Build Smart Solutions using Big Data Stack on Microsoft Azure Platform – Azure Data Lake Analytics (Part 2)

After learning how to create Azure Data Lake Analytics (ADLA) account. It’s the time to write some queries to leverage this account. As we know, U-SQL is a query language for this platform. The best thing about Microsoft Big Data Stack on Azure is, the query languages are SQL like and are really easy to understand.

Let’s see how to leverage ADLA and write U-SQL queries. There are options like:

1. Submit the job directly from the portal –


Major Parameters are as follows:

1. Job Name –> Name of the Job
2. Parallelism –> Maximum number of compute processes that can happen at the same time
3. Priority –> Lesser the value , higher the priority is. Job with higher priority will run first.
4. Query Editor –> Write your U-SQL Queries

2. Other tools like:

Download the add-ins as per the preference. Another example, I will show you is from Visual Studio. After installing the tools for visual studio, the options will look like:



Just select the first option and the interface will be like this:


On the left hand side, you are seeing the Azure Analytics account, underneath that we have ADLA database (Master as a default). It gives a feel of SQL Server DBs underneath which we can have procedures/tables/views etc.

In the middle, there is an option to select the database (a new DB can be created), schema of the object , ADLA account (Local is by default) and then Submit. Moreover, if you click the drop down underneath submit , you can even select the parallelism and priority.

On the right side, you can register/create assemblies for the programming purpose and then later use those in the U-SQL queries.

After submitting the job when it completes, the interface looks like:

As shown in the pic, you can see the status of job and see how long it took to run the entire job.

Moreover, to learn U-SQL, please follow –

Let’s finish the all the components of Cortana Analytics Suite. After that, we will pick up a real life scenario and explain how all these components fit together.


Build Smart Solutions using Big Data Stack on Microsoft Azure Platform – Azure Data Lake Analytics(Part 1)

As we have set the context right through previous posts, now it’s time to understand how Big Data Queries are written and executed. As we know, we can store the data on Azure data lake store and there will be a use case for that. Let’s take a very easy example of Perfmon data – e.g. I have written some queries to process the perfmon data on daily basis. Let’s say, we want to find out, how many servers out of 500,000, servers faced memory pressure. We have automated perfmon data collectors scheduled on all the systems and the logs need to be analyzed on the daily basis.


1. Perfmon data collector files in CSV format are saved on Azure data lake store
2. Need to process all the files to find out the servers which faced memory pressure

In this scenario, we have options like put the data inside SQL Server and then do the analysis on the top of it. Analyzing perfmon data for 500,000 server is going to need lots of compute on SQL server and it may cost really heavy for the hardware. Moreover, the query has to be run just once per day. Do you think, it’s wise purchase 128 core machine and with TBs of SAN to do this job? In such case, we have options to process the data using Big Data solutions.

Note – I have used this very simple example to help you understand the concepts. We will talk about real life case studies as we move forward. 

In this particular scenario, I have choices like:

1. Use Azure Data Lake Analytics
2. Use Azure Data Lake HDInsight Hive cluster

For this post, I will pick Azure Data Lake Analytics (ADLA). This particular Azure service is also known as Big Data Query as a Service. Let’s first see how to create ADLA:

Step 1

Step 2  Enter the Data Lake Store detail for the storage and other details


In above steps, we have create compute account i.e. Azure Data Lake Analytics account which will process the files for us. ( Analogically, one machine with set of processors/RAM(ADLA) and for storage we added ADL store to the account). In Azure, we have both storage and compute as different entities. It helps to scale either compute or storage independent of each other.

Step 3 – After clicking create, the dashboard will look like this:


Now, both the compute (to process the files) and storage (where the perfmon files are stored) is created. As this service is big data query as a service, we can just write big data queries which internally will be executed by Azure platform automatically. It’s a PaaS service like SQL Azure DB where you just write your queries without bothering about what machine is underneath or where the files are stored internally.

Analogically, it’s a broker for you who you hand over the files , give him the instructions  , instruct how many people should work on the task (for compute) and then he shares the results with you. This broker understand U-SQL as a language like T-SQL is for SQL Server. If you want to get your task done, you need to write U-SQL queries and submit to the ADLA. Based on the instructions and compute defined by you, it will return the results.
Let’s talk about framework to write U-SQL Queries in the upcoming posts.