How do you use Azure Data Lake Store to store Stream Analytics data and why would you do that?
2) Create the Stream Analytics Job
Before creating this new job, we already added a new consumer group to our IoT Hub, called 'DataLake'. Using multiple consumer groups makes it possible for several consumer applications to read data from this IoT Hub independently. Click here to see where you can add/manage consumer group(s).
Note:
The Azure Portal is still in development, so adding a new consumer group is now at a different place than in an earlier post. See screenshot below.
Now we can create the job. We choose the same Resource Group as the IoT Hub and Storage account, just like the creation of the Data Lake. Our Location is the Netherlands, so we choose West-Europe.
3) Configure the Stream Analytics Job
We start with defining the Input. We choose 'Data stream' as Source Type because the sensor data is an ongoing stream and is derived from the IoT Hub. Under Source we choose 'IoT hub'. In our case we have one IoT Hub, but when you have multiple IoT Hubs you can choose one from the list. Now our IoT Hub appears automatically. Next we choose 'datalake' as Consumer group. This is the one we have created earlier. Finally we choose 'JSON' as Event serialization format.
Now we can specify the Output. Give it a suitable name and choose 'Data Lake Store' as Sink. The corresponding (in thise case the one we have created earlier) Account Name will be selected automatically. Next we enter a file path to store the files in our Data Lake Store account. Optionally, you can specify this path with date and time. Now the data will be stored in multiple instances per day and per hour and this makes the storage more clearly. At last you will choose the 'JSON' format.
In this post we only configuring the Input and Output of the job, as mentioned earlier. Now we can run the job with a valid query.
Result
After running the Stream Analytics Job, the data is now stored in the Data Lake Store. You can find this in the Azure portal. Open the Data Lake Store and go to Data Explorer. Now you see one folder named 'sensordata'. This folder contains multiple subfolders: year, month, day and hour. Now we have only one file with data in the subfolder 'hour', but each next hour there will be a new file (as long as your Stream Analytics Job is running). This is exactly what we have configured earlier. In each file, the data is stored per 10 seconds. It works!
Differences between Blog Storage and Data Lake Store
Conclusion
Solution
In an earlier post we have set up a Stream Analytics Job where sensor data is send to a Blob file. In this case we create a new Stream Analytics Job with Data Lake Store as output and were we use the sensor data as input. Despite the fact that the Input and Output is different, the query stays the same as other Stream Analytics Jobs. You can find the configuration of the Stream Analytics Job query here.
A Data Lake Store is a scalable repository optimized for storing IoT data, log files and other large datasets for Big Data scenarios. It contains folders and these folders contain the files (data). As said before, you can also store (sensor) data using Blob storage. These two storage options are similar, but there are some important differences, which we will explain later in this post.
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| Overview Data Lake Store with different types of data |
1) Create a Data Lake Store
First we have to create the Data Lake Store. Click on New (+ icon) in your portal and under the Storage category you will find the Data Lake Store. Give it a suitable name. Next we choose the Resource Group which we created earlier by setting up the IoT Hub. The benefit of choosing the same group is that among other things, the rights are the same. At this time there are not many options available in both Location and Pricing, so it's default.
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| Azure Portal - Create Data Lake Store |
2) Create the Stream Analytics Job
Before creating this new job, we already added a new consumer group to our IoT Hub, called 'DataLake'. Using multiple consumer groups makes it possible for several consumer applications to read data from this IoT Hub independently. Click here to see where you can add/manage consumer group(s).
Note:
The Azure Portal is still in development, so adding a new consumer group is now at a different place than in an earlier post. See screenshot below.
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| Azure Portal - Adding Consumer Groups |
Now we can create the job. We choose the same Resource Group as the IoT Hub and Storage account, just like the creation of the Data Lake. Our Location is the Netherlands, so we choose West-Europe.
 |
| Azure Portal - Create Stream Analytics Job |
3) Configure the Stream Analytics Job
We start with defining the Input. We choose 'Data stream' as Source Type because the sensor data is an ongoing stream and is derived from the IoT Hub. Under Source we choose 'IoT hub'. In our case we have one IoT Hub, but when you have multiple IoT Hubs you can choose one from the list. Now our IoT Hub appears automatically. Next we choose 'datalake' as Consumer group. This is the one we have created earlier. Finally we choose 'JSON' as Event serialization format.
Now we can specify the Output. Give it a suitable name and choose 'Data Lake Store' as Sink. The corresponding (in thise case the one we have created earlier) Account Name will be selected automatically. Next we enter a file path to store the files in our Data Lake Store account. Optionally, you can specify this path with date and time. Now the data will be stored in multiple instances per day and per hour and this makes the storage more clearly. At last you will choose the 'JSON' format.
In this post we only configuring the Input and Output of the job, as mentioned earlier. Now we can run the job with a valid query.
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| Azure Portal - Configure Stream Analytics Job |
Result
After running the Stream Analytics Job, the data is now stored in the Data Lake Store. You can find this in the Azure portal. Open the Data Lake Store and go to Data Explorer. Now you see one folder named 'sensordata'. This folder contains multiple subfolders: year, month, day and hour. Now we have only one file with data in the subfolder 'hour', but each next hour there will be a new file (as long as your Stream Analytics Job is running). This is exactly what we have configured earlier. In each file, the data is stored per 10 seconds. It works!
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| Azure Portal - Result Data Lake Store |
Differences between Blog Storage and Data Lake Store
The first big difference is the size limits. There are no limitations in account/file size or number of files at a Data Lake Store, while Blob storage has such restrictions. In addition Data Lake Store has built-in Hadoop integration. Therefore (along with the unlimited storage) this makes it suitable for storing Big Data and then analyze this data. Another difference is the authentication. Blob storage works with generated storage access keys, while a Data Lake Store use Azure Active Directory for this.
Overall a Data Lake Store has more possibilities then Blob storage and is optimized for Big Data purposes. The general purpose of Blob Storage is storing data in different scenarios like backups or media files (for streaming). The starting prices are lower for Blob storage, but you have different storage prices for your Blob Storage account. This means: you can make it as expensive as you want and in some cases the monthly charges per GB will be higher than a Data Lake Store. Click here for more details about the differences and prices between a Data Lake Store and Blob storage.
Azure Data Lake Store is very useful with Big Data scenarios because it can combine storage (to more then 1 petabyte) with the ability to analyze this data. This can be done with Hadoop (built-in integration) or Azure Data Lake Analytics, which is specially optimized to work with Azure Data Lake Store. From this perspective it offers more than, for example Blob storage.
