Azure Data Lake Analytics Reviews & Insights

Score8.3 out of 10

17 Reviews and Ratings

Community insights

TrustRadius Insights for Azure Data Lake Analytics are summaries of user sentiment data from TrustRadius reviews and, when necessary, third party data sources.

Pros

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.

Azure Data Lake Analytics Reviews

6 Reviews

" Azure Data Lake Analytics is accurate, fast, and reliable."

Rating: 9 out of 10

Incentivized

July 26, 2022

Use Cases and Deployment Scope

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.

Pros

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

Cons

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.

Likelihood to Recommend

Azure Data Lake Analytics services are beneficial when working with a lot of data. It can process enormous amounts of data extremely quickly. Service is secure and easy to set up, build, scale, and run on Azure. Regarding big data analytics and reporting, parallel processing has a significant impact. It consolidated our analytics from multiple systems and increased our analysis productivity. This tool has excellent support for reporting tools like Power BI and is very quick when performing analytics.

Sam Joseph Gomez

Sr. Data Analyst in Information Technology at Cramstack Ltd (11-50 employees)

Vetted Review

1 year of experience

View profile

Fast and scalable azure data lake analytics!

Rating: 8 out of 10

Incentivized

July 26, 2022

Use Cases and Deployment Scope

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.

Pros

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.

Cons

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

Likelihood to Recommend

For us we have an enterprise of SQL users at all skill levels, and this product is very SQL friendly and extremely fast in creation of data aggregates and analysis. If you are an Azure storage user, considering using Lake Analytics over top of your blob or any other storage just adds complementary services and functions native to your existing architecture.

Verified User

Director in Product Management (201-500 employees)

Vetted Review

1 year of experience

Playing with data have never been easy with Azure Data Lake Analytic

Rating: 9 out of 10

Incentivized

July 26, 2022

Use Cases and Deployment Scope

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.

Pros

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

Cons

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

Likelihood to Recommend

U-SQL is the language used by Azure Data Lake Analytics for query and processing. The SQL and C# computer languages are combined to create the U-SQL language. The U-SQL language is easily learned by SQL Server database specialists.

Ceyhun Haqverdiyev

Security Operation Center Analyst in Information Technology at Cyberpoint (201-500 employees)

Vetted Review

1 year of experience

View profile

Good choice regarding features

Rating: 9 out of 10

Incentivized

July 3, 2022

Use Cases and Deployment Scope

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.

Pros

Easy usage
Interface
Connectivity

Cons

Sometimes requires previous experience in cloud.

Likelihood to Recommend

Azure Data Lake Analytics is a perfect fit for those who are needing to have a data lake where you have some tools to process and visualize the data. I would say it's a smart choice for companies going to the cloud due to the fact of the quick learning and easy implementation.

Verified User

Analyst in Information Technology (10,001+ employees)

Vetted Review

3 years of experience

Value for Volume

Rating: 8 out of 10

Incentivized

January 18, 2022

Use Cases and Deployment Scope

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.

Pros

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

Cons

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.

Likelihood to Recommend

Azure Data Lake Analytics is best suited for -
1) Storing raw data ( original data format)
2) You can store Unstructured, semi-structured and structured in it
3) Data lake follows schema on the reading method in which data is transformed as per requirement basis

Not the best scenario when -

1) Data volume isn't great
2) Latency, and querying speed isn't the most important criteria

Verified User

Program Manager in Information Technology (501-1000 employees)

Vetted Review

2 years of experience

Aggregate Data Lake Data

Rating: 8 out of 10

Incentivized

December 8, 2021

Use Cases and Deployment Scope

We utilize this solution for reporting on our storage usage.

Pros

Reporting
Data Aggregation
Trends

Cons

Pricing model, I understand why it is per jib but our junior engineers make mistakes.

Likelihood to Recommend

It is great for analyzing large workloads and large amounts of data, but I think that there needs to be a certain amount of data even present, to begin with, to make the additional costing worthwhile.

Verified User

Engineer in Engineering (51-200 employees)

Vetted Review

1 year of experience

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