How fighting, flaunting, and improvising led me to graph databases
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In this presentation for high school girls at the Bluemix code-a-thon, I shared stories of how I became a software engineer along with tips and tricks. I also share how I ended up working on IBM Graph and the basics of graph databases.
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How fighting, flaunting, and improvising led me to graph databases
- 1. How fighting, flaunting, and improvising led me to graph databases @Lauren_Schaefer
- 2. Who am I now? New mom Part-time software engineer Inventor Work from home in DE
- 3. The internet is awesome! <html> <body> Hello world! </body> </html>
- 4. Go Pack!
- 5. “Impostor syndrome (also known as impostor phenomenon or fraud syndrome) is a term coined in 1978 by clinical psychologists Dr. Pauline R. Clance and Suzanne A. Imes referring to high-achieving individuals marked by an inability to internalize their accomplishments and a persistent fear of being exposed as a "fraud".[1] Despite external evidence of their competence, those exhibiting the syndrome remain convinced that they are frauds and do not deserve the success they have achieved.” The all-knowing Wikipedia https://en.wikipedia.org/wiki/Impostor_syndrome Fight imposter syndrome!
- 6. Who gets the imposter syndrome? High achievers Women African Americans
- 7. Know that the confidence gap is real Men overestimate their abilities while women underestimate http://www.theatlantic.com/magazine/archive/2014/05/the-confidence-gap/359815/
- 8. If you’ve got it, flaunt it Embrace what makes you unique and use it to your advantage Be memorable!
- 9. <b>be</b> http://freestock.ca/flags_maps_g80-world_map__abstract_acrylic_p2970.html, Creative Commons Attribution 3.0 Unported License.
- 10. Improv your career. Say “yes and…”
- 11. All of that improvising led me to IBM Graph Let’s talk about #GraphDatabases
- 14. Node (Noun) Node (Noun) Node (Noun) Node (Noun) Edge (Verb) Edge (Verb) Edge (Verb) Edge (Verb)
- 15. Node (Noun) Node (Noun) Node (Noun) Node (Noun) Edge (Verb) Edge (Verb) Edge (Verb) Edge (Verb)
- 17. People People who viewed this item ultimately bought… Blue pool float Red wagon
- 18. Places
- 19. Things
- 20. Fraud $$$
- 21. Let’s try a graph database
- 22. IBM Graph to the rescue! Highly available Scales seamlessly Managed 24/7
- 23. Live demo <b>because I’m feeling<b>
- 24. Let’s recap Fight imposter syndrome If you’ve got it, flaunt it Improv your career – say, “yes and…” Use a graph database when relationships matter
Editor's Notes
- Picked computer science Java 1 and Java 2 Asked a lot of questions Felt very behind
- Throughout undergrad, I felt like I didn’t belong. I felt like the guys around me knew so much more than I did. They seemed so confident. Keep this in mind for yourself Keep this in mind when picking partners or interviewing someone for a job - Graduated with a Bachelor and Master degrees in Computer Science
- Look like everybody else, I don’t have the same hobbies as everybody else, I don’t have the same natural skillset as everybody else Embrace it because it makes me memorable So much of career advancement is making sure other people know what you’ve accomplished and making a name for yourself Social media I’ve made a name for myself by writing blogs and creating videos
- Teammates based in US, Canada, Japan, Switzerland, and Sweden Java developer, test automation specialist, web developer, growth hacking engineer, and developer advocate Built a wide variety of skills, I don’t ever get bored When you have the opportunity to try something new, go for it even if you don’t have all of the required skills. Automation specialist Social media (twitter)
- Graph databases are a type of nosql database that focus not only on the data being stored but also on the relationships between the data. In relational databases, you have tables.
- In graph databases, you have nodes and edges.
- Think of the nodes as your nouns. It’s where you store information about your people, places, and things.
- Think of the edges as your verbs. It’s where you store information about the actions that connect your nodes. Property graphs allow you to store properties (or information about your data) in your nodes and edges. There are two things I really like about graph databases: You can model the data and the relationships just as they exist in real life without having to map or abstract them to something else.
- You can easily traverse your graph, which basically means to follow the connections between your nodes. You don’t have to do expensive joins to bring your data together to find the connections—the connections are already defined so you can easily look for patterns.
- Let’s start with people. When people shop online, it can be incredibly helpful to see what other people like them purchased. Recommendations could be based on demographics, past purchase history, or social networks. Recommendation engines can be efficiently run on graph databases.
- Graph databases can be used to determine the shortest or fastest path.
- Let’s talk about the internet of things. We live in a smart, connected world. Storing data about devices and all of their connections can be done quite easily in a graph database. Snapchat glasses
- One other common use case when you start combining people, places, and things is detecting fraud. When you need to search for patterns of fraudulent behavior, graph databases are a fantastic option. In this connected world where we need to do more with our data than just access it, graph databases help us efficiently store and gain insight into the connections and patterns of our data.
- It turns out that setting up a graph database can be really complicated. Apache TinkerPop is an open source graph computing framework that’s very popular. If you want to use TinkerPop, you’ll need to setup a graph database such as Titan, which requires you to also setup and configure Cassandra or HBase and Elastic Search. Assuming you like the system you set up, you’ll have to keep all of the systems up to date and ensure they’re always running. I don’t know about you, but that sounds a bit overwhelming to me. I’d rather just focus on my app and my data.
- IBM Graph is a fully-managed graph database service on Bluemix. You may have heard of Software as a Service. You can think of IBM Graph as TinkerPop as a Service. There are 3 big perks to IBM Graph It’s highly available, so your data is always accessible. It scales seamlessly, so you don’t have to worry about updating your graph database when your app becomes the next big thing. It’s managed 24/7 by IBM experts, so you don’t have to worry about upgrading or migrating your data as the technologies change.
- I’m in the Data & Analytics section of the IBM Bluemix catalog, which you can access at bluemix.net. I’m going to click on IBM Graph. On the Service Creation page, I’ll click Create. My graph database has been created, and I didn’t have to download anything or configure any servers. Nice, right? To get started, I’ll click Open. Here I can read about how to get started and check out the documentation. I’m going to jump right in and load the sample dataset. This dataset includes information about attendees, bands, and venues for an imaginary music festival. I’ll open the Example Queries. Here I can see the dataset’s model. I can see I have nodes for attendees, bands, and venues. I also have edges that connect my nodes through actions like bought ticket and advertised to. I can query the database using the Gremlin graph traversal language. Let’s run a query that visualizes all venues that are hosting folk performances. At the top, I can see a Gremlin query has been loaded for me. I’ll run it. We can see the JSON response on the left and a visual representation of the response on the right. I can click any of the nodes on the right to see the associated JSON response on the left. The nice thing about this query editor is that I can easily copy my previous query, update it, and run it again. If you’re new to graph databases or Gremlin queries, this is a great place to get your feet wet.