Azure Data Lake Analytics Reviews and Ratings

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

Use Cases and Deployment Scope

We utilize this solution for reporting on our storage usage.

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.