Databricks Data Intelligence Platform vs. Zoho DataPrep

If you need a managed big data megastore, which has native integration with highly optimized Apache Spark Engine and native integration with MLflow, go for Databricks Lakehouse Platform. The Databricks Lakehouse Platform is a breeze to use and analytics capabilities are supported out of the box. You will find it a bit difficult to manage code in notebooks but you will get used to it soon.

Incentivized

Scenarios where it works well: 1. Preparing reports like CRM, Marketing, and sales for monthly reports. 2. Cleaning CSV files with inconsistent format 3. Removing of duplicates and finding error values 4. Enabling non-technical people to manage data preparation without coding. Scenarios where it is less appropriate: 1. Processing real time data and large scale data needs 2. Deep customization at code level 3. At advanced predictive modeling

Incentivized

• First, it handles large amounts of data. We run daily and weekly jobs that process a lot of records. Databricks manages it very well, with no issues, if the cluster is set up properly.
• Second, it really works well for incremental updates. We load only new or changed data, which makes it easy to update existing tables without duplicating records.
• Third, job scheduling is useful. We can schedule the jobs easily and monitor them. The best part is that we can retry or repair the failed runs.
• The last one is about the notebook interface that I really love. It makes development and debugging easy. We can test logic step by step, validate data, and fix all our issues.

Incentivized

• Connect my local code in Visual code to my Databricks Lakehouse Platform cluster so I can run the code on the cluster. The old databricks-connect approach has many bugs and is hard to set up. The new Databricks Lakehouse Platform extension on Visual Code, doesn't allow the developers to debug their code line by line (only we can run the code).
• Maybe have a specific Databricks Lakehouse Platform IDE that can be used by Databricks Lakehouse Platform users to develop locally.
• Visualization in MLFLOW experiment can be enhanced

Incentivized

Because it is an amazing platform for designing experiments and delivering a deep dive analysis that requires execution of highly complex queries, as well as it allows to share the information and insights across the company with their shared workspaces, while keeping it secured.

in terms of graph generation and interaction it could improve their UI and UX

Incentivized

I would rate Zoho DataPrep a 10/10 for its usability because I feel it is very easy and intuitive platform and even non-technical people can access it easily without coding knowledge. Also, its workflow builder is very straightforward, once we create any data pipeline then we can reuse it easily.

Incentivized

One of the best customer and technology support that I have ever experienced in my career. You pay for what you get and you get the Rolls Royce. It reminds me of the customer support of SAS in the 2000s when the tools were reaching some limits and their engineer wanted to know more about what we were doing, long before "data science" was even a name. Databricks truly embraces the partnership with their customer and help them on any given challenge.

Databricks is a true all-in-one platform, and at the time of implementation, it had more features available to us, making it a clear choice over Snowflake. Moving our workloads from local computing to the servers in Databricks gave our start-up staff a great quality of life boost.

Incentivized

Zoho DataPrep is much easier to use and quick to set up. It user interface is very user friendly, even people with no technical knowledge can use it easily to transform the data. It creates the repeatable workflows easily and more cost-effective for small to mid-size teams. We selected it because it aligns with our business needs and goals perfectly like fast, easy, and efficient for everyday use.

Incentivized

• ROI for us has been tremendous. Time to market by processing raw data in our big data infrastructure has been pretty fast.
• Non engineers can easily use Databricks, hence helping business customers.
• Thousands of different data combinations can easily be joined and used by our data teams.

Incentivized