Speaker: Anisha Nainani

Software Engineer @Paypal

Anisha has 2 plus years of experience in building big data platforms. Anisha did her Masters in Computer Science from University of Texas at Dallas in 2014 with a focus on Data Engineering. After that she joined Paypal as a Software Engineer, where she helped build a compute framework to provide unified experience to users to run SQL queries on any compute engine such as spark, presto, hive. Anisha is also the core contributor of PayPal Gimel’s HBASE & Aerospike APIs - enabling SQL & unified API for users to access HBASE & Aerospike at scale. Currently, she is working on PayPal’s Core Data Highway, which enables streaming data from Oracle to Kafka and other offline data stores in near real time.

Find Anisha Nainani at

Proposed Tracks

  • Real-World Data Engineering

    Showcasing DataEng tech and highlighting the strengths of each in real-world applications.

  • Deep Learning Applications & Practices

    Deep learning lessons using Tensorflow, Keras, PyTorch, Caffe across machine translation, computer vision.

  • AI Meets the Physical World

    The track where AI touches the physical world, think drones, ROS, NVidea, TPU and more.

  • Data Architectures You've Always Wondered About

    How did they do that? Real-time predictive pipelines at places like Uber, Self-Driving Cars at Google, Robotic Warehouses from Ocado in the UK, are all possible examples.

  • Applied ML for Software

    Practical machine learning inside the data centers and on software engineering teams.

  • Time Series Patterns & Practices

    Stocks, ad tech/real-time bidding, and anomaly detection. Patterns and practices for more effective Time Series work.