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Where Change Is Welcome

Drift Synchronization with StreamSets Data Collector and Azure Data Lake

By March 6, 2017

ADLS Drift PipelineOne of the great things about StreamSets Data Collector is that its record-oriented architecture allows great flexibility in creating data pipelines – you can plug together pretty much any combination of origins, processors and destinations to build a data flow. After I wrote the Ingesting Local Data into Azure Data Lake Store tutorial, it occurred to me that the Azure Data Lake Store destination should work with the Hive Metadata processor and Hive Metastore destination to allow me to replicate schema changes from a data source such as a relational database into Apache Hive running on HDInsight. Of course, there is a world of difference between should and does, so I was quite apprehensive as I duplicated the pipeline that I used for the Ingesting Drifting Data into Hive and Impala tutorial and replaced the Hadoop FS destination with the Azure Data Lake Store equivalent.

Ingest Data into Azure Data Lake Store with StreamSets Data Collector

By February 20, 2017

SDC and Power BIAzure Data Lake Store (ADLS) is Microsoft’s cloud repository for big data analytic workloads, designed to capture data for operational and exploratory analytics. StreamSets Data Collector (SDC) version included an Azure Data Lake Store destination, so you can create pipelines to read data from any supported data source and write it to ADLS.

Since configuring the ADLS destination is a multi-step process; our new tutorial, Ingesting Local Data into Azure Data Lake Store, walks you through the process of adding SDC as an application in Azure Active Directory, creating a Data Lake Store, building a simple data ingest pipeline, and then configuring the ADLS destination with credentials to write to an ADLS directory.

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