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

3 Reviews

Mid-sized Companies (51-1,000 employees)

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 (Marketing & Advertising company, 201-500 employees)

Vetted Review

1 year 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 (Management Consulting company, 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 (Information Technology & Services company, 51-200 employees)

Vetted Review

1 year of experience

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