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Azure Data Lake Analytics

Azure Data Lake Analytics

Overview

What is Azure Data Lake Analytics?

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

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Recent Reviews

Value for Volume

8 out of 10
January 18, 2022
Incentivized
Used Azure Data Lake Analytics while working for a CPG major to store/process/analyze large volumes of data (daily cadence). Used Python …
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Awards

Products that are considered exceptional by their customers based on a variety of criteria win TrustRadius awards. Learn more about the types of TrustRadius awards to make the best purchase decision. More about TrustRadius Awards

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Pricing

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Azure Data Lake Analytics

Azure Data Lake Analytics

N/A
Unavailable

What is Azure Data Lake Analytics?

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

Entry-level set up fee?

  • No setup fee

Offerings

  • Free Trial
  • Free/Freemium Version
  • Premium Consulting/Integration Services

Would you like us to let the vendor know that you want pricing?

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Product Details

What is Azure Data Lake Analytics?

Azure Data Lake Analytics Technical Details

Operating SystemsUnspecified
Mobile ApplicationNo
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Comparisons

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Reviews and Ratings

(17)

Reviews

(1-2 of 2)
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July 26, 2022

Fast and scalable azure data lake analytics!

Verified User
Director in Product Management
Marketing & Advertising Company, 201-500 employees
Score 8 out of 10
Vetted Review
Verified User
Incentivized
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 and Cons
  • 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.
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.
Most Important Features
  • Uniqueness to run on a per job basis
  • Security and support services (professional services) are the best in the industry.
Return on Investment
  • 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.
Alternatives Considered
Compared to Databricks which we have fully implemented and all teams use, Azure Data Lake Analytics was first pushed on our engineering team from the Data Science group pretty much from familiarity. Once we did a proof of technology, we found it to natively have the better scale and performant access for users needing access to data and building data aggregations from many sources. The bonus as well as how well it plays with very large data sets, and the service integration with other Azure products makes life easy for engineers and security professionals. From a cost perspective, we found and I'm sure you will as well that our enterprise pricing made it very competitive compared to competitors.
Other Software Used
Databricks Lakehouse Platform (Unified Analytics Platform), Confluent Platform, Azure Bot Service (Microsoft Bot Framework), Azure Blob Storage, Pypestream, Kore.ai
December 08, 2021

Aggregate Data Lake Data

Verified User
Engineer in Engineering
Information Technology & Services Company, 51-200 employees
Score 8 out of 10
Vetted Review
Verified User
Incentivized
Use Cases and Deployment Scope
We utilize this solution for reporting on our storage usage.
Pros and Cons
  • Reporting
  • Data Aggregation
  • Trends
  • 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.
Most Important Features
  • Scalability
  • Data Aggregation
  • Azure offered product
Return on Investment
  • Better understanding of data lake data
  • Aggregation of data
Alternatives Considered
Both of the products selected are very good at what they do, but data lake analytics is able to bundle everything else within our preexisting data lake, which is a very big [deciding] factor.
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