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Azure Databricks

Azure Databricks

Overview

What is Azure Databricks?

Azure Databricks is a service available on Microsoft's Azure platform and suite of products. It provides the latest versions of Apache Spark so users can integrate with open source libraries, or spin up clusters and build in a fully managed…

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What is Azure Databricks?

Azure Databricks is a service available on Microsoft's Azure platform and suite of products. It provides the latest versions of Apache Spark so users can integrate with open source libraries, or spin up clusters and build in a fully managed Apache Spark environment with the global scale and…

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Product Details

What is Azure Databricks?

Azure Databricks is a service available on Microsoft's Azure platform and suite of products. It provides the latest versions of Apache Spark so users can integrate with open source libraries, or spin up clusters and build in a fully managed Apache Spark environment with the global scale and availability of Azure. Clusters are set up, configured, and fine-tuned to ensure reliability and performance without the need for monitoring. The solution includes autoscaling and auto-termination to improve total cost of ownership (TCO).

It is presented as an open and unified platform to run all types of analytics workloads, whether as a data scientist, data engineer, or a business analyst. Choice of language can include Python, Scala, R, and SQL. It provides version control of notebooks with GitHub and Azure DevOps.

It provides advanced automated machine learning capabilities using the integrated Azure Machine Learning to identify suitable algorithms and hyperparameters. The solution helps to simplify management, monitoring, and updating of machine learning models deployed from the cloud to the edge. Azure Machine Learning also provides a central registry for experiments, machine learning pipelines, and models.

Azure Databricks Technical Details

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Mobile ApplicationNo
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Reviews and Ratings

(29)

Reviews

(1-3 of 3)

Azure Databricks: A Data Consultant's Dream

Rating: 10 out of 10
October 07, 2024
Verified User
Vetted Review
Verified User
Azure Databricks
3 years of experience
As a Big Data Consultant. Azure Databricks is my favorite tool in the house!
The biggest problems with data consulting is a plethora of programming languages it deals in, from SQL, Scala,R, Python, Java etc.
That is exactly where Azure Databricks excels! It supports all languages in a single notebook with an equivalent performance for all! Club that with a visually pleasing UI, features that integrate the entire data lifecycle, and an architecture that gets the best of spark and you have one of the best data tools in your hand!
  • Data Processing and Transformations based on Spark
  • Delta Lakehouse when clubbed with an external cloud storage
  • Governance using Unity Catalog to unify IAM
  • Delta Live Tables is a product, which although relatively newer, has a great potential with the visuals of a pipeline.
Suppose you have multiple data sources and you want to bring the data into one place, transform it and make it into a data model. Azure Databricks is a perfectly suited solution for this. Leverage spark JDBC or any external cloud based tool (ADG, AWS Glue) to bring the data into a cloud storage. From there, Azure Databricks can handle everything. The data can be ingested by Azure Databricks into a 3 Layer architecture based on the delta lake tables. The first layer, raw layer, has the raw as is data from source. The enrich layer, acts as the cleaning and filtering layer to clean the data at an individual table level. The gold layer, is the final layer responsible for a data model. This acts as the serving layer for BI For BI needs, if you need simple dashboards, you can leverage Azure Databricks BI to create them with a simple click! For complex dashboards, just like any sql db, you can hook it with a simple JDBC string to any external BI tool.
Platform Connectivity (4)
80%
8.0
Connect to Multiple Data Sources
60%
6.0
Extend Existing Data Sources
90%
9.0
Automatic Data Format Detection
90%
9.0
MDM Integration
80%
8.0
Data Exploration (2)
65%
6.5
Visualization
60%
6.0
Interactive Data Analysis
70%
7.0
Data Preparation (4)
80%
8.0
Interactive Data Cleaning and Enrichment
70%
7.0
Data Transformations
90%
9.0
Data Encryption
90%
9.0
Built-in Processors
70%
7.0
Platform Data Modeling (4)
82.5%
8.3
Multiple Model Development Languages and Tools
80%
8.0
Automated Machine Learning
90%
9.0
Single platform for multiple model development
80%
8.0
Self-Service Model Delivery
80%
8.0
Model Deployment (2)
85%
8.5
Flexible Model Publishing Options
80%
8.0
Security, Governance, and Cost Controls
90%
9.0
  • The support team is amazing, they help you at every stage of the projects, from sales to delivery.
  • On a framework level, it has had an amazing impact and has reduced the clients overall data platform costs by a staggering 65%
  • There has been a 40% Manual work requirement on average for the clients when they move to Azure Databricks Data Platform
Based on my extensive use of Azure Databricks for the past 3.5 years, it has evolved into a beautiful amalgamation of all the data domains and needs. From a data analyst, to a data engineer, to a data scientist, it jas got them all!
Being language agnostic and focused on easy to use UI based control, it is a dream to use for every Data related personnel across all experience levels!
Against all the tools I have used, Azure Databricks is by far the most superior of them all! Why, you ask? The UI is modern, the features are never ending and they keep adding new features. And to quote Apple, "It just works!"
Far ahead of the competition, the delta lakehouse platform also fares better than it counterparts of Iceberg implementation or a loosely bound Delta Lake implementation of Synapse

Azure Databricks ! Best of cloud and Big data

Rating: 8 out of 10
January 26, 2024
PG
Vetted Review
Verified User
Azure Databricks
2 years of experience
We are leveraging Databricks capabilities in various use cases. For instance, to design a tailor made change data capture that keep track of users account details and keep it updated in delta lake. We have also designed numerous ETL processes which is scheduled to provide data to data analytics on strict delivery timelines. Moreover, the workspaces is integrated with other Azure services such as Azure Synapse Analytics, Azure data lake, Azure Data Factory. Some of our Databricks are triggered by Azure Data Factory.
  • Consistently great performance when dealing with huge scale data with the help of spark architecture
  • Magic commands such as spark sql, pyspark, scala . This comes really handy in day to day work
  • Integration with other Azure services is super smooth and robust
It works great for use cases where you want to have a more customized solution able to handle huge data volumes ( cluster nodes power and spark). Also, if you want to migrate native spark solution to cloud. Or if you want to integrate your existing Azure data services together

Our new go-to tool for managing large databases and tables!

Rating: 9 out of 10
December 15, 2023
Verified User
Vetted Review
Verified User
Azure Databricks
1 year of experience
We use Databricks to pull performance metrics for our content hosted on the company website. Having one tool to view and analyze the data has been a game changer for us, saving many hours of collecting the data various sources in the past.
  • SQL
  • Data management
  • Data access
Having access to all databases and tables in one place is what has helped me and my team to function better. The in built functionality/access to SQL and Python is definitely an added bonus! The icing on the cake is the ability to export your data into an Excel spreadsheet for additional analysis. If you have less to no working knowledge of SQL or Python, its better to look at alternatives.
Platform Connectivity (4)
72.5%
7.3
Connect to Multiple Data Sources
100%
10.0
Extend Existing Data Sources
90%
9.0
Automatic Data Format Detection
100%
10.0
MDM Integration
N/A
N/A
Data Exploration (2)
40%
4.0
Visualization
40%
4.0
Interactive Data Analysis
40%
4.0
Data Preparation (4)
85%
8.5
Interactive Data Cleaning and Enrichment
70%
7.0
Data Transformations
80%
8.0
Data Encryption
100%
10.0
Built-in Processors
90%
9.0
Platform Data Modeling (4)
90%
9.0
Multiple Model Development Languages and Tools
100%
10.0
Automated Machine Learning
80%
8.0
Single platform for multiple model development
90%
9.0
Self-Service Model Delivery
90%
9.0
Model Deployment (2)
90%
9.0
Flexible Model Publishing Options
80%
8.0
Security, Governance, and Cost Controls
100%
10.0
  • Helped reduce time for collecting data
  • Reduced cost in maintaining multiple data sources
  • Access for multiple users and management of users/data in a single platform
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