Udacity: The Ultimate Guide to Data Streaming Nanodegree by Udacity
- Aisha
- Nov 7, 2019
- 5 min read
Updated: Nov 13, 2019
Disclosure: This post contains affiliate links, meaning I will earn a commission if you click through and make a purchase, you will not pay the additional cost for your purchase.
Every fresh graduate has one question
“How to stand out in a saturated job market?”
It is a common lot of the companies that include everything they can dream of having, in the job description. every kind of skill they need or plan to employ in the future. The job description does not fit one employee, it’s like combinations of all the departments they are looking for, in one person and, after a long list of requirements they ask for two years’ experience.
In this situation whenever candidates go through requirements, they feel devastated. If you’ve been on a course for some time now, it doesn’t hurt to think outside the box.
Data streaming is one of the fields that will get you to a new area of requirement. Data streams are on-demand as a media publisher because they stream billions of click stream records from its online properties, an online gaming company collects streaming data about player-game interactions, social media, and online store collect data from their customers. This process is continuous. Data streaming is vital for digital marketing, and online marketing’s growth. Companies like to collect and read data to understand their customers’ interests and trends.
Data Engineer is one of the most outstanding jobs for 2019, with a base salary of $100k.
Many companies increasingly rely on applications that produce and process data in real-time, data streaming is a high in-demand skill for data engineers.
What is a data stream?
Streaming Data is data that is achieved regularly by thousands of information sources, which typically send in the data records simultaneously, Streaming data includes a diversity of data, such as bulk files generated by customers using mobile or web applications, online shopping, in-game player activity, information from social networks, commercial trading.
What is the benefit of streaming data?
Information collected from data streaming gives companies clarity into many aspects of their business and customer activity such as billing information, server activity, it gauges customers' search behavior and their locations. It enables them to respond promptly to emerging situations.
For example, companies can track a change in customers’ behaviors on their brand and products by continuously analyzing social media streams. If companies have launched a new product, they can read and visualize respond to their products in the market, which helps them in the future what kind of products are in demand?
Job opportunities for data engineers
Data engineering is a developing field, with many opportunities for job and career growth existing across all industries. The volume of data is increasing day by day, the information generated by every human being has been growing every second, according to newganapps.com
By 2020, the collected volume of big data will increase from 4.4 zettabytes to roughly 44 zettabytes or 44 trillion GB.
Originally, data engineers continue that the volume of data would double every two years thus reaching the 40 ZB point by 2020. That number was later bumped to 44ZB when the impact of IoT was brought into consideration.
Therefore, the demand for data engineering will increase tremendously in 2020.
Data streaming and data engineering
Data streaming is a skill that will take you to the next level of data engineering. Data streaming is the process of collecting data in real-time by building fluency in modern data engineering tools, such as Apache Spark, Kafka, Spark Streaming, and Kafka Streaming. If you are a data engineer this skill will hoist you in the crowd of data engineers.
The goal of the Data Streaming Nanodegree program design by Udacity is to provide students with the latest skills to Process information in real-time by building fluency in modern data engineering tools, such as Apache Spark, Kafka, Spark Streaming, and Kafka Streaming.
A graduate of this program will be able to:
Learn the components of data streaming systems. Ingest data in real-time using Apache Kafka
Use the Faust Stream Processing Python library to build a real-time stream-based application. Assemble real-time data and run live analytics, as well as draw insights from reports generated by the streaming console.
Master the Kafka ecosystem, and the types of issues each solution is designed to solve. Use the Confluent Kafka Python library for simple topic management, production, and utilization.
Explain the components of Spark Streaming (architecture and API), integrate Apache Spark Structured, Streaming and Apache Kafka, shape data using Spark, and understand the statistical report generated by the Structured Streaming console.
This program consists of 2 courses and 2 projects. Each project you build will be a fortune to demonstrate what you’ve learned in the course and will demonstrate to employers that you have skills in these areas.
Essential knowledge to be eligible for the Udacity Data Streaming program.
Intermediate SQL, Python, and experience with ETL. Basic familiarity with traditional batch processing and traditional service architectures is preferred, but not required.
Estimated time
Two month
5 to 10 hours/week
Fees: $549.2
Flexible Learning:
Self-paced, so you can learn on the schedule that works best for you.
The course details.
It includes two courses.
Course 1: Foundations of Data Streaming, and SQL & Data Modeling for the Web
The purpose of this course is to demonstrate knowledge of tools such as Kafka Consumers, Producers, Sinks, Kafka REST Proxy for producing data over REST, Data Schemas with JSON and Apache Avro/Schema Registry, Stream Processing with the Faust Python Library, and Stream Processing with KSQL.
You will learn in the first course
Learn and master Kafka architecture, topics, and configuration
Apply Confluent Kafka Python to build topics and configuration
Learn Kafka producers, consumers, and configuration
Apply Confluent Kafka Python to create producers and configuration
The first part includes 7 lessons
Lesson one: introduction to stream processing
Lesson two: Apache Kafka
Lesson three: Data Schemas and Apache Avro
Lesson four: Kafka Connect and REST Proxy
Lesson Five: Stream Processing Fundamentals
Lesson Six: Stream Processing with Faust
Lesson Seven: KSQL
Course 2: Streaming API Development and Documentation
This course has been designed to grow your expertise in the components of streaming data systems, and build a real-time analytics application. You will be able to: explain components of Spark Streaming, Learn streaming data to Apache Spark Structured Streaming and perform analysis, integrate Apache Spark Structured Streaming and Apache Kafka, and understand the statistical report generated by the Structured Streaming console.
You will learn and master through this part of the course,
illustrate and explain the big data ecosystem
illustrate and explain the hardware behind big data
illustrate and explain distributed systems
figure out when to use Spark and when not to use it.
It includes six lessons
Lesson one: The Power of Spark
Lesson two: Data Wranglng with Spark
Lesson three: Debugging and Optimization
Lesson Four: Debugging and Optimization
Lesson Five: Structured Streaming APIs
Lesson Six: Integration of Spark Streaming and Kafka
You can customize your study plan to work as per your convenience, you will able to work with a mentor, a mentor will help you to customize your study plan to suit your timing and schedule. Mentor will help you to keep track of your progress goal too.
Comments