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"A Game Changer For DDO"
What do you like best about the product?
Databrick is very user friendly, and i find it to be very benificial. The feature i like the most is MLflow as it allows us to keep record of machine learning models. It also processes data very quickly and reduces workload, which is very helpful. Additionally it is cost efficient, and multiple team can collaborate easily.Its Spark support, ML pipelines and DL make it an even more powerful tool for DDO.I find it to be a perfect platform.
What do you dislike about the product?
There is nothing in it that is worth disliking. But in my opinion, for beginners, understanding its interface and features can be difficult at first, but otherwise, it is a fantastic platform, and its features are very powerful
What problems is the product solving and how is that benefiting you?
I didn't face any issue with it, but if i had to, Databricks 'customer support is excellent. I found their higher plan to be very benificial as i received a quick response from customer support, and the phone support is outstanding.
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Scalability of data bricks helped me a lot in enhancing my work quality
What do you like best about the product?
Databricks significantly enhances my work by providing a "unified platform" that integrates various data sources and supports multiple programming languages, simplifying the development and management of ETL workflows. Its scalability, powered by Apache Spark, allows for horizontal scaling to handle increasing data volumes efficiently. Additionally, the collaborative notebooks foster teamwork among data engineers, scientists, and analysts, streamlining communication and accelerating workflow development.
What do you dislike about the product?
I can say about the dislikes of data bricks that
-Cost Management Complexity:
Scaling can lead to unexpected costs if not monitored closely.
- Management Overhead:
Large clusters increase complexity in management and configuration.
- Performance Variability:
Improper configurations may lead to inefficiencies and longer runtimes.
-Cost Management Complexity:
Scaling can lead to unexpected costs if not monitored closely.
- Management Overhead:
Large clusters increase complexity in management and configuration.
- Performance Variability:
Improper configurations may lead to inefficiencies and longer runtimes.
What problems is the product solving and how is that benefiting you?
the collaborative notebooks foster teamwork among data engineers, scientists, and analysts, streamlining communication and accelerating workflow development.
Superb data analytics and Ai platform !
What do you like best about the product?
It has been very amazing in creating data pipelines for data transformation and data analysis + queries easily in dashboard. It is best for data engineers in our company , they use it daily for implementing ML and setting up workflow using Databricks.
What do you dislike about the product?
I think trial period can be bit enhanced for testing this vast platforms. In terms of functionality i see no issues.
What problems is the product solving and how is that benefiting you?
Databricks played big role in warehouse , ML feature with Ai capabilities for managing workflow in team project . Plus it is very helpful in data transformation and analysis which is very much needed.
Ai driven big data and Ml in one place !
What do you like best about the product?
I really like databricks 's integration with Apache spark which helps us in analysing large datasets fairly quickly . And It feature of unifying different data sources in one places is also my favorite feature of this tool.It also has very advanve Machiene learning which helps our data scientist and analyst to work in sync.Also its costumer support is very helpful too.
What do you dislike about the product?
It is definately not for beginners in dataset operations , needs a highly skilled person to do the job.Other than that there can be high cost because it need resources so for small companies it might not be good fit.
What problems is the product solving and how is that benefiting you?
Databricks helps us in collecting all the data sets from different sources which further helps us in simplifying workflows and improved collaboration in team.Also its integration with Apache Spark is very helpful for processinf big amount of data very quickly.
Powerful Analytics Platform
What do you like best about the product?
I like the scalability and flexibility Databricks provides for large datasets. It easily integrates with cloud storage platforms like AWS and Azure. This helps our team implement and analyze massive datasets quickly.
What do you dislike about the product?
The pricing can be high for smaller organizations, making it less accessible to smaller projects.
What problems is the product solving and how is that benefiting you?
The integration with various data sources and AI tools has simplified our complex workflows, which has led to faster project completion and overall business growth.
Best platform for data engineers !
What do you like best about the product?
It has been great platform for our company . It works on real time data collection tech and ai . Its monitoring feature works excellently for data analysis and data distribution. Its scalability can be increased with large data volumes very smoothly. Its ability to improve frequency and pipeline has really good thing.
What do you dislike about the product?
I find that it will need some learning curve at the beginning other than that its great tool.
What problems is the product solving and how is that benefiting you?
Databricks has been great tool for data processing Big data and ML . It is very helpful in complex data workflows management in team. It also supports ai based insights that are way faster and accurate than manual . It sorts our issue of collboration between teams of data.
1 person found this helpful
Unified environment for data engineering, ML and analytics
What do you like best about the product?
- handles large datasets and complex workflows
- using apache spark for efficient processing
- using apache spark for efficient processing
What do you dislike about the product?
- learning curve for new user
- cost is an additional factor
- cost is an additional factor
What problems is the product solving and how is that benefiting you?
- helps in managing large scale datasets, data processing and data validation
- solving data science business problems
- helps in customer report generation and dashboarding
- building pipelines for ETL
- solving data science business problems
- helps in customer report generation and dashboarding
- building pipelines for ETL
Data analyst's go to platform !
What do you like best about the product?
Databricks has been amazing to work on. It provides real time data collection and management through big data ML , so resourceful i would say. It combines different data from large scale data processes and make it more usable which is execellent for medium as well big enterprises.And most amazing part i like its integration with Delta lake and spark.
What do you dislike about the product?
It is an advance platform although it needs some improvement in ui for beginners and cost can get pretty high for working on large data.
What problems is the product solving and how is that benefiting you?
Databricks is very helpful in sorting out and analysis of large datasets in real time which is big thing. Scalability options are very very versatile and ai works swiftly for analysis and decision making as well. So it makes us quite productive and saves lots of time.
1 person found this helpful
All in one database management tool with Ai !
What do you like best about the product?
I find it very reliable and futuristic for its price . There are lots of function that is very important for our company like Data transformation tool and data analyst usage with queries makes it even more useful.I also use lakeflow which is quite easy to implement with low codes and customer support helped us with important guides to implement in initial setup.
What do you dislike about the product?
It is powerful tool although its Machiene learning and Ai capabilities can be made more smootherand better.
What problems is the product solving and how is that benefiting you?
Databricks managed all big data in our company and generates good results when our analyst team compare it with when we were not using it. I never faced downside or major clutter while working on this platform .
Unlocking Insights - Databricks : Unified Lakehouse Platform for Modern Data Intelligence
What do you like best about the product?
Mainly 60-70% of day-day actviities goes with Databricks DLT , Autoloader, databricks Workflows usage for Building unified pipelines
Also I use for creating the MlOps MlFlow Model serving , feature Store Online store tables for Model deployments, evaualtion & use the Lakeouse flow monitoring. Also used Mosiac AI gateway
Soon looking forward of using the Databricks serverless in engineering notebook pipelines also apart from DLT
DB-SQL with Delta Photon Optimization and the lIquid clustering solves the Optimize/Z-order manual work of optimizations
Also recently migrated coupled oh Hive Metastore of various Line of busienss for Civil quants & Risk applications into Unity Catalog
Also I use for creating the MlOps MlFlow Model serving , feature Store Online store tables for Model deployments, evaualtion & use the Lakeouse flow monitoring. Also used Mosiac AI gateway
Soon looking forward of using the Databricks serverless in engineering notebook pipelines also apart from DLT
DB-SQL with Delta Photon Optimization and the lIquid clustering solves the Optimize/Z-order manual work of optimizations
Also recently migrated coupled oh Hive Metastore of various Line of busienss for Civil quants & Risk applications into Unity Catalog
What do you dislike about the product?
Currently not much, slowly everything getting addressed on improvements with Public releases starting from enahnced workflows , Genie, Databricks AI assitant & so
Still need to more fine tune the AI assitants to understand the previous error history happened in that cluster & provide suggestions not only in code syntax but also provide alternate options to permanently solve & write teh code in optmized ways without asking in Prompts
Vector search need more improvemnt with more precise reterievals to provide context for DBR models & more embedding models need to be served from AI gateway
Still need to more fine tune the AI assitants to understand the previous error history happened in that cluster & provide suggestions not only in code syntax but also provide alternate options to permanently solve & write teh code in optmized ways without asking in Prompts
Vector search need more improvemnt with more precise reterievals to provide context for DBR models & more embedding models need to be served from AI gateway
What problems is the product solving and how is that benefiting you?
Used for Migrating Hive Metastore to Unity Catalog ensures better data governance and accessibility across various lines of business, enhancing data management and compliance. Integrated also Snowflake catalog to Unity catalog as external catalog as part of POC tetsing
Lakehouse flow monitoring provides insights into data processes, allowing for timely detection and resolution of issue, used DLT for data quality expectation for providing insights
For analysis DB-SQL leveraging Delta and Photon optimization, you minimize manual optimization efforts and improve query performance through automatic Z-ordering and clustering.
For model building Utilizing MLFlow for model serving and feature store tables allows for smoother model deployment and evaluation, enhancing the overall machine learning lifecycle.
Lakehouse flow monitoring provides insights into data processes, allowing for timely detection and resolution of issue, used DLT for data quality expectation for providing insights
For analysis DB-SQL leveraging Delta and Photon optimization, you minimize manual optimization efforts and improve query performance through automatic Z-ordering and clustering.
For model building Utilizing MLFlow for model serving and feature store tables allows for smoother model deployment and evaluation, enhancing the overall machine learning lifecycle.
1 person found this helpful
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