I am working as a data engineer at Fractal. On a daily basis, I work on Azure Cloud, and I use Databricks frequently. We have EDF pipelines and utilize Synapse for our daily tasks.
Databricks Data Intelligence Platform
Databricks, Inc.External reviews
640 reviews
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Simplify big data challenges for better decision-making
What do you like best about the product?
Recommendation engine for an e-commerce platform was developed by our team with the help of DataBricks. The project involved analysing customer behaviour to suggest products on the website. For this project we are required to process bulk data without any performance issues. That could only be possible with DataBricks as the platform is scalable. We also integrated DataBricks with AWS S3 to access data on cloud.
What do you dislike about the product?
Initially, we faced some challenges as the platform has a learning curve, but when we encountered any challenges, we connect with their customer support team and they provided a detailed guidance on every issues that we had.
What problems is the product solving and how is that benefiting you?
We have multiple sources of data and Databricksh has greatly improved our efficiency by combining all the sources of data into single platform. This has eliminated the need to switch between different tools and saving us hours of work each time.
Best platform for data engineering and data science
What do you like best about the product?
We used Databricks for its features such asreal time data processing and dat exploration tools for visualizing data.AutoML and Mlflow is one of the best AI integration in this platform.This software is cost efficient
What do you dislike about the product?
Limited tutorials for new users , not beginner freindly interface
What problems is the product solving and how is that benefiting you?
We used this platform analyzing and processing big data and process data from various formats, this tool is really great
Databricks - best integration tool
What do you like best about the product?
Databricks data intelligence platform make integration of data engineering, data science, and machine learning into a single environment simplify workflow. Users can easily share data and models in same platform.
Databricks optimize for cloud environment, this flexibility allows organisation to choose their preferred cloud provider.
Databricks has a large and active user community and ecosystem include a wealth of share knowledge resources and third party integration.
Databricks optimize for cloud environment, this flexibility allows organisation to choose their preferred cloud provider.
Databricks has a large and active user community and ecosystem include a wealth of share knowledge resources and third party integration.
What do you dislike about the product?
I have been using this software from while but didn't find any dislike in it.
What problems is the product solving and how is that benefiting you?
Databricks support integration with wide range of data source, they allow users id easily ingest, process,and analysis data from disparate system.
Exceptional performance for end to end data management
What do you like best about the product?
I used Databricks to optimise customer segmentation strategy for a retail campaign. It helped me to analyse millions of records, clean the data and create the ML model based on purchasing behavior. The Delta Lake technology ensured data consistency during the process. Its ability to integrate with our Azure data lake made is easy to access datasets.
What do you dislike about the product?
Tableau integration with Databricks was challenging and I encountered issues while setting up real-time data visualisation. Despite the challenges, the platform enabled me to automate data pipelines, which saved me hours.
What problems is the product solving and how is that benefiting you?
Our operations team used Databricks to monitor and optimse supply chain performance. It has become an essential tool for us to enhance both individual productivity and team collaboration. Its impact can be felt acoss multiple projects.
Capability to integrate diverse coding languages in a single notebook greatly enhances workflow
What is our primary use case?
What is most valuable?
Databricks offers various courses that I can use, whether it's PySpark, Scala, or R. I can leverage all these courses in a single notebook, which is beneficial for clients as they can access various tools in one place whenever needed. This is quite significant.
I usually work with PySpark based on client requirements. After coding, I feed the Databricks notebooks into the ADF pipeline for updates. Databricks' capability to process data in parallel enhances data processing speed. Furthermore, I can connect our Databricks notebook directly with Power BI and other visualization tools like Qlik. Once we develop code, it allows us to transform raw data into visualizations for clients using analysis diagrams, which is very helpful.
What needs improvement?
As a data engineer, I see cluster failure in our Databricks user databases as a major issue. I am unsure why, however, our flow, typically involving three to four notebooks, sometimes leads to cluster failure. Despite attempts to identify the problem, there are times when the reason remains unclear. Adjusting features like worker nodes and node utilization during cluster creation could mitigate these failures.
For how long have I used the solution?
I have been using the solution for three years now.
What do I think about the stability of the solution?
Cluster failure is one of the biggest weaknesses I notice in our Databricks.
Which solution did I use previously and why did I switch?
Databricks is beneficial for cost-saving since clients I work for transitioned from AWS Cloud to Azure Cloud for this reason.
How was the initial setup?
The initial setup is very straightforward for us.
What's my experience with pricing, setup cost, and licensing?
I am not very aware of the pricing. We use three to four clusters in our project. Increasing the number or size of clusters, such as adding more workers, would result in higher costs. That's why we limit ourselves to four clusters for our business.
Which other solutions did I evaluate?
In terms of cost efficiency, it's very useful because our clients switched from AWS Cloud to Azure Databricks to save costs.
What other advice do I have?
I would rate the overall product eight out of ten.
Everything is probably good as far as I have used it, but there's room for improvement in cluster integration. Enhancing cluster capabilities while keeping costs lower would be beneficial.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Microsoft Azure
The Best Data Engineering Tool uses Delta Lake
What do you like best about the product?
This tool is very efficient because it using Delta lake. This supports ETL Pipelines and Machine Learning workflows which Guide to extract and transform data into Various forms. And i like the interactive notebooks supporting python language .
AutoML and Delta Lake is best features.
AutoML and Delta Lake is best features.
What do you dislike about the product?
This tool in begining there is complexity for using now it became simople.
What problems is the product solving and how is that benefiting you?
the problems solved this tool , hectic data analysis and processing many type of datas
The gold standard for scalable ML and Analytics
What do you like best about the product?
My team recently used Databricks to implement a machine learning model for fraud detection. We used the Delta Lake for data preprocessing and insured real time updates from our database. One of the most helpful features in Databricks is the Delta Lake functionality, which ensures data consistency. The platform supports both Python and SQL, which fills the cap between Data engineers and Analysts. This makes it easy for teams to collaborate. Customer support is another highlight as they respond quickly and provide clear guidance.
What do you dislike about the product?
While integrating Databricks with our existing Azure Data Lake, we faced issues syncing access permissions for multiple datasets. Additionally, their pricing models makes it better suited for large organisations, but for smaller teams scaling up can be expensive.
What problems is the product solving and how is that benefiting you?
In recent projects our sales and operation teams needed unified view of supply chain metrics. Using Databricks, we collected data from multiple sources and created a centralised dashboard and enabled real time reporting. This improved our decision making speeed and helped us prevent bottlenecks.
The best Bigdata Processing Tool
What do you like best about the product?
I have used this tool for past two years , the attractive feature were faster data processing and data warehousing. i can easily intergrate it with power bi so it become easy to implement it
What do you dislike about the product?
I dont like the interface of this tool , and also latency issues
What problems is the product solving and how is that benefiting you?
My main problem was processing data from clients and upload the processed data to cloud by using this , this task became very easy
The go-to platform for scalable data analysis and AI.
What do you like best about the product?
Databricks provides excellent tools for data engineering, machine learning and business analytics. The interactive notebooks makes exploring datasets straight forward, with support for multiple languages like python, SQL and Scala. We used Databricks to create a centralised data pipeline for customer sentiment analysis. With its ability to handle streaming data, we integrated Twitter feeds, customer reviews and support tickets into single databset.
What do you dislike about the product?
We faced difficulties while integrating Databricks with a on-premises database due to limited support for hybrid environment. This required building a custom connector, with took additional time.
What problems is the product solving and how is that benefiting you?
Our data science team used Databricks to build a recommendation engine for our e-commerce client. Which was only possible because of Databrick's ability to process large datasets efficiently and provide insights faster. Also, collaborative notebooks enabled team members to debug issues and refine the algorithm together.
Revolutionizing Data analytics and AI integration
What do you like best about the product?
MLflow , and coloborative notebooks are the main feature of this tool and anothere features i like about his is Data Lake Storage layer nd Auto ml model traing helps for efficient processing.
What do you dislike about the product?
I dont like the SQlanalytics feature , gives error most of time , better improving this .
What problems is the product solving and how is that benefiting you?
We using this tool for Data Warehousing and Dataprocessing in a bulk , by using this tool we can improve time efficently
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