" Azure Data Lake Analytics is accurate, fast, and reliable."
To make our work more accessible and more efficient, Azure Data Lake has provided fast access to and analysis of data. This product solves …
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.
Recent Reviews
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
Pricing
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?
Alternatives Pricing
What is Klipfolio?
Klipfolio PowerMetrics is a metric-centric business intelligence platform. Data teams are able to bring all of their data together and create a catalog of verified metrics that end users are able to explore and visualize using self-serve dashboards and reports.
What is Active Query Builder?
Active Query Builder is a component for business applications which helps users without any SQL experience to work with SQL queries and get data fast. Users can get a clear view of database schema and design SQL queries with natural point-and-click actions rather than tedious typing. Active Query…
Product Details
- About
- Tech Details
What is Azure Data Lake Analytics?
Azure Data Lake Analytics Technical Details
| Operating Systems | Unspecified |
|---|---|
| Mobile Application | No |
Comparisons
Compare with
Reviews and Ratings
(17)Reviews
(1-5 of 5)Companies can't remove reviews or game the system. Here's why
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)
Azure Data Lake simplifies extensive data analysis. It runs Hadoop, HDInsight, and Data Lakes, and even complex queries run smoothly and quickly. We write queries to transform data and extract insights instead of configuring hardware. It can handle any size job by adjusting the power. Azure's servers, networking, and data entry are fantastic. It provides security and assured data access.
July 26, 2022
Fast and scalable azure data lake analytics!
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.
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.
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.
- Easy usage
- Effective data storage
- Report
- Of course price
- Documentation is not available online.
- Slow progress.
July 03, 2022
Good choice regarding features
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.
- Easy usage
- Interface
- Connectivity
- Sometimes requires previous experience in cloud.
- Connectivity
- All tools centralized
- 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.
We did some research about Alibaba Cloud Data Lake Analytics and even being cheaper than Azure Data Lake Analytics, we decided to go for the second one once we noticed they have more features and better documentation. Another thing we considered during this process was the fact that we have more people that already have Azure Cloud knowledge.
January 18, 2022
Value for Volume
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!