G2

Saloni N.

Junior Data Engineer

Mid-Market (51-1000 emp.)

10/19/2026

"Databricks Makes Real-World Data Pipelines Easy with Notebooks, Spark, and Delta Lake"

4.5/5

What do you like best about Databricks?

I like Databricks because it gives me a practical environment to work with data processing and transformation using Python, PySpark, and SQL. I can explore datasets, clean inconsistent records, remove duplicates, and prepare structured data for reporting. I also find the notebook environment helpful because I can test my code step by step and understand the result immediately. I like notebooks, PySpark DataFrames, Spark SQL, Delta Lakes, and job scheduling. Notebooks make development easier, while Delta Lake helps to manage reliable tables and supports operations such as updates and incremental processing. These features are useful for learning real-world data pipeline development. The workspace is well-suited for writing and testing code in notebooks; running individual cells and viewing output makes debugging easier. However, it works well with cloud storage, data orchestration tools, and other data platforms. Connecting it with Azure Data Lake Storage and Azure Data Factory is useful for building end-to-end data pipelines. Review collected by and hosted on G2.com.

What do you dislike about Databricks?

The platform has many features, so it can take time to understand the workspace compute settings, permissions, and job configuration. Clustering starting times can sometimes interrupt development, and I need to be careful with compute usage to avoid unnecessary costs. Review collected by and hosted on G2.com.

What problems is Databricks solving and how is that benefiting you?

Databricks helps me process data that would be difficult to handle efficiently with basic scripts alone. I can combine data from different sources, apply transformation rules, perform data quality checks, and organize datasets into bronze, silver, and gold layers. This gives me a better understanding of how raw data becomes reliable, analytics-ready information. It performs well when processing large datasets with Spark. I can use DataFrame operations and Spark SQL to transform data and analyze results. Performance depends on cluster configuration, data size, partitioning, and the way transformations are written, so I am still developing my optimization skills. The AI-assisted development features can help explain code, suggest improvements, and speed up SQL or PySpark development where available. Review collected by and hosted on G2.com.