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.
Chief Data-strategist and Director at Theworkshop.es
Real User
Top 10
2024-01-25T11:39:24Z
Jan 25, 2024
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.
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.
Chief Technology Officer at Teslon Technologies Pvt Ltd
Real User
Top 20
2023-06-08T10:44:00Z
Jun 8, 2023
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 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.
What is Streaming Analytics? Streaming analytics, also known as event stream processing (ESP), refers to the analyzing and processing of large volumes of data through the use of continuous queries. Traditionally, data is moved in batches. While batch processing may be an efficient method for handling huge pools of data, it is not suitable for time-sensitive, “in-motion” data that could otherwise be streamed, since that data can expire by the time it is processed. By using streaming...
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.
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.
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 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 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.
We're primarily using the solution for anomaly detection.
Near Real time analytics using Near real time data ingestion.
The primary use of the solution is to implement predictive maintenance qualities.