In this chapter, you’ll be given an introduction to basic concepts in batch data processing, revolving around implementation, scaling, and potential challenges during use.

Intro to batch processing

Batch characteristics

Batch ordering

Scaling batch processing

On the scale

Horizontally opposed

Batch issues

Batch problems

Batch scenarios

Methods for Processing Data

Here, you’ll learn about the basics of streaming, event-based data processing, and the advantages  over traditional batch-based data processing.

Intro to event-based computing

In the event of...

Welcome to the event!

Queuing

Queue characteristics

To queue or not to queue

Single system data streaming

Log stream order

Log options

Batching vs. streaming

Batch, queue, or stream?

Log stream processor

Intro to Streaming

This chapter introduces the concepts behind modern streaming systems, how to scale them, and some of the issues found in using streaming processes.

Intro to real-time streaming

Real-time?

Is it real this time?

Vertically scaling streaming systems

Scaling reasons

To vertically scale...?

Horizontally scaling streaming systems

Upscaled out

SLA guarantees

Streaming roadblocks

Streaming attributes

Issue types

Streaming challenges

Streaming Systems

In this final chapter, you’ll learn about common streaming systems and consider the implementation of streaming data processing in different use cases.

Popular streaming systems

Options

Statements

True

False

Streaming truths

Requirements

Celery

Kafka

Spark streaming

Crossing the streams...

Real-world use case: streaming music service

Attributes

Required

Not required

Message components

Questions

Can answer

Cannot answer

Answer me this...

Real-world use case: sensor data

SLA priority

Great order of the SLAs

Projects

Vertical

Horizontal

Sensor scaling considerations

Real-world use case: vaccination clinic

Areas

Batch process

Queued process

Streaming process

Vaccination clinic - classify areas

Batch

Queue

Stream

A new problem...

Congratulations!

Real-World Use Cases

Streaming is a huge aspect of the data world right now and is being used by nearly every industry from manufacturing to healthcare. Would you like to learn more about the general concepts behind data pipelines and how the processes work?

This course provides a general introduction to streaming concepts including batching, queuing, and stream processing along with where they fit into data processing frameworks. It covers real-world examples of how streaming is implemented in production. It is designed as a general introduction to these concepts and does not require an extensive background in data processing.


Understanding Data Engineering

Learn about the difference between batching and streaming, scaling streaming systems, and real-world applications.

Streaming Concepts

Professional Data Engineer in Python

Spark streaming can be used to transition from batch workloads

Kafka can store messages indefinitely, assuming there is space to do so

Spark streaming can store messages indefinitely

Streaming truths

Streaming Concepts

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