Azure Databricks Reviews & Product Details - Page 3

The most impressive thing which I liked about the Azure Databricks is that It helps to simply our Data science and Data engineering workloads. As we use Azure as our cloud provider including Databricks from the same vendor allows us to do all billing without any hazel. The optimised notebook in Databricks help us to write efficient pyspak code which can handle big data processing like a charm. Also it get easily integrated with Azure DataFactory to build some pipeline for automation purpose. Review collected by and hosted on G2.com.

1. We use Azure Databricks to build som code in notebook which can process and transform complex data problems, but when it comes to automate, the Databricks native workflow and job scheduler doesn't give that much features when comparing to other platforms like Azure DataFactory.

2. When we use SQL environment for data warehousing purpose, since it use Photon engine the upfront cost of having it is much higher than other platforms like snowflake. Review collected by and hosted on G2.com.

As Platform engineer it's easy to use and understand databricks. User friendly portal. Workflows and Computes I am using on daily basis. Catalogs gives very clear feature of datalog, schemas and tables. Databricks customer support team is very helpful. Review collected by and hosted on G2.com.

No dislike till now. But sometimes ganglia matrics and logs are not coming poroperly. Review collected by and hosted on G2.com.

It is cool. It gives you everything you need to perform analytics, ranging from Data engineering workspace, to features specially for ML and data science models.

It has features like running the scripts in sql, python, scala and R, which is a huge flexibility for a developer.

These days they have given option to create dashboards also, it means one can create on-fly dashboards to analyse their datasets.

Personally, i love their command like interface (like jupyter notebook) and the way we could configure clusters on-fly. Unity catalog is the recent feature which i am working on. Review collected by and hosted on G2.com.

While Azure Databricks is a robust platform, occasional user interface responsiveness issues have been noted. Additionally, the initial learning curve may pose challenges for new users, affecting ease of onboarding and usability.

Specifically, one need to learn the basics of the various specific techs, like parameterization, configuration of clusters, running jobs on workflows, and a lot of specific syntax which are databricks-specific and not spark in general. Review collected by and hosted on G2.com.

I'm liking all the feature of azure databricks specially coding facilities. We can do the coding in Scala, pyspark and using R language. Also the best part it it we can make use of Sql, to query. I used to code in databricks using pyspark and sql features and with this I can do entire ETL process( Exrract, transform and load the data into database). Review collected by and hosted on G2.com.

There is nothing as such which I don't like about azure databricks, only sometimes the drivers are becoming unhealthy due to heavy load on cluster and sometimes to clear ka cache we need to restart the cluster. Review collected by and hosted on G2.com.

I am a data engineer and Databricks integration into azure has made my work a lot easier i can directly spin up spark clusters on cloud and work on big data .The interface is easy to use just like a python notebook and its packed with so many features. Review collected by and hosted on G2.com.

well for me there hasn't been any issues while using but using azure databricks can cost money. You need to be careful about how much you use to avoid high bills.azure databricks works closely with microsoft azure. If you prefer using different cloud services his might not be the best for you. Review collected by and hosted on G2.com.

It has a fexcibility to handle big data and databricks provides functionallity of delta lake so it's easy to read and write data in parquet format that is more faster in processing also delta lake is more usable as deta lake. Review collected by and hosted on G2.com.

No dislike yet, Only thing is that it gives the performance as per Cluster level configuration. So some times it takes higher cost or processing. Review collected by and hosted on G2.com.

One of the best datalake solutions in the market with efficient data storage and retrieval capabilities. Also introduction of various layers like bronze, silve and Gold allows you to segregate your logical data retrieval Review collected by and hosted on G2.com.

One of the major drawbacks is integration with third party tools if someone wants to connect to azure Databricks with HL7 spy or Caristix. Also UI needs some modifications in terms of how much data one can allow to be populated from cache Review collected by and hosted on G2.com.

Azure Databricks is a very powerful ETL tool integrated with Spark. We can use python, scala , java and sql language to write our ETL logic and then it has its own inhouse hive storage and also it can perform orchestration. Review collected by and hosted on G2.com.

The main disadvantage which I think its relational database model. Which is not powerful as other rdbms but they are working on it using delta and iceberg concept. Review collected by and hosted on G2.com.

-Spinning up cloud based Spark environmet in minutes , autoscale capabilities in case of high performance required.

-Multi language support like python, Scala , R , Java , SQL

- Best tool for AI based systems and data science requirements.

-large scale data processing for both batch & streaming workloads. Review collected by and hosted on G2.com.

There are no downsides of Azure databricks as it's having many benefits like large scale data processing for both batch & streaming workloads.

End to end data analytics.

So featurewise there are no drawback at all Review collected by and hosted on G2.com.

Azure databricks is easy to use and support all the features like data engineering tools support like spark and mlops tools like mlflow.

Easy to scale in production with minimal efforts Review collected by and hosted on G2.com.

Few components are tightly coupled those are not flexible to change Review collected by and hosted on G2.com.