More and more of businesses are either storing or manipulating some, if not all, of their data in the cloud. We learnt from our customers that they would prefer to leverage their existing on-premises ETL investment while seeking low cost solution to access and integrate data in the cloud.
SQL Server Integration Services Feature Pack for Azure enables you to easily extend your SSIS workflow to use cloud technologies powered by Microsoft Azure, such as Azure Storage, HDInsight, and Machine Learning etc.
The rest of the article lists four types of canonical scenarios using the SQL Server Integration Services Feature Pack for Azure to accomplish business needs common in hybrid data integration scenarios.
Businesses nowadays often have lot of data and log from the web site and/or web services. Processing such large amount raw data is important to extract business insight such as web page usage patterns, customer preference prediction and more. However, it does not make too much sense cost-wise to move all of this data from cloud to on-premises before processing it with SSIS. It is more efficient to process these data on the cloud upfront before moving the business insight back to on-premises data warehouse for internal reporting.
With the HDInsight Task from the SQL Server Integration Services Feature Pack for Azure , you can now orchestrate and create a Hadoop jobs on Azure HDInsight as part of the SSIS workflow to process raw data on the cloud. You can now use SSIS as your main data orchestration pipeline for both on-premises and cloud transformation activities.
Here is how you can do that:
1) Creating an on-demand cluster using Azure HDInsight Create Cluster Task .
2) Uploading a bunch of Hive scripts (*.hql) to the cluster storage using Azure Blob Upload Task .
3) Invoking all the Hive scripts by wrapping an Azure HDInsight Hive Job Task in the Foreach Loop Container (with Azure Blob Enumerator) .
4) Deleting the cluster using Azure HDInsight Delete Cluster Task at the end.
Note: Using an on-demand HDInsight cluster is a practice to save cost from Azure HDInsight pricing , as it is costly to keep the cluster alive but idle. The tradeoff, however, is the overhead time on creating and deleting the cluster dynamically.
HDInsight Task from the SQL Server Integration Services Feature Pack for Azure provides a new way to manipulate and transform data in the cloud. In order to get relevant business insight from the web raw data, it is common that businesses usually run the HDInsight processing with both raw web data and company data on-premises. It is important to prepare the on-premises data in certain format before consuming by HDInsight. It is also important to mask PII or sensitive data before moving the data to the cloud for HDInsight processing.
In such case, you can use the transformations in SSIS to prepare and mask the data that contains your customer information. Then, you can use Azure Blob Destination from the SQL Server Integration Services Feature Pack for Azure to load the masked data into Azure Storage blob.
The transformation runs following C# code snippet for each row of [CustomerName] column.
public override void Input0_ProcessInputRow(Input0Buffer Row)
string customerName = Row.CustomerName;
Row.HashedCustomerName = MaskCustomerName(customerName);
private string MaskCustomerName(string customerName)
//Add your data mask logic here
Once you get the business insight data from the cloud compute services, such as Azure Stream Analytics , Azure Machine Learning and Azure HDInsight , the next thing you would like to do is to move the business insight data back to on-premises for reporting or for additional processing.
In such case, you can use the Azure Blob Source from the SQL Server Integration Services Feature Pack for Azure to extract the data from Blob storage.
In the following example, the SSIS package uses a Foreach Loop Container (with Azure Blob Enumerator) to run a Data Flow Task against each blob outputted from HDInsight Hive processing.
Inside the Data Flow , the Azure Blob Source extracts the data and passes it to a Slowly Changing Dimension Transformation . The transformation helps identify Type 1 and Type 2 changes, which makes it a very useful component in SSIS to handle data upsert into data warehouse.
Traditionally, businesses build data warehouse with extra secondary storage for backup and/or archival purpose, especially on transactional data that are required to be kept due to legal or audit reason. With the rapid growth of data size, cloud-based storage, such as Azure Storage , becomes popular mainly due to the benefit of cost and scalability.
The SQL Server Integration Services Feature Pack for Azure helps you to easily create a SSIS package that migrates all your data into Azure blob for archiving purpose, using the Azure Blob Destination in the Data Flow .
Sometimes you may just want to use Azure blob as a back-up storage, while keeping the data written into the original data store. In this case, you can slightly modify your SSIS package and add a Multicast Transformation to write another copy of the output data into Azure Blob Destination .
Building ETL data pipeline and workflow sometimes is just like building LEGO, you can build it with different patterns and possibilities. The SQL Server Integration Services Feature Pack for Azure , now opens more possibilities for building different patterns of hybrid data pipeline.
To build and run the scenarios by yourself, please download and install:
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