As a data engineer, I use Apache Spark Streaming to process real-time data for web page analytics and integrate diverse data sources into centralized data warehouses.

We have built services around Apache Spark Streaming. We use it for real-time streaming use cases. There are many last-minute delivery use cases. We are trying to build on Apache Spark Stream but the latency has to be better.

The solution has industry-related use cases, with orders flowing from the order management system. We use Apache Spark Streaming to collect and store these orders in our database.

We use Spark Streaming in a micro-batch region. It's not a full real-time system, but it offers high performance and low latency.

We used Spark and Spark Streaming, as well as Spark ML, for multiple use cases, particularly streaming IoT-related data. Additionally, we applied Spark ML for various machine learning algorithms on the streaming data, mainly in the healthcare space. So, primarily in the healthcare domain.

The primary use case of this solution is for streaming data. It can stream large amounts of data in small data chunks which are used for Databricks data. I've been using the solution for personal research purposes only and not for business applications. I'm a customer of Apache. 

We're primarily using the solution for anomaly detection.

I've used it more for ETL. It's useful for creating data pipelines, streaming datasets, generating synthetic data, synchronizing data, creating data lakes, and loading and unloading data is fast and easy. 

In my ETL work, I often move data from multiple sources into a data lake. Apache Spark is very helpful for tracking the latest data delivery and automatically streaming it to the target database.

The primary use of the solution is to implement predictive maintenance qualities.