Overview
This one day, advanced level boot camp helps participants master how to design, build and operate serverless data lake solutions with AWS services. The course includes topics like ingesting data from any data source at a large scale, storing the data securely and durably, enabling the capability to use the right tools to process large volumes of data. The course also helps participants understand the options available for analyzing the data in near-real time.
What You'll Learn
- Collect large amounts of data using services such as Kinesis Streams and Firehose and store the data durably and securely in Amazon Simple Storage Service
- Create a metadata index of your data lake
- Choose the best tools for ingesting, storing, processing, and analyzing your data in the lake
- Apply the knowledge to hands-on labs that provide practical experience with building an end-to-end solution
Curriculum
- Key services that help enable a serverless data lake architecture
- A data analytics solution that follows the ingest, store, process, and analyze workflow
- Repeatable template deployment for implementing a data lake solution
- Building a metadata index and enabling search capability
- Setup of a large scale data ingestion pipeline from multiple data sources
- Transformation of data with simple functions that are event-triggered
- Data processing by choosing the best tools and services for the use case
- Options available to better analyze the processed data
- Best practices for deployment and operations
Who should attend
The course is highly recommended for:
- Solution architects
- Big data developers
- Data architects
- Data analysts
- Other hands-on data analysis team members
Prerequisites
Participants for this course need to have a good understanding of AWS core services, including Amazon Elastic Compute Cloud (EC2) and Amazon Simple Storage Service (S3). They need to have some experience working with a programming or scripting language. They need to be familiar with the Linux operating system and command line interfaces.
Participants will need to have laptops to complete the lab exercises that are a part of this course, tablets will not be appropriate here.
