Value for Volume
January 18, 2022

Value for Volume

Anonymous | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User

Overall Satisfaction with Azure Data Lake Analytics

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.
  • Ability to store data in its native format (Unstructured, semi-structured, images, online reviews)
  • Scalable and flexible - according to data loads
  • Cheaper storage option
  • Yet to realize its full potential - Owing to skill shortage in the org
  • Adoption across organization a challenge
ADL Analytics supports big data such as Hadoop, HDInsight, Data lakes. Usually, a traditional data warehouse stores data from various data sources, transform data into a single format and analyze for decision making. Developers use complex queries that might take longer hours for data retrieval. With Data Lake Analytics, the processing is so smooth and fast that -- complex queries run within minutes, much to our surprise. Pretty amazing!

Do you think Azure Data Lake Analytics delivers good value for the price?


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Did Azure Data Lake Analytics live up to sales and marketing promises?

I wasn't involved with the selection/purchase process

Did implementation of Azure Data Lake Analytics go as expected?


Would you buy Azure Data Lake Analytics again?


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