Real-time data streaming and processing
There are two techniques used for
streaming big data: batch and stream. The first is a collection of grouped data points for a specific time period. The second handles an ongoing flow of data and helps to turn big data into fast data.
Batch processing requires all data to be loaded to storage (a database or file system) for processing. This approach is applicable if real-time analytics isn’t necessary. Batching big data is great when it’s more important to process large volumes of data than to obtain prompt analytics results. However, batch processing isn’t a must for working with big data, as stream processing also deals with large amounts of data.
Unlike batch processing,
stream processing processes data in motion and delivers analytics results fast. If real-time analytics are critical to your company’s success, stream processing is the way to go. This methodology ensures only the slightest delay between the time data is collected and processed. Such an approach will enable your business to be transformed fast if needed.
There are open-source tools you can use to ensure real-time processing of big data. Here are some of the most prominent:
Apache Spark is an open-source stream processing platform that provides in-memory data processing, so it processes data much faster than tools that use traditional disk processing. Spark works with HDFS and other data stores including OpenStack Swift and Apache Cassandra. Spark’s distributed computing can be used to process structured, unstructured, and semi-structured data. Furthermore, it’s simple to run Spark on a single local system, which makes development and testing easier.
Apache Storm is a distributed real-time framework used for processing unbounded data streams. Storm supports any programming language and processes structured, unstructured, and semi-structured data. Its scheduler distributes the workload to nodes.
Apache Samza is a distributed stream processing framework that offers an easy-to-use callback-based API. Samza provides snapshot management and fault tolerance in a durable and scalable way.
World-recognized companies often choose these well-established solutions. For example, Netflix uses Apache Spark to provide users with recommended content. Because Spark is a one-stop-shop for working with big data, it’s increasingly used by leading companies. The image below shows the principle of how it works:
You can also opt for building a custom tool. Which way you should go depends on your answers to three how much questions:- How much complexity do you plan to manage?
- How much do you plan to scale?
- How much reliability and fault tolerance do you need? If you’ve managed to implement the above-mentioned process properly, you won’t lack users. So how can you get prepared for them? By ensuring...
