In course one of the AWS Big Data Specialty Data Collection learning path we explain the various data collection methods and techniques for determining the operational characteristics of a collection system. We explore how to define a collection system able to handle the frequency of data change and the type of data being ingested. We identify how to enforce data properties such as order, data structure, and metadata, and to ensure the durability and availability for our collection approach.
- Recognize and explain the operational characteristics of a collection system.
- Recognize and explain how a collection system can be designed to handle the frequency of data change and the type of data being ingested.
- Recognize and identify properties that may need to be enforced by a collection system.
This course is intended for students looking to increase their knowledge of data collection methods and techniques with big data solutions.
While there are no formal prerequisites, students will benefit from having a basic understanding of analytics services available in AWS. Please take a look at our Analytics Fundamentals for AWS
This Course Includes
- 45 minutes of high-definition videos
- Live hands-on demos
What You'll Learn
- Introduction to Collecting Data: In this lesson, we'll prepare you for what we'll be covering in the course; the Big Data collection services of AWS Data Pipeline, Amazon Kinesis, and AWS Snowball.
- Introduction to Data Pipeline: In this lesson, we'll discuss the basics of Data Pipeline.
- AWS Data Pipeline Architecture: In this lesson, we'll go into more detail about the architecture that underpins the AWS Data Pipeline Big Data Service.
- AWS Data Pipeline Core Concepts: In this lesson, we'll discuss how we define data nodes, access, activities, schedules, and resources.
- AWS Data Pipeline Reference Architecture: In this lesson, we'll look at a real-life scenario of how data pipeline can be used.
- Introduction to AWS Kinesis: In this lesson, we'll take a top-level view of Kinesis and its uses.
- Kinesis Streams Architecture: In this lesson, we'll look at the architecture that underpins Kinesis.
- Kinesis Streams Core Concepts: In this lesson, we'll dig deeper into the data records.
- Kinesis Streams Firehose Architecture: In this lesson, we'll look at firehose architecture and the differences between it and Amazon Kinesis Streams.
- Firehose Core Concepts: Let's take a deeper look at some details about the Firehose service.
- Kinesis Wrap-Up: In this summary, we'll look at the differences between Kinesis and Firehose.
- Introduction to Snowball: Overview of the Snowball Service.
- Snowball Architecture: Let's have a look at the architecture that underpins the AWS Snowball big data service
- Snowball Core Concepts: In this lesson, we'll look at the details of how Snowball is engineered to support data transfer.
- Snowball Wrap-Up: A brief summary of Snowball and our course.
Okay, let's have a look at the core concepts that underpin the Amazon Kinesis Firehose Big Data service. AWS makes Kinesis Firehose simple to use by predefining the data flows that are required to load data into the destinations. For Amazon S3 destinations, streaming data is delivered to your S3 bucket. If data transformation is enabled, you can optionally back up source data to another Amazon S3 bucket.
For Amazon Redshift destinations, streaming data is delivered to your S3 bucket first. Kinesis Firehose then issues an Amazon Redshift copy command to load from your S3 bucket to your Amazon Redshift cluster.
If data transformation is enabled, you can optionally back up source data to another Amazon S3 bucket. Note that you need to configure your Amazon Redshift cluster to be publicly accessible and unblock the Kinesis Firehose IP addresses. Also note that Kinesis Firehose doesn't delete the data from your S3 bucket after loading it to your Amazon Redshift cluster. For Amazon Elasticsearch destinations, streaming data is delivered to your Amazon Elasticsearch cluster and can optionally be backed up to your S3 bucket concurrently. There are a number of limits within Amazon Kinesis Firehose service you need to be aware of.
Amazon Kinesis imposes limits on resources that you can allocate and at the rate at which you can allocate resources. The displayed limits apply to a single AWS account. If you require additional capacity, you can use the standard Amazon process to increase the limits for your account when the limit is flagged as adjustable.
Shane has been emerged in the world of data, analytics and business intelligence for over 20 years, and for the last few years he has been focusing on how Agile processes and cloud computing technologies can be used to accelerate the delivery of data and content to users.
He is an avid user of the AWS cloud platform to help deliver this capability with increased speed and decreased costs. In fact its often hard to shut him up when he is talking about the innovative solutions that AWS can help you to create, or how cool the latest AWS feature is.
Shane hails from the far end of the earth, Wellington New Zealand, a place famous for Hobbits and Kiwifruit. However your more likely to see him partake of a good long black or an even better craft beer.