Azure - Continue with Azure Data Lake for Big Data

Case
I an earlier post we showed you how to transform sensor data using Azure Data Lake. Many companies are gathering (or already have) a lot of Big Data in many different files. How can we use Azure Data Lake Analytics (ADLA) to handle these files?

Big Data and U-SQL

Solution
Just like the previous post, the sensor data is already stored in an Azure Data Lake Store (ADLS). Next, we build and configure an U-SQL Job. This is Microsoft's new Big Data query language that you can use in ADLA. Last time we developed in the Azure Portal, but there are other options. Last month, Microsoft released a Visual Studio plug-in for Azure Data Lake and Stream Analytics. This allows you, while writing U-SQL queries, to use other benefits of Visual Studio like Team Foundation Server (TFS), debugging and adding C# code for custom inputs and outputs.

In this case we have sensor data from one year. The data is stored in several files: one file per day. We want to create a U-SQL job that aggregates the data per day and then stores all the data. For now we focus on the query itself. See here how to create an ADLA service/account and to create a new U-SQL Job.

1) Install plug-in for Visual Studio

First we have to download and install the plug-in Microsoft Azure Data Lake and Stream Analytics Tools for Visual Studio. You can download the plug-in here. Besides the creating and debugging of U-SQL scripts, you can also build queries of Azure Stream Analytics jobs using this plug-in.

2) Write the Query

Open Visual Studio and create a new U-SQL project. Our U-SQL script is called 'multipleFiles'. The starting point is the query we made in an earlier post extracting one single sensor file.

Variables
Because we have multiple files, we are creating a dynamic FROM clause using variables. In this case the folder path from ADLS. We use the following syntax for this:"bitools_sample_data_{*}.csv". This is a wildcard and will get you every file of the year (see comment in the query below for the input files structure). We also skip the first row, the headers.

// File naming convention: bitools_sample_data_01-01-2016.csv, bitools_sample_data_01-02-2016.csv etc.
// Create variable for input files
DECLARE @folderInput string = "/SensorData/Input/";
DECLARE @inputString string = @folderInput + "bitools_sample_data_{*}.csv";

Extract
To retrieve the data from the files, we use an EXTRACT statement. In an earlier post, we extracted the data as a string. Now we extract the 'time' column as date time format (just like the source file), using the variable in the FROM clause we created earlier.

// Extract the sensor data from CSV file (skip the header)
@sensorData = 
    EXTRACT 
        [time]                    DateTime
    ,   [dsplid]                  string
    ,   [dspl]                    string
    ,   [temp]                    string
    ,   [hmdt]                    string
    ,   [status]                  string
    ,   [location]                string
    ,   [EventProcessedUtcTime]   string
    ,   [PartitionId]             string
    ,   [EventEnqueuedUtcTime]    string
    FROM @inputString
    USING Extractors.Csv(skipFirstNRows:1);

Transformation
Next we aggregate the data into averages based on the 'time' and 'location' column, using a SELECT statement. We convert the 'time' column to a date format, because we want to aggregate per day. We give the column names a suitable name. You may have noticed that we do not select all the columns, because we do not need all columns from the source file.

// Aggregate the sensor data (average per location) and data type conversions
@result =
    SELECT
        time.ToString("yyyy-MM-dd") AS Date
    ,   AVG(Convert.ToInt32([temp])) AS Temperature
    ,   AVG(Convert.ToInt32([hmdt])) AS Humidity
    ,   [location] AS Location
    FROM @sensorData
    GROUP BY
        time.ToString("yyyy-MM-dd")
    ,   [location];

Output
Finally, we save the data in a new CSV file. In the OUTPUT statement, you can also add an ORDER BY clause. We want the header back in our output data and therefore we use 'outputHeader'.

// Save the sensor data to a new CSV file
OUTPUT @result
TO "/SensorData/Output/bitools_sample_data_AveragePerDayPerLocation.csv"
ORDER BY 
    [Location] ASC
USING Outputters.Csv(outputHeader : true, quoting:false);

See below a screenshot of the full query in Visual Studio.

Visual Studio - U-SQL script

3) Run the Job

When you have built the query, click 'Submit' and then the Job View screen automatically appears. This is similar to Job Details in the Azure Portal that we used earlier. But when you look closely, you see Visual Studio offers more information then the portal. For example, more details at 'Job Summary' and errors details.

Visual Studio - Run U-SQL script

Error details
When you have an error in the U-SQL query, you can see often the details of this error directly in the 'Job View' screen. In case of an Vertex user code error, you do not immediately see the error details on this screen. If you want to see details of this error, scroll down in the 'Job Summary' and click on 'Resources'. Then choose 'Profile' and search for the keyword 'jobError'. This row contains the details of the error.

Visual Studio - U-SQL Query error details

4) Result
Now go to the Azure portal and to your Azure Data Lake Store. Open the new file in 'Data Explorer'. Our output file is located in the folder 'SensorData' and then 'Output'. The result should look like this:

Azure Portal - View result in Data Lake Store

Conclusion
In this post we went deeper into building an U-SQL script using Visual Studio. In our opinion, you should develop as much as possible in Visual Studio, because we all know the benefits of this tool like TFS and debugging.

Use PolyBase to read Blob Storage in Azure SQL DW

Case
I have a file in an Azure Blob Storage container which I want to use in my Azure SQL Data Warehouse. How can I push the content of that file to Azure SQL DW?

One of the options: PolyBase

Solution
You could of course use an ETL product or Azure Data Factory, but you can also use PolyBase technology in Azure SQL DW and use that file as an external table. The data stays in the Azure Blob Storage file, but you can query the data like a regular table.

Starting position
Starting position is a file in an Azure Blob Storage container. This file was created with U-SQL in an other post to quickly process large amounts of files in Azure.

The content of the CSV file

Before we start, make sure your Azure SQL Data Warehouse is started and use SQL Server Management Studio (SSMS) to connect to your Data Warehouse. Notice that the icon of a SQL DW is different than SQL DB.

Icon SQL DW vs SQL DB

1) Master key

In the next step we will use a credential that points to the Azure Blob Storage. To encrypt that credential, we first need to create a master key in our Azure SQL Data Warehouse, but only if you do not already have one. You can check that in the table sys.symmetric_keys. If a row exists where the symmetric_key_id column is 101 (or the name column is '##MS_DatabaseMasterKey##') then you already have a master key. Else we need to create one. For Azure SQL Data Warehouse a password for that master key is optional. For this example we will not use the password.

--Master key
IF NOT EXISTS (SELECT * FROM sys.symmetric_keys WHERE symmetric_key_id = 101)
BEGIN
    PRINT 'Creating Master Key'
    CREATE MASTER KEY;
END
ELSE
BEGIN
    PRINT 'Master Key already exists'
END 

2) Credentials
Next step is to create a credential which will be used to access the Azure Blob Storage. Go to the Azure portal and find the Storage Account that contains your blob file. Then go to the Access keys page and copy the key1 (or key2).

Access keys

Then execute the following code where IDENTITY contains a random string and SECRET contains the copied key from your Azure Storage account.

--Credential
CREATE DATABASE SCOPED CREDENTIAL AzureStorageCredential
WITH
    IDENTITY = 'user',
    SECRET = 'JGadV/tAt1npuNwkiH9HnI/wosi8YS********=='
;

Tip: give the credential a descriptive name so that you know where it is used for. You can find all credentials in the table sys.database_credentials:

--Find all credential
SELECT * FROM sys.database_credentials

3) External data source
With the credential from the previous step we will create an External data source that points to the Azure Blob Storage container where your file is located. Execute the code below where:

• TYPE = HADOOP (because PolyBase uses the Hadoop APIs to access the container)
• LOCATION = the connection string to the container (replace [ContainerName] with the name of the container and [StorageAccountName] with the name of your storage account).
• CREDENTIAL = the name of the credentials created in the previous step.

--Create External Data Source
CREATE EXTERNAL DATA SOURCE AzureStorage
WITH (
    TYPE = HADOOP,
    LOCATION = 'wasbs://[ContainerName]@[StorageAccountName].blob.core.windows.net',
    CREDENTIAL = AzureStorageCredential
);

Tip: give the external source a descriptive name so that you know where it is used for. You can find all external data sources in the table sys.external_data_sources:

--Find all external sources
SELECT * FROM sys.external_data_sources

Notice that the filename is not mentioned in the External Data Source. This is done in the External Table. This allows you to use multiple files from the same container as External Tables.

Filename not in External Data Source

4) External File format
Now we need to describe the format used in the source file. In our case we have a comma delimited file. You can also use this file format to supply the date format, compression type or encoding.

--Create External Data Source
CREATE EXTERNAL FILE FORMAT TextFile
WITH (
    FORMAT_TYPE = DelimitedText,
    FORMAT_OPTIONS (FIELD_TERMINATOR = ',')
);

Tip: give the format a descriptive name so that you know where it is used for. You can find all external file formats in the table sys.external_file_formats:

--Find all external file formats
SELECT * FROM sys.external_file_formats

5) External Table
The last step before we can start quering, is creating the external table. In this create table script you need to specify all columns, datatypes and the filename that you want to read. The filename starts with a forward slash. You also need the datasource from step 3 and the file format from step 4.

--Create External table
CREATE EXTERNAL TABLE dbo.sensordata (
    [Date] DateTime2(7) NOT NULL,
    [temp] INT NOT NULL,
    [hmdt] INT NOT NULL,
    [location] nvarchar(50) NOT NULL
)
WITH (
    LOCATION='/bitools_sample_data_AveragePerDayPerBuilding.csv',
    DATA_SOURCE=AzureStorage, -- from step 3
    FILE_FORMAT=TextFile      -- from step 4
);

Note: PolyBase does not like columnname headers. It will handle it like a regular data row and throw an error when the datatype doesn't match. There is a little workaround for this with REJECT_TYPE and REJECT_VALUE. However this only works when the datatype of the header is different than the datatypes of the actual rows. Else you have to filter the header row in a subsequent step.

--Create External table with header
CREATE EXTERNAL TABLE dbo.sensordata5 (
    [Date] DateTime2(7) NOT NULL,
    [temp] INT NOT NULL,
    [hmdt] INT NOT NULL,
    [location] nvarchar(50) NOT NULL
)
WITH (
    LOCATION='/bitools_sample_data_AveragePerDayPerBuilding.csv',
    DATA_SOURCE=AzureStorage,
    FILE_FORMAT=TextFile,
    REJECT_TYPE = VALUE, -- Reject rows with wrong datatypes
    REJECT_VALUE = 1     -- Allow 1 failure (the header)
);

You can find all external tables in the table sys.external_tables.

--Find all external tables
SELECT * FROM sys.external_tables

However you can also find the External Table (/the External Data Source/the External File Format) in the Object Explorer of SSMS.

SSMS Object Explorer

6) Query external table
Now you can query the external table like any other regular table. However the table is read-only so you can not delete, update or insert records. If you update the source file then the data in this external table also changes instantly because the file is used to get the data.

--Testing
SELECT count(*) FROM dbo.sensordata;
SELECT * FROM dbo.sensordata;

Quering an external table

7) What is next?
Most likely you will be using a CTAS query (Create Table As Select) to copy and transform the data to an other table since this is the fasted/preferred way in SQL DW. In a subsequent post we will explain more about CTAS, but here is how a CTAS query looks like.

--CTAS
CREATE TABLE [dbo].[Buildings]
WITH
(
    DISTRIBUTION = ROUND_ROBIN
,   CLUSTERED COLUMNSTORE INDEX
)
AS
SELECT  [location]
,       [date]
,       [temp]
,       [hmdt]
FROM    [dbo].[sensordata]
;

In some cases you could also use an SELECT INTO query as an alternative for CTAS.

Summary
In this post you saw how easy it was to read a file from the Azure Blob Storage and use it as a table in Azure SQL Data Warehouse. The big advantage of PolyBase is that you only have one copy of the data because the data stays in the file. In a next post we will see how to read the same file from the Azure Data Lake Store which does not use the Access keys.
In an other post we will explain the basic usage of the CTAS query which is the preferred way to handle large sets of data in Azure SQL DW and in its on-premises precursor APS (a.k.a. PDW).