G2

umar k.

Data Eng

Computer Software

Enterprise (> 1000 emp.)

3/12/2026

"Seamless Azure Integration with Effortless Spark Scaling on HDInsight"

5/5

What do you like best about Apache Spark for Azure HDInsight?

The best part about using Spark on HDInsight is the seamless integration within the Azure ecosystem. It allows for effortless scaling of compute power to handle massive datasets. The managed nature of the service means I don't have to worry about the underlying infrastructure overhead, and the Jupyter Notebook integration makes iterative development and data exploration extremely efficient for our engineering team Review collected by and hosted on G2.com.

What do you dislike about Apache Spark for Azure HDInsight?

One downside is the cluster spin-up time, which can feel slow when you need immediate compute for ad-hoc tasks. Additionally, while it is a robust managed service, the pricing can escalate quickly if clusters are not managed or scaled down properly during idle times. It also feels slightly less 'modern' compared to newer alternatives like Azure Databricks, particularly regarding UI and collaborative features Review collected by and hosted on G2.com.

What problems is Apache Spark for Azure HDInsight solving and how is that benefiting you?

We use it primarily for heavy ETL (Extract, Transform, Load) processes and large-scale data processing that traditional SQL environments can't handle. It has significantly reduced our processing time for daily data pipelines. By leveraging its distributed computing capabilities, we can process terabytes of data in parallel, which directly helps our business stakeholders get insights much faster than before Review collected by and hosted on G2.com.