Name: Azure Data Lake Analytics
Rating: 5.7 (20 reviews)
Author: Microsoft

Help Desk

Web and Video Conferencing

Customer Support

Business Intelligence (BI)

Collaboration

Data Discovery and Visualization

Document Management

Enhanced 911 (E911)

File Sharing

Risk Management

Statistical Analysis

Enterprise

Accounting

Invoicing

Subscription Management

Finance and Accounting

Applicant Tracking

Corporate Learning Management

Diversity, Equity, and Inclusion (DEI)

HR Management

Payroll

Recruitment Agency

Recruitment Automation

Talent Intelligence

Talent Management

Workforce Analytics

Workforce Management

Human Resources

Cloud Storage

Data Lakehouse

Data Mapping

Firewall Security Management

Fraud Detection

IP Address Management (IPAM)

In-Memory Databases

Incident Response

Infrastructure-as-Code (IaC)

Integration Platform as a Service (iPaaS)

Log Management

Managed IT Service

Network Performance Monitoring

Password Management System

Release Management

SOA Application Gateways

Web Portal

WiFi Hotspot

Information Technology

A/B Testing

Ad Serving & Retargeting

All-in-One Marketing

Appointment Scheduling

Audio Editing

Content Management

Customer Reference Management

Email Marketing

Email Testing

Marketing Automation

Product Sampling

Retail Media Advertising

Social Media Management

Social Media Marketing

Survey & Forms Building

Web Analytics

Marketing

Field Service Management

Project Management

Professional Services

Customer Relationship Management (CRM)

Lead Generation Services

Lead-to-Account Matching and Routing

Multichannel Retail

Sales Coaching

Sales Gamification

Social Commerce

Sales

Architecture

InsurTech

Laboratory Information Management

Legal Billing

OSS/BSS (Operations Support/Business Support)

Oil and Gas

Real Estate

Vertical-Specific

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Data Capabilities: Users have appreciated the platform's combination of big data capabilities, providing them with robust tools for analysis and decision-making. This feature has allowed users to handle large datasets efficiently and derive valuable insights for their businesses.

Monitoring and Alert Functionalities: Reviewers highlighted the helpfulness of the monitoring and alert functionalities within the platform. These features have enabled users to proactively track performance metrics, identify anomalies, and receive timely notifications for prompt action.

Report Visualization: The report visualization dependent on analytics has been considered valuable by users. Through this functionality, users can create visually appealing reports that effectively communicate complex data patterns and trends to stakeholders.

Inefficient Data Pipeline Management: Some users have expressed dissatisfaction with the inefficiency in managing and monitoring the data pipeline within AWS Glue, leading to potential delays and operational issues for organizations relying on smooth data processing workflows.

Lack of Streaming and Event Processing Workloads: Users have mentioned that the platform lacks adequate support for streaming and event processing workloads, limiting its capabilities for real-time data analysis and responsiveness to dynamic data streams.

Limited 24/7 Support Availability: Concerns have been raised by users regarding the absence of round-the-clock support for AWS Glue, potentially impacting timely assistance during critical situations or unexpected technical challenges.

Microsoft's Azure Data Lake Analytics is a BI service for processing big data jobs without consideration for infrastructure.

WinForms Edition

WPF Edition

ASP.NET Edition

VCL Edition (Delphi, C++Builder)

.NET All-in-One Edition

Core .NET Edition (non-visual)

ActiveX Edition

Java Edition

Active Query Builder

Azure Data Lake Analytics

Data Analytics  and BI Services

Likelihood to Recommend

Do more with less. At Microsoft we empower people and businesses throughout the world realize their full potential and achieve more. We make this simple mission come to life every day through our passion to create technologies and develop products that touch just about every kind of customer. With Microsoft for Nonprofit, we offer industry leading products as standalone solutions, but they're even better when used together. Our modular and extensible cloud platform allows your organization to start with your needs and adapt as you grow. These solutions provide foundational systems ranging from security and business applications to modern work tools and data and AI integration. Support you donors, volunteers, and constituents with tools and applications that help improve fundraising, engagement, training, data intelligence and much more. With Microsoft for Nonprofit we're here to help your organization expand beyond the boundaries and do more with less.

Splunk Enterprise

Databricks Data Intelligence Platform

Unified

Microsoft Power BI

Confluent

Sr. Data Analyst

Azure Data Lake Analytics 2022-07-26 05:19:16

Apache Spark

a3doc cloud

Accountancy Cloud

Accio Data

Security Operation Center Analyst

Azure Data Lake Analytics 2022-07-25 12:15:37

Denodo

Kore.ai

Azure AI Bot Service

Azure Blob Storage

Pypestream

Azure Data Lake Analytics 2022-07-25 11:53:20

Amazon DynamoDB

Amazon EMR (Elastic MapReduce)

Amazon QuickSight

AWS Lambda

Amazon Kinesis

AWS Backup

Alibaba Cloud Data Lake Analytics

Amazon Elasticsearch Service

AWS CloudFormation

Azure Data Lake Analytics 2022-07-01 02:28:51

SAS Enterprise Guide

Azure Data Lake Storage

Azure Data Lake Analytics 2022-01-17 18:42:32

TrustRadius is a technology and business research firm based in Austin, Texas. The company is known as a review platform for verified B2B technology and software reviews through a proprietary algorithm and human verification.

TrustRadius

Needs improvements

Training materials

Amount of data

Available online

Slows down

Multi cloud

Difficult to learn

Time consuming

Visual studio

Pricing model

Data storage

Data governance

Easy data

Scale up

Data aggregation

Data visualization

Stores data

No code

Big data

Effective and efficient

Home

To make our work more accessible and more efficient, Azure Data Lake has provided fast access to and analysis of data. This product solves our need for quick reporting on cross-platform applications and bulk data from partners. We can manage on-premises access and roles because the analytics service integrates with Azure Active Directory. There are no clusters, virtual machines, or servers to manage, maintain, or fine-tune—the utility of a highly adaptable, Azure Blob Storage-based information lake that is also secure. Azure Data Lake Analytics' simple interface makes it a reliable and easy-to-use program for beginners. SQL benefits are combined with user code flexibility through the inclusion of U-SQL. Scalable distributed runtime for U-SQL allows us to analyze data across SQL Servers in Azure (SQL database and data warehouse) in a streamlined manner.

It combines big data.
Monitors and alerts are helpful.
Report visualization relies on analytics.
It is compatible with Power BI services for report generation.

The data pipeline is managed and monitored inefficiently.
Streaming and event processing workloads are lacking.
It's memory-intensive but useful for networking data and cloud storage.

Easy to store for in-depth data analysis.
User-friendly and straightforward rest API.
SQL and C# scripting combined to make it easy to use.

It lets us manage and scan data, making our work easy and efficient.
It helped me manage real-time data, process it, and send it to reporting.
Data centralization or data warehousing projects are being implemented with its help.

Databricks Lakehouse Platform (Unified Analytics Platform)

Splunk Enterprise, Microsoft Power BI, Google BigQuery, Confluent Platform

We have Azure Storage Blobs, which is traditionally one of the many ways that we would and probably more often and not the default way where we would store data. We make our data workforce by putting in Azure Storage Blobs We use the Azure SQL Database, a traditional SQL-based database. Microsoft makes that available to us in the Azure platform and we can host our data there. We also have a SQL Database, running an Azure on a Virtual Machine, if we don't want to use the Azure base SQL DB directly.

Allows us to take in data, unstructured or structured
Good documentation
SaaS

AWS Glue could be more effective.
There is no 24/7 support.
Documentation is not available online.

Of course price
Documentation is not available online.
Slow progress.

a3doc cloud, Accountancy Cloud, Accio Data

My primary use case in using and investigating Azure Data Lake Analytics was in comparing how it fulfilled aggregate build in our data lake environment compared to how Databricks solved for our initial use cases. At the time, in building out a raw, refined, and curated zone before landing data in a warehouse multiple bidirectional transformation processes run between the Refined to Curated and then ultimately Warehouse layer. Key was scale, cost, and performance as compared to what can be done in processing aggregates via Databricks and opposite that ELT to a warehouse like Snowflake instead of load from lake to Microsoft Synapse.

Process large data transformation jobs using pretty much any language needed.
Native integration with Azure storage.
Top notch security that fulfills all audit needs.
Easy to consolidate enterprise data under one location - Single source of truth.

Learning curve and professional services were the only reason why we got up and running quickly - Not a downside but a need to know.

Uniqueness to run on a per job basis
Security and support services (professional services) are the best in the industry.

Has allowed us to reduce compute expenses by enabling better synchronization of workloads and user usage.
Ease of data virtualization or rather connection of data sources from multiple locations.

Databricks Lakehouse Platform (Unified Analytics Platform) and Denodo

Databricks Lakehouse Platform (Unified  Analytics Platform), Confluent Platform, Azure Bot Service (Microsoft Bot Framework), Azure Blob Storage, Pypestream, Kore.ai

We have been using Azure Data Lake Analytics as one of our data lakes, we are collecting data from many different sources, storing it on the data lake, and processing this data. As result, we have Business Intelligence tools connected to this result which we use to present some KPIs.

Sometimes requires previous experience in cloud.

Since we have implemented this solution, we have been more able to follow what is going on in our process and sells.
We are also sparing some money by comparing the costs now against the costs we had on-premise.

AWS Backup, AWS CloudFormation, Amazon Kinesis, Amazon Athena, Amazon QuickSight, Amazon Elasticsearch Service, Matillion, Amazon DynamoDB, Amazon EMR (Elastic MapReduce), AWS Lambda

Used Azure Data Lake Analytics while working for a CPG major to store/process/analyze large volumes of data (daily cadence). Used Python as a programming language for processing the stored data. Also, with fluctuating data volume across weekdays/weekends, ADL analytics was helpful in processing data on demand, and scale instantly, thereby enabling us to pay for the services used/rendered.

Effective and efficient data storage
pretty fast querying ability
Incredibly scalable (need based usage and billing)

There's a bit of bias towards cloud with ADL Analytics. Depending upon a company's infra strategy and investment plans, there are some challenges with migration and integeration.
Not worth the time/effort/money if the organization doesn't have "Volume" of data. Cost effective only when daily loads exceed around 1million.
While training materials are available online, Adoption rate - Yet to pick up.

Azure Data Lake Analytics

Overview

What is Azure Data Lake Analytics?