Qubole

Content Type 2:

3. Big Data Vendor Buyer's Guide

4. A definitive guide on Big Data vendors. Which big data vendor is right for your organization?

Where

Amazon
7 West 34th St. New York, NY 10001

When

Monday, July 15, 2019
1:00 PM - 4:00 PM (EDT)


About This Workshop

Data Engineering is fast emerging as the most critical function in Analytics and Machine Learning (ML) programs. In this hands-on workshop for Data Engineers, you will learn how to acquire and transform batch (Redshift) and streaming (Twitter) data sets, build and orchestrate pipelines using Apache Spark and Airflow from your AWS S3 Data Lake to support your data science and analytics use cases.



You'll have access to an environment loaded with the appropriate tools, including Apache Spark, Airflow, Hive and Presto on Qubole, as well as other technologies such as Kafka and AWS Sagemaker, plus interactive notebooks for building an end-to-end ML application.  


In this free half-day workshop, you will learn how to:


  • Leverage streaming and batch data sets for machine learning applications

  • Transform, prepare data with Apache Spark, and manage the  pipelines Apache Airflow

  • Use Qubole Notebooks to run ML models and deploy them using AWS SageMaker.


Please bring your laptop to participate in this workshop.

Request a Seat

Agenda

1:00 PM - 1:30 PM  

Opening Remarks: Machine Learning at Enterprise Scale

1:30 PM - 2:15 PM  

Ingesting Streaming Data

2:15 PM - 3:00 PM 

Analytics on Data Lake

3:00 PM - 3:30 PM 

Building ML Model & Deployment on AWS SageMaker

3:30 PM - 4:00 PM

Operationalize & Deploy ML Model

4:00 PM ~

Networking & Happy Hour


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