Skip to main content


Showing posts with the label ADL

Azure Data Lake Analytics Services feature ?

Azure data lake analytics is 2nd main component of Azure data lake and bigdata analytics platform. There are below main feature; It is built on apache Yarn. Scale dynamically with the turn of a dial. Pay by the query. Supports Azure AD for access control, roles and integration with on-prem identity system. Built with U-SQL with the power of C#. Process data across Azure. Write,debug and optimize big data app in visual studio. Multiple language U-SQL , Hive and Pig. Thanks for reading Plz dont forget to like Facebook Page..

What are the Feature of Data lake stores ?

Azure data lake is the key services of microsoft bigdata platform. There are 2 main component of Azure Data lake is Data store and analytics. Store is one of important. 1.Each file in ADL stores is sliced into blocks. 2. Blocks are distributed across multiple data nodes in the backed storage system. 3. With sufficient number of back-end storage data nodes, files of any size can be stored here. 4. Back-end storage runs in to the azure cloud which has virtually unlimited resources. 5. Metadata stored about each file. No limit to metadata either. 6. Azure maintains 3 replicas of each data object per region across three fault and upgrade domains. 7. Each create or append operation on a replica is replicated to other two. 8. Writes are committed to application only after all replicas  are successfully updated. 9. Read operation can go against all replica. 10. It is role based access mechanism. Each file/ directory has owner and group. they have r,w,x permissions. Thanks for

What are Traditional BI and Analytics process model.

If you see the Traditional BI processing model and modern data lake environment then you will find the processing model are totally different.  In respect of schema or transformation or requirements.Before we were using schema on write and now we are doing schema on read. In BI model Transformation was done after extraction but now after load.    Start with end-user requirement, to identify desired reports and analysis. ·   Define corresponding database schema and queries. ·   Identify the required data source ·   Create a ETL pipeline to extract required data and transform it to target schema. Create reports. Analyze data. Thanks for reading Plz dont forget to like Facebook Page..

What is Data Lake and Azure Data Lake

What is data lake ? A data lake is a storage repository that holds a vast amount of raw data in its native format until it is needed. While a hierarchical data warehouse stores data in files or folders, a data lake uses a flat architecture to store data. Each data element in a lake is assigned a unique identifier and tagged with a set of extended metadata tags. When a business question arises, the data lake can be queried for relevant data, and that smaller set of data can then be analyzed to help answer the question. A data lake, on the other hand, maintains data in their native formats and handles the three Vs of big data — volume, velocity, and variety — while providing tools for analyzing, querying, and processing. Data lakes eliminate all the restrictions of a typical data warehouse system by providing unlimited space, unrestricted file size, schema on read, and various ways to access data (including programming, SQL-like queries, and REST calls). What is Azure Data La