Index
A
- ADF
- SQL Server Integration Services (SSIS) / SSIS in ADF
- Databricks notebook execution, calling / Calling Databricks notebook execution in ADF
- ADF integration runtime
- downloading URL / Self-hosted integration runtime
- ADF V2.0
- features / What's new in V2.0?
- package, leveraging / Leveraging our package in ADF V2
- algorithms, machine learning
- supervised learning / Machine learning algorithms, Supervised learning
- unsupervised learning / Machine learning algorithms, Unsupervised learning
- reinforcement learning / Machine learning algorithms, Reinforcement learning
- Azure
- reference / Azure Data Factory
- Azure Blob storage
- about / Azure Blob storage
- containers / Blob containers
- page blobs / Page blobs
- account, creating / Creating an Azure Blob storage account
- Azure Databricks
- setup / Azure Databricks setup
- Azure Data Factory (ADF)
- creating / Azure Data Factory
- reference / Azure Data Factory
- datasets / Datasets
- activities / Activities
- data factory pipeline runs, monitoring / Monitoring the data factory pipeline runs
- Azure Data Lake Analytics (ADLA)
- Azure Feature Pack
- reference / SSIS in ADF
- Azure Machine Learning / Block blobs
- Azure Machine Learning Studio
- about / Azure Machine Learning Studio
- reference / Azure Machine Learning Studio
- account / Azure Machine Learning Studio account
- experiment / Azure Machine Learning Studio experiment
- Azure storage account
- folder, setting up / Setting up the folder in the Azure storage account
- Azure Storage Explorer
- reference / Ways to directly copy files into the Data Lake
B
- blobs
- reference / Types of blobs
- types / Types of blobs
- block blobs / Block blobs
- replication of storage / Replication of storage
- blob storage dataset
- linked service / Linked service
- dataset, adding / Dataset
- block blobs / Block blobs
- breast cancer detection
- about / Breast cancer detection
- data, obtaining / Get the data
- data, preparing / Prepare the data
- model, training / Train the model
- model, evaluating / Score and evaluate the model
- model, scoring / Score and evaluate the model
- Business Intelligence (BI)
- about / Driven by IT, Different types of BI
- self-service / Self-service – personal
- team BI / Team BI – sharing personal BI data
- corporate BI / Corporate BI
C
- classifier / Automated classification using machine learning
- cloud-based BI / Cloud-based BI – big data and artificial intelligence
- clustering methods
- used, for identifying groups / Identifying groups using clustering methods
- components, modern data warehouse
- staging area / Staging area
- consumption layer / Consumption layer – BI and analytics
- linked services / What is Azure Data Factory
- datasets / What is Azure Data Factory
- pipeline / What is Azure Data Factory
- copy activity
- publishing / Publish and trigger the copy activity
- triggering / Publish and trigger the copy activity
- corporate BI
- premium / Power BI Premium
- premium, reference / Power BI Premium
- Power BI Report Server / Power BI Report Server
D
- data
- preparing, for ingest / Prepare the data to ingest
- coping, from SQL Server to sales-data / Copy data from SQL Server to sales-data
- Databricks notebook
- creating / Databricks notebook
- Databricks notebook execution
- calling, in ADF / Calling Databricks notebook execution in ADF
- Data Explorer / Ways to directly copy files into the Data Lake
- data factory
- used, for data manipulation in Data Lake / Using the data factory to manipulate data in the Data Lake
- Data Factory
- used, for copying/importing data from SQL Server to Blob Storage file / Task 1 – copy/import data from SQL Server to a blob storage file using data factory
- Data Factory Pipeline
- U-SQL task, executing for data summarization / Task 2 – run a U-SQL task from the data factory pipeline to summarize data
- Data Lake Analytics
- resource, creating / Creating a Data Lake Analytics resource
- U-SQL, executing from job / Run U-SQL from a job in the Data Lake Analytics
- Data Lake Store
- configuring / Creating and configuring Data Lake Store
- creating / Creating and configuring Data Lake Store
- steps / Next Steps
- data, copying/ importing from database / Ways to copy/import data from a database to the Data Lake
- imported data, storing in files / Ways to store imported data in files in the Data Lake
- data, moving / Easily moving data to the Data Lake Store
- files, copying directly / Ways to directly copy files into the Data Lake
- reference / Ways to directly copy files into the Data Lake
- prerequisites / Prerequisites for the next steps
- data factory, used for data manipulation / Using the data factory to manipulate data in the Data Lake
- service principal authentication / Service principal authentication
- datasets
- about / Datasets
- linked services / Linked services
- integration runtimes / Integration runtimes
- setup / Datasets setup
- Data Vault 2.0
- reference link / Data warehouse
- data warehouse
- need for / The need for a data warehouse
- IT driven / Driven by IT
- self-service BI / Self-service BI
- cloud-based BI / Cloud-based BI – big data and artificial intelligence
- decision support systems (DSS) / Driven by IT
- dimensionality reduction
- used, for improving performance / Dimensionality reduction to improve performance
- feature selection / Feature selection
- feature extraction / Feature extraction
E
- encryption Enabled option
- reference / Creating and configuring Data Lake Store
- experiment, Azure Machine Learning Studio
F
- features, ADF V2.0
- integration runtime / Integration runtime
- linked services / Linked services
- datasets / Datasets
- pipelines / Pipelines
- parameters / Parameters
- expressions / Expressions
- flow of activities, controlling / Controlling the flow of activities
- SSIS package deployment, in Azure / SSIS package deployment in Azure
- Spark cluster data store / Spark cluster data store
- features, modern data warehouse
- hybrid deployment / The modern data warehouse
- integration / The modern data warehouse
- advanced analytics / The modern data warehouse
- in-database analytics / The modern data warehouse
- feature selection
- about / Feature selection
- filter class / Feature selection
- wrapper class / Feature selection
- embedded class / Feature selection
I
- integration runtime
- Azure integration runtime / Integration runtimes, Azure integration runtime
- self-hosted integration runtime / Integration runtimes, Self-hosted integration runtime
- Azure-SSIS integration runtime / Integration runtimes
- about / Integration runtimes
- self-hosted runtime / Self-hosted runtime
- SSIS integration runtime / SSIS integration runtime
K
- Kimball group
- reference link / Data warehouse
L
- linked service
- setup / Linked service setup
M
- machine learning
- overview / Machine learning overview
- algorithms / Machine learning algorithms
- tasks / Machine learning tasks
- used, for automated classification / Automated classification using machine learning
- modern data warehouse
- about / The modern data warehouse
- features / The modern data warehouse
- Kimball group data warehouse bus / Data warehouse
- Inmon CIF / Data warehouse
- Data Vault / Data warehouse
- cube / Cubes
- components / What is Azure Data Factory
- limitations / Limitations of ADF V1.0
- module palette / Work area
P
- pipelines
- executing, with scheduled trigger / Pipelines
- executing, with tumbling window trigger / Pipelines
- control activities / Activities
- Power BI
- reference / Self-service – personal, Team BI – sharing personal BI data
- reports, creating / Creating our Power BI reports
- Power BI consumption, methods
- web browser / Power BI consumption
- Power BI mobile / Power BI consumption
- Power BI embedded / Power BI consumption
- Power BI Report Server
- reference / Power BI Report Server
- principal component analysis (PCA) / Feature extraction
R
- regression analysis
- explanatory / Making predictions with regression algorithms
- predictive / Making predictions with regression algorithms
- reinforcement learning / Reinforcement learning
- replication type
- Local Redundant Storage (LRS) / Replication of storage
- Zone Redundant Storage (ZRS) / Replication of storage
- Geo-Redundant Storage (GRS) / Replication of storage
- Read-Access Geo-Redundant Storage (RA-GRS) / Replication of storage
- reports, Power BI
- creating / Creating our Power BI reports
- on-premise data sources, using / Reporting with on-premise data sources
- resource group
- creating / Resource group
- reference / Resource group
S
- self-hosted integration runtime / Self-hosted integration runtime
- self-service BI / Self-service BI
- Spark data
- incorporating / Incorporating Spark data
- SQL Azure database
- reference / SQL Azure database, Creating the Azure SQL Server
- about / SQL Azure database
- controlling / Creating the Azure SQL Server
- BACPAC, attaching to database / Attaching the BACPAC to our database
- data, copying with data factory / Copying data using our data factory
- SQL Server Analysis Services (SSAS) / Self-service – personal
- SQL Server dataset / SQL Server dataset
- SQL Server Data Tools (SSDT) / SSIS package deployment in Azure, Sample solution in Visual Studio
- SQL Server Integration Services (SSIS)
- about / SSIS in ADF
- sample setup / Sample setup
- sample databases / Sample databases
- components / SSIS components
- SQL Server Management Studio (SSMS)
- about / SSIS package deployment in Azure
- reference / Sample databases
- SSIS components
- integration services catalog setup / Integration services catalog setup
- sample solution, in Visual Studio / Sample solution in Visual Studio
- project on-premises, deploying / Deploying the project on-premises
- SSIS execution
- from pipeline / SSIS execution from a pipeline
- SSIS integration runtime
- adding, to factory / Adding an SSIS integration runtime to the factory
- standard classification process
- algorithm, training / Automated classification using machine learning
- algorithm, testing / Automated classification using machine learning
- supervised learning / Supervised learning
T
- tasks, machine learning
- about / Machine learning tasks
- predictions, creating with regression algorithms / Making predictions with regression algorithms
- training set / Supervised learning
- types, clusters
- interactive clusters / Azure Databricks setup
- job clusters / Azure Databricks setup
U
- U-SQL
- executing, from job in Data Lake Analytics / Run U-SQL from a job in the Data Lake Analytics
- U-SQL task
- executing, from Data Factory Pipeline for data summarization / Task 2 – run a U-SQL task from the data factory pipeline to summarize data
- unsupervised learning / Unsupervised learning
V
- Virtual Machines (VMs) / Page blobs
- virtual private network (VPN) / Integration runtime