Learn the basics of real-time analytics.
- [Narrator] Alright, now let's take a look…at doing real-time processing here,…and in Spark terms, we call this streaming analytics.…Now, here's an ideal worldview…of how your big data system could help everyone.…Real time events stream in,…and data at rest gets batched in…on a regular interval, say nightly.…Inside of Spark Streaming, you can execute…both machine learning queries,…using MLlib, as well as interactive queries,…using Spark SQL.…Data is processed and then exported…to either a storage engine or to your analytics platform.…
Some common use cases are streaming ETL,…where data is continuously cleansed…and aggregated before being stored;…detecting anomalies in your data…and triggering further jobs, think fraud protection…from your bank; augmented user experience,…where you can change the interface for the user…to match their preferences; and in other good cases,…performing real-time analytics, where you're actually…measuring events as they occur.…This is particularly useful for system monitoring…for websites and call volume monitoring for call centers.…
- Understanding Spark
- Reviewing Spark components
- Where Spark shines
- Understanding data interfaces
- Working with text files
- Loading CSV data into DataFrames
- Using Spark SQL to analyze data
- Running machine learning algorithms using MLib
- Querying streaming data
- Connecting BI tools to Spark
Skill Level Intermediate
Amazon Web Services for Data Sciencewith Lynn Langit3h 56m Intermediate
1. Introducing Apache Spark
2. Analyzing Data in Spark
3. Using Spark SQL to Analyze Data
4. Running Machine Learning Algorithms Using MLlib
5. Real-Time Data Analysis with Spark Streaming
6. Connecting BI Tools to Spark
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