Name: Azure Databricks
Rating: 8.6 (46 reviews)
Author: Microsoft

Communications Outsourcing

Help Desk

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Customer Support

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Collaboration

Embedded Business Intelligence (BI)

Environmental Health and Safety (EHS)

Org Chart

Procurement

Enterprise

Accounting

Crypto Asset Management

Merger and Acquisition

Subscription Management

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Applicant Tracking

Corporate Learning Management

Employee Recognition

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HR Management

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Recruitment Agency

Talent Intelligence

Talent Management

Workforce Analytics

Workforce Management

Human Resources

Browser

Cloud Storage

Consulting and Advisory Services

Data Center Infrastructure Management

Data Center Virtualization

Data Observability

Data Pipeline

Distributed Denial of Service (DDoS) Protection

Enterprise Architecture Management

Extended Detection and Response (XDR)

Fraud Detection

IT Infrastructure Monitoring

Integration Platform as a Service (iPaaS)

Internet of Things (IoT)

Network Monitoring

Network Performance Monitoring

Remote Monitoring and Management

Software Defined Networking (SDN)

Tower Servers

Vulnerability Management

Information Technology

A/B Testing

Ad Serving & Retargeting

All-in-One Marketing

Audience Engagement

Contact Management

Content Management

Email Marketing

Heatmap and Session Recording

Landing Page

Marketing Automation

Social Media Management

Survey & Forms Building

Web Analytics

Marketing

Project Management

Professional Services

Customer Relationship Management (CRM)

Lead Generation Services

Product Configuration

Quote to Cash

Sales Coaching

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Sales Intelligence

Sales

Auto Repair

Construction Workforce Management

Emergency Notification

Oil and Gas

Public Sector Software

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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).

RapidMiner is a data science and data mining platform, from Altair since the late 2022 acquisition. RapidMiner offers full automation for non-coding domain experts, an integrated JupyterLab environment for seasoned data scientists, and a visual drag-and-drop designer.  RapidMiner’s project-based framework helps to ensure that others can build off their work using visual workflows or automated data science.

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Free

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Azure Databricks

Data Science

Likelihood to Recommend

Usability

Platform Connectivity

Data Exploration

Data Preparation

Platform Data Modeling

Model Deployment

Connect to Multiple Data Sources

Extend Existing Data Sources

Automatic Data Format Detection

MDM Integration

Visualization

Interactive Data Analysis

Interactive Data Cleaning and Enrichment

Data Transformations

Data Encryption

Built-in Processors

Multiple Model Development Languages and Tools

Automated Machine Learning

Single platform for multiple model development

Self-Service Model Delivery

Flexible Model Publishing Options

Security, Governance, and Cost Controls

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Ability to connect to a wide variety of data sources

Ability to connect to a wide variety of data sources including data lakes or data warehouses for data ingestion

Use R or Python to create custom connectors for any APIs or databases

Automatic detection of data formats and schemas

Integration with MDM and metadata dictionaries

Ability to explore data and develop insights

The product’s support and tooling for analysis and visualization of data.

Ability to analyze data interactively using Python or R Notebooks

Access to visual processors for data wrangling

Use visual tools for standard transformations

Library of processors for data quality checks

Access to multiple popular languages, tools, and packages such as R, Python, SAS, Jupyter, RStudio, etc.

Tools to help automate algorithm development

Single place to build, validate, deliver, and monitor many different models

Multiple model delivery modes to comply with existing workflows

Tools for deploying models into production

Publish models as REST APIs, hosted interactive web apps or as scheduled jobs for generating reports or running ETL tasks.

Built-in controls to mitigate compliance and audit risk with user activity tracking

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Jupyter Notebook

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Azure Databricks 2024-09-29 10:30:55

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Microsoft Excel

Azure Databricks 2023-12-15 09:13:39

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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.

The new UI is a bit clunky compared to the old UI. It also adds new elements in the sidebar which are not relevant to the workspace. Can be worked upon
Delta Live Tables, although powerful, has a lot of things that can be improved, including error debugging, support for new things
Concurrent requests need some more optimisation and work in the delta lake tables.

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

Jupyter Notebook, Azure Synapse Analytics and Cloudera Data Platform

Azure Data Factory, Cloudera Data Platform, Apache Iceberg

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

Their pipeline workflow orchestration is pretty primitive. Lacks some common features
Workspace UI and navigation requires steep learning curve
Personally, I am not fond of their autosave feature. Its dangerous for production level notebooks scripts

Azure Data Factory, Azure Synapse Analytics, Azure Data Lake Storage

Associate Data Engineer

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.

Intuitive interface
Ease of use
Providing FAQ or QRGs

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

Tableau Server, Tableau Cloud, Microsoft Excel

Azure Databricks

Overview

What is Azure Databricks?