Azure Data Engineering Course in Hyderabad

Enroll in our Azure Data Engineering Course in Hyderabad to gain in-depth knowledge of Microsoft Azure's powerful data processing capabilities. Learn essential skills such as data ingestion, storage, and analytics using Azure services. Our hands-on training, led by industry experts, will equip you with the expertise needed to design and implement robust data solutions. Prepare for a successful career in data engineering with our specialized course in the heart of Hyderabad.

1. Azure Data Engineering

2. Table of content
Introduction to Azure Data Engineering
Azure Data Services Overview
Azure Data Factory
Azure Databricks
AzureSynapseAnalytics
AzureDataLakeStorage
Real-timeDataProcessingwithAzureStreamAnalytics
IntegrationwithPowerBI

3. Introduction to Azure Data Engineering
•AzureDataEngineeringreferstothesetofservicesandtools
providedbyMicrosoftAzurefordesigning,implementing,and
managingdatasolutionsinthecloud.Itencompassesvarious
technologiesandcapabilitiesthatalloworganizationstoprocess,
store,andanalyzelargevolumesofdataefficiently.Whetherdealing
withstructuredorunstructureddata,AzureDataEngineering
providesacomprehensivesuiteofservicestomeetdiversebusiness
needs.
•AsanAzuredataengineer,youhelpstakeholdersunderstandthe
datathroughexploration,andbuildandmaintainsecureand
compliantdataprocessingpipelinesbyusingdifferenttoolsand
techniques.YouusevariousAzuredataservicesandframeworksto
storeandproducecleansedandenhanceddatasetsforanalysis.

4. Azure Data Services Overview
1.AzureSQLDatabase:Afullymanagedrelationaldatabaseservicethatoffershigh-performance,scalability,andbuilt-insecurityfeatures.Itsupportspopulardatabaseengines
suchasSQLServer,MySQL,andPostgreSQL.
2.AzureCosmosDB:Agloballydistributed,multi-modeldatabaseservicedesignedforbuildinghighlyresponsiveandscalableapplications.Itsupportsmultipledatamodels,
includingdocument,graph,key-value,table,andcolumn-family.
3.AzureSynapseAnalytics(formerlySQLDataWarehouse):Anintegratedanalyticsservicethatbringstogetherbigdataanddatawarehousing.Itallowsuserstoqueryand
analyzelargedatasetsusingbothon-demandandprovisionedresources.
4.AzureDataLakeStorage:Ascalableandsecuredatalakesolutionforbigdataanalytics.Itenablesorganizationstostoreandanalyzemassiveamountsofdatawithfeatures
likehierarchicalnamespaceandfine-grainedaccesscontrol.
5.AzureBlobStorage:Amassivelyscalableobjectstorageservicethatisoptimizedforstoringandservinglargeamountsofunstructureddata,suchasdocuments,images,and
videos.
6.AzureDataFactory:Acloud-baseddataintegrationservicethatallowsorganizationstocreate,schedule,andmanagedatapipelines,facilitatingthemovementand
transformationofdataacrossvarioussourcesanddestinations.
7.AzureDatabricks:AnApacheSpark-basedanalyticsplatformthatprovidesacollaborativeenvironmentforbigdataanalytics.Itallowsdataengineersanddatascientiststo
worktogetheronlarge-scaledataprocessingandmachinelearningtasks.
8.AzureHDInsight:Afullymanagedcloudservicethatmakesiteasytoprocesslargeamountsofdatausingpopularopen-sourceframeworkssuchasHadoop,Spark,Hive,
HBase,andmore.
9.AzureStreamAnalytics:Areal-timeanalyticsservicethatingests,processes,andanalyzesstreamingdatafromvarioussources.Itprovidesinsightsintotrendsandpatterns
asdataisgenerated.
10.AzureDataExplorer:Afastandhighlyscalableservicedesignedforanalyzinglargevolumesofdatainreal-time.Itisparticularlywell-suitedforlogandtelemetrydata.
11.AzureCacheforRedis:Afullymanaged,open-source,andin-memorydatastoreservicethatprovidessub-millisecondresponsetimes.Itiscommonlyusedforcachingand
acceleratingdataaccess.
12.AzureDataBox:AfamilyofdevicesdesignedtofacilitatethesecureandefficienttransferoflargeamountsofdatatoandfromAzure.Thisisparticularlyusefulfor
organizationsdealingwithmassivedatasets.
13.AzureDataShare:Aservicethatenablesorganizationstosecurelysharedatawithotherorganizationsinagovernedandcompliantmanner.Itsimplifiestheprocessofsharing
dataacrossAzuresubscriptionsandwithexternalpartners.
14.AzureDataCatalog:Afullymanagedservicethatservesasacentralizedrepositoryfordiscovering,understanding,andmanagingdataassetsacrossanorganization.Ithelps
inmaintainingadatacatalogforbetterdatagovernance

5. Azure Data Factory
•AzureDataFactory(ADF)isacloud-baseddataintegrationservice
providedbyMicrosoftAzure.Itallowsorganizationstocreate,schedule,
andmanagedatapipelinesthatcanmovedatabetweensupportedon-
premisesandcloud-baseddatastores.AzureDataFactorysimplifiesthe
processoforchestratingandautomatingthemovementandtransformation
ofdata,makingitafundamentalcomponentinmoderndataengineering
workflows.
•AzureDataFactoryisAzure'scloudETLserviceforscale-outserverless
dataintegrationanddatatransformation.Itoffersacode-freeUIfor
intuitiveauthoringandsingle-pane-of-glassmonitoringandmanagement.
YoucanalsoliftandshiftexistingSSISpackagestoAzureandrunthem
withfullcompatibilityinADF.
•AzureDataFactoryisacloud-baseddataintegrationservice
providedbyMicrosoft.Itallowsyoutocreate,schedule,andmanage
datapipelinesthatcanmoveandtransformdatafromvarious
sourcestodifferentdestinations.

6. Azure Databricks
•AzureDatabricksisacloud-basedbigdataanalyticsplatformprovidedby
MicrosoftincollaborationwithDatabricks.ItisbuiltonApacheSparkand
designedfordataengineering,datascience,andmachinelearning.Azure
DatabrickssimplifiestheprocessofbuildingandmanagingApacheSpark-
basedbigdataandmachinelearningsolutionsbyprovidinganintegrated,
collaborativeenvironmentfordatascientists,dataengineers,andbusiness
analysts.
•AzureDatabricksisafullymanagedfirst-partyservicethatenablesanopen
datalakehouseinAzure.Withalakehousebuiltontopofanopendatalake,
quicklylightupavarietyofanalyticalworkloadswhileallowingfor
commongovernanceacrossyourentiredataestate.
•Databricksisanindustry-leading,cloud-baseddataengineeringtoolused
forprocessingandtransformingmassivequantitiesofdataandexploring
thedatathroughmachinelearningmodels.RecentlyaddedtoAzure,it'sthe
latestbigdatatoolfortheMicrosoftcloud

7. AzureSynapseAnalytics:
AzureSynapseAnalytics,formerlyknownasAzureSQLDataWarehouse,isacloud-based
analyticsserviceprovidedbyMicrosoftAzure.Itisdesignedtoenableorganizationstoanalyze
andquerylargevolumesofdatawithhighperformanceandscalability.AzureSynapseAnalytics
integratesbothdatawarehousingandbigdataanalyticscapabilities,providingaunifiedplatform
forprocessingandanalyzingdiversedatasets.
AzureDataLakeStorage:
AzureDataLakeStorage(ADLS)isascalableandsecurecloud-baseddatalakesolution
providedbyMicrosoftAzure.Itisdesignedtohandlelargevolumesofdataforbigdata
analyticsanddatascienceapplications.AzureDataLakeStorageisbuilttosupportboth
structuredandunstructureddata,allowingorganizationstostoreandanalyzediversedatasets
withhighthroughputandlow-latencyaccess.
Real-timeDataProcessingwithAzureStreamAnalytics:
AzureStreamAnalyticsisareal-timeanalyticsserviceprovidedbyMicrosoftAzurethatallows
organizationstoprocessandanalyzestreamingdatainreal-time.Itenablestheextractionof
insightsandactionableinformationfromcontinuousstreamsofdatageneratedbyvarious
sources,suchasIoTdevices,socialmedia,applications,andmore.AzureStreamAnalytics
supportsawiderangeofscenarios,includingreal-timemonitoring,anomalydetection,and
event-drivenapplications

8. Integration with Power BI
1.Configure Power BI Output in Azure Stream Analytics:In the Azure Stream Analytics job
definition, users can configure Power BI as an output sink. This is done by specifying the Power BI
output settings, including the Power BI workspace, dataset, and table to which the streaming data
will be sent.
2.Define Query Logic: Users define the query logic in Azure Stream Analytics using the SQL-like
query language. This query defines how the incoming streaming data is processed, filtered, and
transformed before being sent to Power BI. The query can include various operations to extract
meaningful information from the data.
3.Specify Output Schema:Users need to specify the output schema that aligns with the structure
expected by the Power BI dataset. This includes defining the data types and structure of the fields
that will be sent to Power BI.
4.Establish Authentication: To enable Azure Stream Analytics to push data to Power BI, users need
to establish authentication. This typically involves providing the necessary credentials or using
Azure Active Directory authentication to ensure secure communication between Azure Stream
Analytics and Power BI.
5.Start the Stream Analytics Job:Once the configuration is complete, users start the Azure Stream
Analytics job. This initiates the real-time processing of streaming data based on the defined query
logic. As the data is processed, the results are continuously sent to the specified Power BI
workspace and dataset.
6.Visualize Real-Time Data in Power BI:In Power BI, users can connect to the configured dataset
and create real-time dashboards and reports. The streaming data from Azure Stream Analytics is
visualized in Power BI, providing users with up-to-the-moment insights into their data.

9. Presenter name: kathika.kalyani
Email address: [email protected]
Website address: www.3ZenX.com