Do you want to dig deeper into AWS cutting-edge technologies? Then don’t miss Ciklum AWS Speakers’ Corner on June 16 to discover emerging practices, trends & features.
Topic: Streaming DynamoDB
Speaker: Ryan Cormack, Tech Lead at Just Eat Takeaway
DynamoDB is a serverless, autoscaling, NoSQL database from AWS. Its ability to scale on demand makes it a great use case for any workload that may have unpredictable bursts and fits in well with other Serverless architecture patterns. DynamoDB streams allow us to further decouple data writers and our event systems and thus help us scale our microservices.
During the event, we’ll discuss how we can use DynamoDB Streams and Lambda together to build a serverless, high-throughput decoupled system at Just Eat Takeaway.
- An introduction to DynamoDB Streams
- How to use DynamoDB Streams with Lambda
- DynamoDB Streams usage for Event Carried State Transfer, Producer-Consumer Correctness, and scaling a decoupled Serverless system.
About the speaker:
Ryan started his career in digital marketing before moving to software engineering. He's worked with AWS technologies for ten years and has recently started working with teams building full Serverless microservices and "breaking apart the monolith.” Ryan has worked in various companies, from founding his startup to global companies like Just Eat. At Just Eat, he currently chairs the internal Lambda Guild. He enjoys working with .NET and Typescript and constantly learns more about AWS Serverless offerings.
Topic: Cases of building modern Data Services in AWS Cloud, using Aurora PostgreSQL, Apache Spark, Snowflake, and AWS Glue
Speaker: Vitalii Bondarenko, Head of Data & Analytics Centre of Excellence at Ciklum
- AWS Data Platform overview: cases of building different types of data services and examples from real projects in AWS cloud
- Data Architecture design and implementation with Aurora PostgreSQL, Apache Spark, Snowflake, and AWS Glue technologies
- Case 1: Low latency data access
- Case 2: Cloud Data Analytics with DWH and Data Lake
- Case 3: ML in AWS Cloud
- Recap: Cloud Data Engineer: skillset and education path