Apache Spark Streaming with Python and PySpark

Spark Streaming can be added to your machine learning and data science Python projects

About This Video

• Spark and Python can be used to create big data streaming pipelines
• Get analytics on Twitter's live tweet data
• Fortune 500 companies use Apache Kafka to integrate Spark Streaming.
• Use the latest Spark version 2.3 to get the most out of your Spark software.

Spark Streaming is gaining popularity, and for good reason. According to IBM, 90% percent of the data in the World today was created within the last two years. Our data output is currently 2.5 quintillion bytes daily. Every day, the world is immersed in data. Analyzing static DataFrames to analyze non-dynamic data is less practical. Data streaming is the solution. It allows data to be processed almost immediately after it is created, and recognizes the time-dependency of data. Apache Spark Streaming allows us to create cutting-edge applications in an infinite way. It is also one the most disruptive technologies in the big data space in the last decade. Spark offers in-memory cluster computing that greatly speeds up interactive data mining tasks and iterative algorithms. Spark is also a powerful engine that can stream data and process it. Spark is a great tool for processing huge data firehoses because of the synergy between them. Apache Spark Streaming is being used by a lot of companies, including Fortune 500 firms, to extract meaning out of massive data streams. You can now access that same big data technology from your computer. This Apache Spark Streaming course can be taken in Python. Python is one of the most used programming languages in the world. Its rich data community makes it an excellent tool for data processing. PySpark, the Python API for Spark, will allow you to interact with Apache Spark Streaming’s main abstraction, RDDs. You can also interact with other Spark components like Spark SQL and many more. Let's see how to create Apache Spark Streaming programs using PySpark Streaming today to process large data sources!