In the digital age, the ability to process data in real time is no longer a luxury but a necessity. As businesses and industries evolve, the demand for instantaneous insights and rapid decision-making has skyrocketed. At the heart of this transformation lies Apache Structured Streaming, which promises timely insights and actionable results from continuous data streams.
This allows you to understand data as it comes in, rather than waiting on periodic batch data. This enables faster decision-making and helps analysts see trends as they happen. Being able to interpret your data on demand allows you to act swiftly when it comes to the things that affect user experience, security, customer service, or business operations.
Structured Streaming is a service that enables real-time data processes. Here’s a deeper dive into what it is and how it can be used.
Understanding Apache Structured Streaming
Apache Spark Structured Streaming is a scalable and fault-tolerant stream processing engine built using the Spark API. At its core, it allows for the processing of live data streams in a manner similar to processing static batches of data. Structured Streaming works by treating a live data stream as an unbounded table to which new rows are continuously appended.
The big difference is that Structured Streaming processes data as it comes in, reducing the latency associated with batch datasets. To do this, the service runs consistent compute calculations on the incoming data incrementally and continuously updates the result as more information arrives.
Structured streaming supports a variety of input sources like Apache Kafka and Amazon Kinesis, as well as customized sources through API integrations, making it versatile for different use cases.
The Need for Real-Time Optimization
Every industry has a need for real-time optimization. While traditional methods of batch-processing static datasets work in some scenarios, there are many more that require the immediacy of Structured Streaming.
For example, real-time data processing could detect a fraudulent transaction and stop it in its tracks before things get out of hand. It can also enable things like real-time bidding in the ad tech industry or ensure that inventories across multiple warehouses are kept up to date — avoiding having to cancel an order when a product is no longer available.
If these scenarios relied on batch processing static datasets, it could lead to very unhappy customers, potential data breaches, and severe delays. Another benefit of Structured Streaming is its consistency thanks to its batch determinations, which guarantees the same result if rerun.
Enabling Real-Time Optimization
As data comes in from different sources and is pushed to file systems, dashboards, and databases, real-time processing allows you to run analysis in a streaming fashion but you’ll only see the benefits of true real-time results if your data is being ingested efficiently.
Intel Granulate uses continuous optimization to improve data streaming workloads, ensuring the data is processed quickly and efficiently. When used in conjunction with Structured Streaming, this can help keep costs down while increasing throughput.
These solutions integrate seamlessly with existing systems, so if you’re looking to harness the power of real-time data but would like to optimize your workflows as you go, implementation is made simple. Even if you’re already using Structured Streaming and are looking for ways to improve your results, Intel Granulate can solve these issues without the need for extensive overhauls.
In a world where data is the new oil, the ability to refine and process this data in real time is what sets industry leaders apart. With Structured Streaming and Intel Granulate, businesses are well-equipped to navigate the challenges of the digital age, ensuring that they remain competitive, agile, and always a step ahead.