What is the
architecture of Azure Data Lake?
Azure Data Lake is designed with 2 major components, data lake store and analytics. And majorly there are below structure:
1.) Internal system - YARN & WebHDFS. Yarn -
Analytics & WebHDFS - Hadoop hdfs
storage.
2.) Analytics - USQL
3.) Compute Engine - HdInsight (Big Data batch processing).
3 Azure Data Lake Store (ADLS) serving as the hyper-scale
storage layer.
What can I do with
Azure Data Lake Analytics?
·
Right now, ADLA is focused on batch processing,
which is great for many Big Data workloads.
·
Prepping large amounts of data for insertion
into a Data Warehouse
·
Processing scraped web data for science and
analysis
·
Churning through text, and quickly tokenizing to
enable context and sentiment analysis
·
Using image processing intelligence to quickly
process unstructured image data
·
Replacing long-running monthly batch processing
with shorter running distributed processes
ADLA is well equipped to handle many of the
types of processing we do in the T portion of ETL; that is, transforming data.
If you've found that your data volumes have increased, changed shape, or you
are generally not happy with your ETL performance, ADLA might serve as a good
replacement for your traditional approach to prepping data for analysis.
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