If you missed the deadline for submitting your talk for The Fifth Elephant 2019 – to be held in Bangalore on 25 and 26 July – you can propose a talk here.
We are accepting talks on:
- Data engineering – engineering and architecture approaches; problems that teams were attempting to solve (and therefore the solutions that they built).
- ML engineering – engineering and architecture approaches; problems that teams were attempting to solve (and therefore the solutions that they built).
- Data science – and its applications in diverse domains.
- Open source algorithms
- Data privacy and its solutions in technology; engineering implementations of HIPPA compliance, GDPR and other data protection frameworks.
- Data security – standards, approaches to solving data security, challenges and problems to solve for data security at scale.
- Business intelligence – how non-technical teams are accessing data in companies to mine intelligence; approaches to BI; real-life case studies and applications of BI; what counts as business intelligence for businesses.
- Decision science.
Perks for submitting proposals:
Submitting a proposal, especially with our process, is hard work. We appreciate your effort.
We offer one conference ticket at discounted price to each proposer.
We only accept one speaker per talk. This is non-negotiable. Workshops may have more than one instructor.
In case of proposals where more than one person has been mentioned as collaborator, we offer the discounted ticket and t-shirt only to the person with who the editorial team corresponded directly during the evaluation process.
The first filter for a proposal is whether the technology or solution you are referring to is open source or not. The following criteria apply for closed source talks:
- If the technology or solution is proprietary, and you want to speak about your proprietary solution to make a pitch to the audience, you should pick up a sponsored session. This involves paying for the speaking slot. Write to firstname.lastname@example.org
- If the technology or solution is in the process of being open sourced, we will consider the talk only if the solution is open sourced at least three months before the conference.
- If your solution is closed source, you should consider proposing a talk explaining why you built it in the first place; what options did you consider (business-wise and technology-wise) before making the decision to develop the solution; or, what is your specific use case that left you without existing options and necessitated creating the in-house solution.
The criteria for selecting proposals, in the order of importance, are:
- Key insight or takeaway: what can you share with participants that will help them in their work and in thinking about the ML, big data and data science problem space?
- Structure of the talk and flow of content: a detailed outline – either as mindmap or draft slides or textual description – will help us understand the focus of the talk, and the clarity of your thought process.
- Ability to communicate succinctly, and how you engage with the audience. You must submit link to a two-minute preview video explaining what your talk is about, and what is the key takeaway for the audience.
No one submits the perfect proposal in the first instance. We therefore encourage you to:
- Submit your proposal early so that we have more time to iterate if the proposal has potential.
- Talk to us on our community Slack channel: https://friends.hasgeek.com if you want to discuss an idea for your proposal, and need help / advice on how to structure it. Head over to the link to request an invite and join #fifthel.
Our editorial team helps potential speakers in honing their speaking skills, fine tuning and rehearsing content at least twice - before the main conference - and sharpening the focus of talks.
How to submit a proposal (and increase your chances of getting selected):
The following guidelines will help you in submitting a proposal:
- Focus on why, not how. Explain to participants why you made a business or engineering decision, or why you chose a particular approach to solving your problem.
- The journey is more important than the solution you may want to explain. We are interested in the journey, not the outcome alone. Share as much detail as possible about how you solved the problem. Glossing over details does not help participants grasp real insights.
- Focus on what participants from other domains can learn/abstract from your journey / solution. Refer to these talks from The Fifth Elephant 2017, which participants liked most: http://hsgk.in/2uvYKI9 and http://hsgk.in/2ufhbWb
- We do not accept how-to talks unless they demonstrate latest technology. If you are demonstrating new tech, show enough to motivate participants to explore the technology later. Refer to talks such as this: http://hsgk.in/2vDpag4 and http://hsgk.in/2varOqt to structure your proposal.
- Similarly, we don’t accept talks on topics that have already been covered in the previous editions. If you are unsure about whether your proposal falls in this category, drop an email to: email@example.com
- Content that can be read off the internet does not interest us. Our participants are keen to listen to use cases and experience stories that will help them in their practice.
To summarize, we do not accept talks that gloss over details or try to deliver high-level knowledge without covering depth. Talks have to be backed with real insights and experiences for the content to be useful to participants.
Passes and honorarium for speakers:
We pay an honorarium of Rs. 3,000 to each speaker and workshop instructor at the end of their talk/workshop. Confirmed speakers and instructors also get a pass to the conference and networking dinner. We do not provide free passes for speakers’ colleagues and spouses.
Travel grants for outstation speakers:
Travel grants are available for international and domestic speakers. We evaluate each case on its merits, giving preference to women, people of non-binary gender, and Africans. If you require a grant, request it when you submit your proposal in the field where you add your location. The Fifth Elephant is funded through ticket purchases and sponsorships; travel grant budgets vary.
You must submit the following details along with your proposal, or within 10 days of submission:
- Draft slides, mind map or a textual description detailing the structure and content of your talk.
- Link to a self-recorded, two-minute preview video, where you explain what your talk is about, and the key takeaways for participants. This preview video helps conference editors understand the lucidity of your thoughts and how invested you are in presenting insights beyond the solution you have built, or your use case. Please note that the preview video should be submitted irrespective of whether you have spoken at past editions of The Fifth Elephant.
- If you submit a workshop proposal, you must specify the target audience for your workshop; duration; number of participants you can accommodate; pre-requisites for the workshop; link to GitHub repositories and a document showing the full workshop plan.
Anomaly Detection at Scale: Architectural Choices for Data Pipelines for 7B events per day
Cloud-native applications. Multiple Cloud providers. Hybrid Cloud. 1000s of VMs and containers. Complex network policies. Millions of connections and requests in any given time window. This is the typical situation faced by a Security Operations Control (SOC) Analyst every single day. In this talk, the speaker talks about the high-availability and highly scalable data pipelines that he built for the following use cases :
- Denial of Service: A device in the network stops working.
- Data Loss : An example is a rogue agent in the network transmitting IP data outside the network
- Data Corruption : A device starts sending erroneous data.
The above can be modeled through anomaly detection models. The main challenge here is the data engineering pipeline. With almost 7 Billion events occurring every day, processing and storing that for further analysis is a significant challenge. The machine learning models (for anomaly detection) has to be updated every few hours and requires the pipeline to create the feature store in a significantly small time window.
The core components of the data engineering pipeline are:
- apache pulsar
- apache flink
- apache kafka
- apache druid
- apche spark
- apache cassandra
Apache Pulsar is the pub-sub messaging system. It provides unified queuing and streaming. Think of it as a combination of Kafka and RabbitMQ. The event logs are stored in Druid through kafka topic. Druid supports apache kafka based indexing service for realtime data ingestion. Druid has primitive capabilities to create sliding time window statistics. More complex real-time statistics are computed using Flink. Apache Flink is a stream-processing engine and provides high throughput and low latency. Spark jobs are used for batch processing. Cassandra serves as the data warehouse as well as the final database.
The speaker talks through the architectural decisions and shows how to build a modern real-time stream processing data engineering pipeline using the above tools.
- The problem: overview
- Different Architecture Choices
- The final architecture - a brief explanation
- Real-Time Processing
- Apache Pulsar
- Queueing and Streaming
- Pulsar vs Kafka vs RabitMQ
- Why Pulsar for this application?
- Apache Flink
- Micro-batching vs Streaming?
- Basic Spark Streaming Micro Batching With State
- Flink ADS - Asynchronous Distributed Snapshot
- Why Flink for this application?
- Apache Druid
- What is OLAP?
- ClickHouse vs Druid
- Why Druid for this application?
- Apache Pulsar
- Batch Processing
- Apache Spark
- Data Engineering + Machine Learning
- ML and MLLIB
- Apache Cassandra
- What is OLTP?
- Cassandra vs Hbase vs Couchbase vs Mongo
- Why Cassandra for this application?
- Apache Spark
- A short demo
Tuhin Sharma is co-founder of Binaize Labs, an AI based Cyber Security Start-up. He worked in IBM Watson and RedHat as Data Scientist where he mainly worked on Social Media Analytics, Demand Forecasting, Retail Analytics and Customer Analytics. He also worked at multiple start ups where he built personalized recommendation systems to maximize customer engagement with the help of ML and DL techniques across multiple domains like FinTech, EdTech, Media, E-comm etc. He has completed his post graduation from Indian Institute of Technology Roorkee in Computer Science and Engineering specializing in Data Mining. He has filed 5 patents and published 4 research papers in the field of natural language processing and machine learning. Apart from this, He loves to play table tennis and guitar in his leisure time. His favourite quote is “Life is Beautiful.” You can tweet him at @tuhinsharma121.