Special Edition, Data Engineering on AWS

This is a past event

200 people went

Amazon Web Services

2 Park St · Sydney

How to find us

You can find us up on level 37 - please bring along a valid photo id.

Location image of event venue

Details

AWS have kindly invited us to their Park St offices for a special edition of the Sydney Data Engineering meetup.

🏠Venue Host: AWS
🍕Food and Beverage sponsor: AWS

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Speakers:
🎤Melody Yang - Big Data & Analytics Solution Architect, AWS
Codeless ETL - ARC Framework
ARC is a Spark-based data ingestion and process framework, which simplifies and accelerates ETL work load at scale. In this session, we will focus on a few data engineering problems that ARC has solved. From a user perspective, I will also touch on the business value and outcomes by leveraging the codeless nature of the framework.
Melody is a data enthusiast - passionate about Data Engineering and Data DevOps. A pioneer of cloud-native Data Lake. Love developing and implementing architectural concepts, practical data patterns and frameworks.

🎤Najah Naaji - Solutions Architect, AWS
Open Distro for Elasticsearch
Elasticsearch has become an essential technology for log analytics and search, fuelled by the freedom of open source. In this talk, we will discuss what Open Distro for Elasticsearch offers and explore functionalities like SQL, Security, Event Monitoring & Alerting, Deep Performance Analysis with Open Distro for Elasticsearch and Kibana.
Najah is a member of AWS Data and Analytics Technical Field Community and has been working with Data and Analytics for over 14 years. He has been with AWS for over 5 years and before joining AWS he was working in the Telecommunications industry. He is an AGSM alumni and holds a Bachelors and Masters in Engineering. He considers himself as a Big&Fast data enthusiast, loves to travel and read books in his spare time.

🎤Chris Horder - Solutions Architect, AWS
Stream processing in 2019
Explore the various options for streaming data on AWS, such as Amazon Kinesis and Amazon Managed Streaming for Kafka, and the various options for processing streams of data such as Apache Spark, Apache Flink, AWS Lambda, and Amazon Kinesis Analytics for Java. Let's explore what an architecture for processing Australia's new Open Banking data format at 60,000 transactions per second could look like.

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Bring along some great questions for our speakers!