Gone are the days when music was only about writing and composing what the artist wanted to create. Now, they need the help of data analytics to predict the next big hit or trend in the music industry. It’s not as easy as it sounds, given the involvement of big data analytics for music preferences and patterns. However, the right stream processing tools can generate the insights you require for your next project.
Finding meaningful patterns and uncovering valuable insights from data is called Analytics. Traditional and streaming analytics both have similar roles, but streaming analytics differs by analyzing continuous processes and in-motion big data. Streaming analytic data can create visualizations, interpret patterns, generate insights, and trigger real-time or near-real-time processes.
To generate valuable insights from streaming analytics, you need data from clickstreams, equipment sensors, social platforms, app activity, social market, and more. Once you have the data you require for use, the following are the methods or tools you can use to generate useful observations:
1. Amazon Kinesis Streams
Amazon Kinesis Streams is a data stream processing tool. It’s scalable and customizable, allowing you to create applications using APIs, connectors, and client libraries. With the help of a Stream Processor, you can capture and process data without worrying about scalability.
2. Amazon Kinesis
Another Amazon tool with automated processing, fine-tuning, and integrated support. You can use Spark, Kafka, and other Apache services with Kinesis to conduct managed stream processing for data insights. The tool collects and analyzes real-time data and offers the flexibility to choose the right tool for your application.
3. Amazon Kinesis Data Firehose
Want to link live streams of data with BI tools and interfaces like Data Warehouse? You can use Amazon Firehose for this type of data processing. The tool will help you transform and load any streaming data, including music files.
4. Apache Kafka
As a widely-used Stream Processing platform and distributed event store, Apache Kafka is all you need for seamless streaming analytics. It helps you create and manage Data Pipelines, Data Analytics, and Integration. The tool is used by major corporations all over the world. Kafka uses API for data streams and application integration. It can move high amounts of data while running simultaneous processing. You can use Apache Kafka with Hive, Hadoop, and Spark.
5. Apache Storm and Flink
If Kafka does not seem like the right tool for your streaming data, you can use other Apache Stream Processing tools, such as Flink and Storm. Both are distributed Stream Processing tools that you can integrate with Hadoop, a framework that allows distributed processing.
Final Words
Data Streams face CAP issues (Consistency, Availability, and Partition Tolerance) that challenge data analysis for various systems. With the right processing tools, the music industry can benefit from data analytics and stream processing.
Streaming data requires powerful tools like the ones Amazon and Apache offer. They can capture, transform, process, or store real-time data without errors while generating useful insights. While we can’t avoid other real-time data processing issues, using these tools can lower the risk of disruptions.
Eric Dalius is The Executive Chairman of MuzicSwipe, a music and content discovery platform designed to maximize artist discovery and optimize fan relationships. Additionally, he runs the weekly podcast “FULLSPEED,” featuring interviews with cutting-edge entrepreneurs. As the founder of the “Eric Dalius Foundation,” he provides four scholarships for US students. Keep up with Eric on Twitter, Facebook, LinkedIn, Instagram, and Entrepreneur.com.