platfora, you first need to understand Hadoop. We will briefly cover what Hadoop is, and how it works at a high level. So what is Hadoop?It is a software application framework originally designed by Google in their earlier days so they could usefully index all of the internet data they were collecting, and then present meaningful results to their users. At the time, there was nothing on the market that could do that, so Google built their own platform. Hadoop is an open source project that was founded based on a paper released by Google describing their innovations. Yahoo was a major contributor to Hadoop and played a key role in developing Hadoop for enterprise applications.Hadoop was purpose built for processing large datasets containing complex data that could not easily be described in table format. It was designed to use the disk, memory, and compute resources of multiple commodity servers that are networked together. Because failures are common and expected in distributed computing, the Hadoop framework handles replicating the data across multiple machines so it can continue data processing uninterrupted in the event of a hardware failure. The Hadoop framework consists of 2 key services. The Hadoop Distributed File System (HDFS) handles the storage and replication of the data files, and MapReduce does the data processing on the data where it resides.
  • These two core services – HDFS and MapReduce – are the foundation of Hadoop. These services work together to provide a storage and processing platform for big data. There are other components in the Hadoop eco-system as well, such as Hive (a SQL-like interface for defining MapReduce jobs) or Pig (a data flow language for defining MapReduce jobs) – but HDFS and MapReduce are the major components of Hadoop.
  • Here is a simple diagram of how a MapReduce job works using the the famous Hadoop Word Count example (count the occurrences of a word in the input data files). A MapReduce job is divided into two main phases – MAP and REDUCEThe MAP phase splits the files into records that can be worked on in parallel, and passes them to individual mapper tasksThe map tasks process the records into key, value pairs. The maps are then sorted and shuffled to consolidate the matching keysThe REDUCE phase then aggregates the individual map results into a final result.
  • There is no doubting that Hadoop is very good at what it does – storing and processing large amounts of data of disparate schemas and formats. There is a reason why Hadoop is considered THE platform for Big Data.Inexpensive – Hadoop clusters often cost 50 to 100 times less per-terabyte of storage than a traditional data warehouse. It is open-source and runs on commodity hardware, so many enterprises are drawn to the price/performance ratio without vendor lock-in. Scalable – Hadoop was designed to store and process large amounts of data at scale. Hadoop scales linearly and can grow organically as your data grows. It scales in both storage capacity, and in compute capacity. Along with scaling, it is fault-tolerant in the face of hardware failures. Powerful – Hadoop can do complex data processing tasks on volumes of data that traditional relational database systems can’t handle. It can work on all types of data, structured and unstructured.Consolidation – It is relatively easy to get data into Hadoop. Since Hadoop is a filesystem and not a database, you can store data from multiple sources - structured or unstructured – and combine all different types of data in one platform. This ‘data reservoir’ approach is attractive to many companies looking to eliminate data silos and have one data repository for all the company’s BI and analytics needs.
  • On the other hand, Hadoop is not good at everything that the enterprise needs to do with their data. Namely, it is not readily accessible to business users.Hard to Query – To query data in Hadoop, you have to know how to write MapReduce programs (i.e have Java programming skills) or use a specialized query language like Hive or Pig. Since the average business user does not have these skills, there is a burden on technical Hadoop experts to write the queries to get at the data and make sense of it. Hadoop requires a technical middle-man between the data and the consumers of the data. Data Access – To store data in Hadoop, you do not need to define the schema or specify any metadata about the data itself. This makes it really easy to get data into Hadoop. However, it makes it hard to know what data you have available to you and how to make use of it. Again, business users have to rely on technical experts to know what data is there and what questions are possible. Slow Batch Processing – Hadoop is powerful but it is limited to batch-oriented processing of data. You cannot query Hadoop in real-time. Many companies use the powerful batch processing capabilities of Hadoop, but then move the data to a different system that can be queried in real-time by their data anlaysts.
  • To make Hadoop data available to the business, the typical solution today is to Pre-process the data first in Hadoop using MapReduce to organize it into a consistent structureThen move a pre-defined subset of data out of Hadoop into a relational data warehouse (Vertica, Oracle, Greenplum, TeraData, etc) using ETL tools (Informatica, etc.) and scriptsThen connect a BI tool (MicroStrategy, Tableau, etc) to certain tables in the database – often pre-defined aggregate tables purpose built to optimize the BI queries.If there is a change in the source data or a need for more or additional data, this entire pipeline has to be re-worked, which can take months – and again puts the burden on IT
  • Voice:So, to review: (review the four requirements)Handle big dataAllow iterative, in-memory exploration through big dataCollect everything – we can’t anticipate the questionsRemove the friction and complexity introduced with data warehouses and allow business users to self-service through the dataThe implication. If you are left using a data warehouse or a connector lifeline to legacy technology, you will be left behind
  • (this is an animated slide – click to advance animation before each talking point)Platfora is an application that optimizes an organizations existing Hadoop cluster(click)Platfora is installed in the same network as an existing Hadoop cluster but on its own dedicated hardware. Platfora connects to Hadoop and utilizes its HDFS and MapReduce services. (click)To make Hadoop data visible and discoverable to business users, Platfora administrators create a data catalog of the datasets residing in Hadoop. The data catalog is just a metadata description of the source data. No data is moved until a user requests it. (click)When a business user finds data they want to explore, they can request it by defining a lens. A lens is a selection of fields chosen from one or more related datasets. (click)To load the requested data into Platfora, users “build” a lens. A lens build initiates a series of MapReduce jobs in Hadoop to pull and process the requested data. The output of the MapReduce job is the lens, which is stored in HDFS and also loaded onto disk in Platfora. (click) Once a lens is built, it can be queried by Platfora’s built-in BI interface – the Vizboard. A lens is a columnar data structure that is loaded into memory to enable real-time interactive queries.This workflow is flexible and iterative – if a business user needs additional data, they can update and refresh their lens on their own.
  • The workflow to go from raw data in Hadoop to interactive in-memory BI is achieved in one end-to-end platform.(click)Connect Platfora to your Hadoop cluster and point to the source data you want to make available.(click)Describe how the data is structured and related by defining and modeling datasets.(click)Once the catalog is defined, users can request data from Hadoop by defining and building a lens. A lens can be thought of as an on-demand data mart that can be updated or deleted as needed. It is a selection and summarization of the source data, but the source data itself in Hadoop remains intact.(click)Once the data is in lens format, it can be queried by Platfora’s BI interface, the vizboard. Lens data is loaded into memory to enable real-time, interactive data exploration.
  • Methodologies – Agile, Waterfall, etc.?What does an Eng org look like without a PGM?
  • Methodologies – Agile, Waterfall, etc.?What does an Eng org look like without a PGM?
  • Recognize when to ‘steer’ v. ‘mandate’Judgement, anticipating problems and making trade-offs
  • Just like technology itself – my career has taken twists and turns – ups and downsIn every career, I think there are things that you didn’t expect, things that maybe were a little uncomfortable at the time, but on the flipside you look back and say “that was awesome!” I call these “happy accidents”My first happy accident was that internship that I landed at the web development consulting firm, Lante, which turned into my first job. They never had a technical writer, I had never been a technical writer, but I had to figure out something to do for them! I started by sitting in on customer meetings and listening to what the customer wanted – I wrote requirements docs and use cases – and final documentation at the end of the project. It turned out that customer satisfaction was higher on projects that had documentation, and it led to more repeat engagements. I was hired to lead the business analysis and documentation portion of the practice, and built a team of writers.My second happy accident was Securant. I was hired by a tech pubs manager who quit on my 4th day. He just stopped coming to work. I don’t blame him. It was 4 weeks out from the first release. It was a start-up in complete chaos. There was no documentation at all. I new nothing about web security. I was way over my head! It was horrible! I felt stupid! I had never worked harder in my life! But that experience accelerated my career growth 10 times over. At the time it sucked – looking back – it was awesome! Greenplum was my third happy accident. I was reluctant to go because I did not know anything about big data or databases. I hadn’t been at Oracle very long, and was worried about job hopping so soon. But the VP of Engineering at the time was a woman who had been a mentor to me at 2 prior companies. I decided to go to Greenplum because of her. Then she quit on my 4th day! The initial beta product didn’t work and had to be re-architected. They had to lay off half the company while they regrouped. It was horrible! But in hindsight, it was awesome. I fell in love with big data. I got to be involved at an early stage with a company that went on to be really successful. I got to build a tech pubs and training organization from the ground up. And it was where I first got to work with Ben Werther, the founder of Platfora!
  • My parents wish I would’ve gone to law school – it would be so much easier to tell their friends that I’m a lawyerI had nothing to do with this!Why yes, I know Word – what is the company again?I write useless instructions that nobody reads, or sometimes I take emails written by Engineers and paste them into Word or FrameMaker or whatever the authoring tool du jour may be. I am an information ninja, managing a complex web of informationActually for about every hour I spend writing, I spend another 4 hours doing other things - like researching competitor products, industry terminology and use cases, using the product, attending meetings, filing bugs, curating sample data, developing demos, everything else…
  • My mission is simple – people use software to make their job easier. I write information to help people use the software to do their job. Part of that is understanding your audience and writing to their level of experience.
  • Authoring toolsGraphic development toolsCollaboration toolsCloud and infrastructure tools to install Platfora and Hadoop so we can test and document different use cases.
  • The word “technical” comes first in technical writer
  • Personal goals / Life changes – getting married, moving home, having a kid, kid transitions (schools)
  • Personal goals / Life change examples: marriage, buying a house, moving home, having a kid, kid transitions (schools)
  • So I am going to talk about how I switched careers and got started in technology
  • Unlike young people today, I did not grow up with technology – there was no email, no laptops, no cell phones, no facebookI’m a little embarrassed to say that I did not use a computer for the first time until I was in college
  • In college I was an English major – I was great at organizing my thoughts and could write a killer essayBut like many English majors, I had no idea how to use my skills to make a living
  • So I got a job at Nordstrom. That job turned into a career, and I worked my way up the chain to an assistant buyer position.I actually decided to change careers when I was offered a promotion – the job I had been coveting for 8 years – BUYER yay! (in Arizona) oh!I had cold feet. If I took that job, I realized that I was giving up what I really wanted to do. I wanted to write, I wanted to learn, and I wanted to make a difference.I wanted to be a geek!
  • At the time of my transition, it was the dot com boom. I knew I wanted to be a part of that! San Francisco State had opened a new program in technical writing and it was love at first task analysis outline! I also enrolled in their web development program.I read more “Dummies” books than I care to admit. I tried every technology and software I could get my hands on. One problem – you can’t get a job without experience, and you can’t get experience without a job.So I found open source projects that did not have much documentation, and offered to contribute. I did free web sites for small local businesses. I had a friend introduce me to someone she knew who worked for a web development start-up, and offered to intern for free.I joined the Society of Technical Communication (STC) and volunteered at their conferences and judging competitions – the competitions let me get my hands on examples of real-world technical documentation, and meet my more-established peers. At the time there was no social media like we know it today – no LinkedIn or Twitter - but I put up a personal web site with articles and whitepapers about the industry – along with my portfolio pieces and a resume of the projects I had done. The purpose is the same – promote yourself and demonstrate what you know (or what you want people to think you know!).
  • Just like technology itself – my career has taken twists and turns – ups and downsIn every career, I think there are things that you didn’t expect, things that maybe were a little uncomfortable at the time, but on the flipside you look back and say “that was awesome!” I call these “happy accidents”My first happy accident was that internship that I landed at the web development consulting firm, Lante, which turned into my first job. They never had a technical writer, I had never been a technical writer, but I had to figure out something to do for them! I started by sitting in on customer meetings and listening to what the customer wanted – I wrote requirements docs and use cases – and final documentation at the end of the project. It turned out that customer satisfaction was higher on projects that had documentation, and it led to more repeat engagements. I was hired to lead the business analysis and documentation portion of the practice, and built a team of writers.My second happy accident was Securant. I was hired by a tech pubs manager who quit on my 4th day. He just stopped coming to work. I don’t blame him. It was 4 weeks out from the first release. It was a start-up in complete chaos. There was no documentation at all. I new nothing about web security. I was way over my head! It was horrible! I felt stupid! I had never worked harder in my life! But that experience accelerated my career growth 10 times over. At the time it sucked – looking back – it was awesome! Greenplum was my third happy accident. I was reluctant to go because I did not know anything about big data or databases. I hadn’t been at Oracle very long, and was worried about job hopping so soon. But the VP of Engineering at the time was a woman who had been a mentor to me at 2 prior companies. I decided to go to Greenplum because of her. Then she quit on my 4th day! The initial beta product didn’t work and had to be re-architected. They had to lay off half the company while they regrouped. It was horrible! But in hindsight, it was awesome. I fell in love with big data. I got to be involved at an early stage with a company that went on to be really successful. I got to build a tech pubs and training organization from the ground up. And it was where I first got to work with Ben Werther, the founder of Platfora!
  • By value I mean being able to articulate the value you bring to a company and knowing what your job and skill-level is worth in the market. Realize that you bring skills and experience to the table when changing careers or when changing roles within the same company. What makes you excited to come to work every day? For me, I have to truly believe in the product or technology I am working on. I need a place where I am learning, and where I feel like my role matters in the success of the company. When you can identify what you need to be happy, you are less likely to to wind up in a job that is a bad fit. The majority of my career growth has come from doing something that has scared me or has made me feel uncomfortable. I remind myself of that when something is hard or I feel like I am not up to a challenge.
  • Even asking “do you have feedback for me”. I used to write this in my notebook to remember to ask this at 1:1’s.Not just managing the message about what your team is doing or doing well. This is asking for what support looks like or changing the the conversation when you get promoted. Easy to get
  • The U.S. Department of Labor projects that by 2020, there will be 1.4 million computer specialist job openings. Yet U.S. universities are expected produce only enough qualified graduates to fill 29% of these jobs.
  • For hiring managers or people that influence hiring or participate in interviews, there are times that you meet someone and say—”wow, there are great but they aren’t a fit for my team or my company!”. [Give personal story about woman at SFDC] Be sure to pass them along to another hiring manager directly or a friend, as many of you know that it is easy to get lost in the hiring machine especially at a big company.
  • Platfora Girl Geek Dinner

    1. 1. @platfora
    2. 2. 2 Today‘s Presenters Denise Daria Sive @platfora
    3. 3. 3 Topics • What‘s big data? • What is my role really about? • How did I… ? • If I had a time machine… • A little more data… • Where are my girls at? @platfora
    4. 4. @platfora
    5. 5. 5 Big data is the term for a collection of data sets so large and complex that it becomes difficult to process them using traditional storage and processing applications. @platfora
    6. 6. 6 The 3Vs of Big Data Volume Velocity Variety Big Data Value @platfora
    7. 7. 7 A Brief History of Big Data and Hadoop The term ‗big data‘ is coined Hadoop open source project founded Google publishes GFS/MapReduce papers Hadoop becomes top- level Apache project Facebook releases Hive (SQL on Hadoop) Google founded 750 attend 3rd Hadoop Summit Platfora GA Release Platfora founded 2700 attend 5th Hadoop Summit @platfora
    8. 8. What is Hadoop? 8 • Open-source software application framework • Designed for processing large datasets • Distributed over multiple commodity servers • With built-in fault detection and recovery • Consisting of two key services: • Distributed file storage (HDFS) • Distributed data processing (MapReduce) @platfora
    9. 9. Hadoop = HDFS + MapReduce [ + … ] 9 Data Files NameNode JobTracker DataNode TaskTracker DataNode TaskTracker DataNode TaskTracker Processing Jobs @platfora
    10. 10. How MapReduce Processing Works 10 yellow blue red orange green blue blue orange red green yellow red yellow blue red orange green blue orange,1 green,1 blue,1 yellow,1 blue,1 red,1 blue orange red green yellow red green,1 yellow,1 red,1 blue,1 orange,1 red,1 yellow,1 yellow,1 blue,1 blue,1 blue,1 red,1 red,1 red,1 orange,1 orange,1 green,1 green,1 yellow,2 blue,3 red,3 orange,2 green,2 yellow,2 blue,3 red,3 orange,2 green,2 Input Files Split Map Sort / Shuffle Reduce Final Output @platfora
    11. 11. So … Hadoop Is Great! • It‘s inexpensive • It‘s scalable • It‘s powerful • It makes data consolidation easy 11@platfora
    12. 12. • Queries require advanced technical skills • Business users cannot access the data directly • Batch processing is too slow for interactive analysis But … Hadoop Has Its Limitations 12@platfora
    13. 13. 13 Typical Pipeline for BI on Hadoop Today Hadoop RDBMS BI Tool ETL query @platfora
    14. 14. • Hadoop is a powerful platform for storing and processing big data • But it doesn‘t meet all of the data needs of the enterprise • Platfora leverages the power of Hadoop and builds on its strengths • Platfora makes data in Hadoop accessible to enterprise business users • Without the need for separate ETL, Data Warehouse, and BI Tools How Platfora Works with Hadoop
    15. 15. 15 How Platfora Works with Hadoop MapReduce HDFS Vizboard query @platfora
    16. 16. 16 The Platfora Pipeline for BI on Hadoop source data Hive HDFS source data 1. Connect to source data in Hadoop define datasets 2. Define and model datasets 3. Build lenses to pull data into Platfora build visualizations 4. Visually analyze the data @platfora
    17. 17. BIG DATA MADE BEAUTIFUL (SHOW ME THE DEMO!) 17
    18. 18. WHAT IS MY ROLE REALLY ABOUT? 18
    19. 19. 19 Engineering “I think you should be more explicit here in step two.” THEN A MIRACLE OCCURS @platfora
    20. 20. 20 Mission Build a usable, scalable, quality product that delivers key business insights to Platfora customers @platfora
    21. 21. 21 How we do it Engineering (and design) Engineering (and design) CONCEPTION INITIATION ANALYSIS DESIGN CONSTRUCTIO N TESTING DEPLOYMENT CONCEPTION INITIATION ANALYSIS DESIGN CONSTRUCTIO N TESTING DEPLOYMENTWaterfall Model VS Agile @platfora
    22. 22. So … what does engineering management do? 22@platfora
    23. 23. 23 What my peers think I do What society thinks I do What recruiters think I doWhat my family thinks I do What I think I do What I actually do
    24. 24. 24 Software Program Management @platfora
    25. 25. 25 Mission Lead a cross-functional team that prides itself on effectively and efficiently delivering to the needs of Platfora customers @platfora
    26. 26. 26 What Does a Software Program Manager Do? • Project Management taken ‗up a level‘ • Lead the cross-functional team and execution • Identify what needs to be done & partner with others to ensure it happens • Collaboration across functions • Roles & Responsibilities • Advocate accountability • ‗Project‘ health and status Sometimes… @platfora
    27. 27. 27 What Else Does a Software Program Manager Do? • Efficiency and effectiveness • Continually evaluate and enhance operational performance • What could we be doing better tomorrow? • How will this positively impact our customers? • Product Roadmap • Translate business objectives into execution strategy @platfora
    28. 28. 28 Keys to a Successful Program Manager • Excellent Communication skills – at all levels of the org • Trusted & Respectful Relationships across the org • Kind, but Pushy – tactfully probe, question and challenge • Sense of Urgency, Responsibility and Accountability • Judgment – lead teams to make the right decisions • Flexibility • Ability to Influence • Positive attitude • Have fun!
    29. 29. One Path to Program Management = acquisition Post Communications
    30. 30. 30 Technical Publications & Training
    31. 31. So … what does technical publications do? 31@platfora
    32. 32. 32 What my family thinks I do What society thinks I do What engineers think I do What I think I do What recruiters think I do What I actually do
    33. 33. 33 Mission Develop and deliver information that helps users be successful in using Platfora’s products to achieve their objectives @platfora
    34. 34. 34 The Tools of the Trade
    35. 35. 35 What it Takes to Work in Tech Pubs • Strong technical knowledge (or aptitude) • Excellent writing skills • Excellent listening, research, and information-organizing skills • Sympathy for the impatient or frustrated user • Humility and curiosity • Sense of responsibility • Resourcefulness and self-sufficiency @platfora
    36. 36. HOW DO I… ? 36
    37. 37. 37 • Use technology and time wisely • Perception is key • Show you are engaged and productive when it is most important • Telecommuting & Mobility tools ** • Spend time working, not commuting • WiFi is everywhere • Didn‘t get to that ‗one last thing‘ before you left the office – wrap it up during alternate hours (eg. after the kids are in bed) ** But don‘t underestimate the value of face time How Do I Balance Work with My Life? • Figure out what works for YOU and your situation Sure my project management system has a few flaws, but I am sticking with it. @platfora
    38. 38. 38 • Set expectations & Keep boundaries • Set expectations up front, avoid difficult discussions after the fact • Prioritize – it‘s OK if you can‘t do it all at once • Beware of the self-imposed guilt trip • Make time to be ‗offline‘ • Explore your options if you take a leave • You don‘t have to return to the SAME job • Ease back in (part-time  full-time) How Do I Balance Work with My Life? Saying NO feels empowering, Except for the guilt. @platfora
    39. 39. 39 How Do I Balance Work with My Life? • Plan ahead, where possible • Plan the weeks personal commitments alongside work calendar • Block time for standing personal commitments on your work calendar • Allow time to establish yourself before making a personal / life change • Too many changes all at once can be overwhelming and impact your work @platfora
    40. 40. 40 How did I Switch Careers? @platfora
    41. 41. 41 I was Not Born a Girl Geek @platfora
    42. 42. 42 I was an English major… @platfora
    43. 43. 43 So I worked at Nordstrom sales associate assistant manager manager assistant buyer regional buyer geek transition @platfora
    44. 44. 44 Geek Transition – Getting a Foot in the Door • Get educated • Take classes • Research, read, experiment • Build a portfolio of work • Internships • Open Source projects, Non-profits, Small businesses • Network • Professional organizations • Social media * @platfora
    45. 45. My (Not so Straight-forward) Career in Technology = happy accident= acquisition @platfora
    46. 46. 46 How did I (Get Started and then) Advance?
    47. 47. 47 Embrace Your Inner Geek @platfora
    48. 48. Never Stop Learning @platfora
    49. 49. IF I HAD A TIME MACHINE… 49
    50. 50. Sive‘s Words of Wisdom… • Don't be afraid to ask questions • Pay attention to your audience • Establish a network of trusted leaders/mentors @platfora
    51. 51. Daria‘s Words of Wisdom… • Know your value • Know what drives you • Know your comfort zone … then push the boundaries @platfora
    52. 52. Denise‘s Words of Wisdom • Be open—―feedback is a gift‖ • Manage up, don‘t manage the message up • Periodically assess your career and outstanding tasks as if you are preparing your legacy @platfora
    53. 53. A LITTLE MORE DATA… 53
    54. 54. 54 Leadership “a process of social influence in which one person can enlist the aid and support of others in accomplishment of a common task” [Thank you, Wikipedia!] @platfora
    55. 55. 55@platfora Percent of qualified graduates to fill the need for 1.4M computer science jobs by 2020
    56. 56. Average age that girls first use a computer vs. age 12 for boys
    57. 57. Computing & information science degrees obtained by women – down from 37% in 1985
    58. 58. Percent of women contributing to open source projects (which includes Daria)
    59. 59. Percent of female executives at Fortune 500 tech companies
    60. 60. 60 Cognitive Diversity • “Cognitive diversity goes beyond job function or titles (where even diverse multifunctional innovation teams can come together yet still fail to come up with truly innovative ideas and development) and gets to a root level differentiator of the way people look at the world and how they communicate that vision.” [Innovation Excellence]
    61. 61. Higher return on sales for companies with 3 or more female board directors
    62. 62. Higher return on investment for technology companies with more women on their management team
    63. 63. WHERE ARE MY GIRLS AT? 63
    64. 64. 64 The fine print… You must tweet a photo of yourself participating in a women and STEM program by October 31. In your photo @mention Bay Area Girl Geek Dinners (@BayAreaGGD) and Platfora (@Platfora). What do you get… You‘ll be entered into a drawing to win a free Hadoop training class* @platfora*Valued up to $2,000.
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