What do you like best about the product?

- handles large datasets and complex workflows
- using apache spark for efficient processing

What do you dislike about the product?

- learning curve for new user
- 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

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.

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 .

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

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

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.

What do you like best about the product?

It seamlessly integrates with various data sources, which makes it easy to setup and use. The user-friendly interface enhances user experience.

What do you dislike about the product?

It can be overwhelming for beginners to navigate all the funtionalities.

What problems is the product solving and how is that benefiting you?

Databricks Data Intelligence Platoform analyze large datasets very efficiently. It has benefited my team by enhancing productivity and reducing time spent on data preparation.

What do you like best about the product?

Notebook UI is easy use and debug while providing single line code runs,According to my use it works well API sources as well as on premises SSMS sources,multiple source integration is provided as well as some easy to read and write code works well.Also introduction of foreign Catalog has made it easier to implement different sources on cloud

What do you dislike about the product?

Concurrent Updates doesn't work makes it a pain to update single table from multiple threads

What problems is the product solving and how is that benefiting you?

It is optimizing API Calls, file retrievals, Data reads and Data Storage of Large tables in existing on premises Databases.
Reduces Job time to perform ETL on the Data Tables.

What do you like best about the product?

I have been using databricks for a more than a year now. It integrates very well with our cloud providers and divides the work in different workspaces from Dev, Test, Pre and Production environment handlings TBs worth of data seamlessly.

What do you dislike about the product?

I think the cluster activation time could be improved. Also it is slow when it comes to fetch data from legacy systems like SQL server.
That takes up a lot of time

What problems is the product solving and how is that benefiting you?

We use databricks as our data warehouse and also as the source that is used by data analysts in the organisation. The intelligence platform helps write code seamlessly and deliver much faster compared. We have reduced the resolve time from 2 weeks to 3-4 days.

What do you like best about the product?

The Collaboration of everything on one platform - MLflow, SQL, Warehouse, Data analyst tools and data engineer tools makes learning of different roles and new verticals easy to process.

What do you dislike about the product?

AI integration can be improvised, can provide more credits for their different teir plans, should add more data visualisation support

What problems is the product solving and how is that benefiting you?

Bringing all the team on a single platform makes integration and pipelining things a lot easier, apart from support from databricks having things open-source delta and unity catalog this becomes much more versatile for us

What do you like best about the product?

Everything was excellent ,The most important thing was the user friendly

What do you dislike about the product?

Nothing ,every thing was excellent ,No other dislilke

What problems is the product solving and how is that benefiting you?

Unified Data Management
Problem: Managing diverse data types (structured, unstructured, and semi-structured) across different storage systems (data lakes, data warehouses) often leads to silos, complexity, and inefficiency.
Solution: Databricks provides a unified platform for all types of data through Delta Lake, which combines the scalability of data lakes with the performance and governance of data warehouses.
Benefit: You get a single platform to manage both batch and streaming data efficiently, reducing complexity and improving scalability. This simplifies your pipeline and reduces costs by eliminating the need for multiple tools.
2. Collaboration Between Teams
Problem: Data engineers, data scientists, and business analysts often work in silos with different tools, which slows down collaboration and innovation.
Solution: Databricks enables collaborative development with tools like Databricks Notebooks for coding, visualization, and sharing insights in real-time across teams.
Benefit: This improves communication and accelerates the development of data-driven applications, like the music recommendation system you're building, by allowing different teams to work together seamlessly.
3. Scalability and Performance
Problem: Processing large datasets can be slow and resource-intensive with traditional data platforms, leading to performance bottlenecks.
Solution: Databricks leverages Apache Spark to provide high-performance distributed data processing, enabling you to process massive datasets quickly.
Benefit: Faster data processing means quicker insights, helping you manage large data flows more effectively in real-time pipelines like the one you are working on with Databricks.
4. Data Governance and Security
Problem: As data volumes grow, ensuring data quality, compliance, and security becomes challenging, especially in industries with strict regulations.
Solution: Databricks includes comprehensive data governance features, including data lineage tracking, access controls, and auditing capabilities, all integrated within the platform.
Benefit: This makes it easier for you to manage data governance for compliance and audit needs, ensuring secure access to data and making sure your data workflows are compliant with regulations.
5. AI and ML Enablement
Problem: Building and deploying machine learning models often requires specialized tools, which can be hard to integrate with data platforms.
Solution: Databricks integrates directly with tools like MLflow for managing the full ML lifecycle, from model training to deployment.
Benefit: This allows you to integrate machine learning models into your application easily, enabling more advanced analytics and AI-driven features such as emotion-based music recommendations.
6. Real-Time Data Processing
Problem: Many organizations struggle to process and analyze real-time data effectively.
Solution: Databricks supports real-time data streaming, enabling companies to process and analyze data as it arrives.
Benefit: For real-time applications, like the music recommendation system you’re working on, this allows instant processing of data inputs (such as user emotions or age), ensuring timely and relevant recommendations.

What do you like best about the product?

he Databricks Data Intelligence Platform is highly regarded for several reasons:

Unified Data Management: It combines the best features of data lakes and data warehouses into a single platform, known as the Lakehouse. This allows for seamless management of both structured and unstructured data.

Scalability and Performance: The platform is designed to handle large-scale data processing and analytics, making it suitable for enterprises of all sizes. It offers robust scalability and high performance2.
Open Source Integration: Databricks embraces open-source technologies like Apache Spark, Delta Lake, and

What do you dislike about the product?

Cost: Some users find the pricing to be on the higher side, especially for smaller organizations or individual users.

Complexity: Despite its powerful features, the platform can be complex to set up and manage, particularly for those who are new to data engineering and analytics.

What problems is the product solving and how is that benefiting you?

Data Silos: By unifying data lakes and data warehouses into a single Lakehouse architecture, Databricks eliminates data silos. This ensures that all data, whether structured or unstructured, is accessible from one platform.

Scalability Issues: The platform is designed to handle large-scale data processing, making it suitable for enterprises of all sizes.