Data warehousing in Microsoft Azure - Azure Architecture Center (2023)

A data warehouse is a centralized repository of integrated data from one or more disparate sources. Data warehouses store current and historical data and are used for reporting and analysis of the data.

Data warehousing in Microsoft Azure - Azure Architecture Center (1)

Download a Visio file of this architecture.

To move data into a data warehouse, data is periodically extracted from various sources that contain important business information. As the data is moved, it can be formatted, cleaned, validated, summarized, and reorganized. Alternatively, the data can be stored in the lowest level of detail, with aggregated views provided in the warehouse for reporting. In either case, the data warehouse becomes a permanent data store for reporting, analysis, and business intelligence (BI).

Data warehouse architectures

The following reference architectures show end-to-end data warehouse architectures on Azure:

  • Enterprise BI in Azure with Azure Synapse Analytics. This reference architecture implements an extract, load, and transform (ELT) pipeline that moves data from an on-premises SQL Server database into Azure Synapse.
  • Automated enterprise BI with Azure Synapse and Azure Data Factory. This reference architecture shows an ELT pipeline with incremental loading, automated using Azure Data Factory.

When to use this solution

Choose a data warehouse when you need to turn massive amounts of data from operational systems into a format that is easy to understand. Data warehouses don't need to follow the same terse data structure you may be using in your OLTP databases. You can use column names that make sense to business users and analysts, restructure the schema to simplify relationships, and consolidate several tables into one. These steps help guide users who need to create reports and analyze the data in BI systems, without the help of a database administrator (DBA) or data developer.

Consider using a data warehouse when you need to keep historical data separate from the source transaction systems for performance reasons. Data warehouses make it easy to access historical data from multiple locations, by providing a centralized location using common formats, keys, and data models.

Because data warehouses are optimized for read access, generating reports is faster than using the source transaction system for reporting.

Other benefits include:

  • The data warehouse can store historical data from multiple sources, representing a single source of truth.
  • You can improve data quality by cleaning up data as it is imported into the data warehouse.
  • Reporting tools don't compete with the transactional systems for query processing cycles. A data warehouse allows the transactional system to focus on handling writes, while the data warehouse satisfies the majority of read requests.
  • A data warehouse can consolidate data from different software.
  • Data mining tools can find hidden patterns in the data using automatic methodologies.
  • Data warehouses make it easier to provide secure access to authorized users, while restricting access to others. Business users don't need access to the source data, removing a potential attack vector.
  • Data warehouses make it easier to create business intelligence solutions, such as OLAP cubes.


Properly configuring a data warehouse to fit the needs of your business can bring some of the following challenges:

  • Committing the time required to properly model your business concepts. Data warehouses are information driven. You must standardize business-related terms and common formats, such as currency and dates. You also need to restructure the schema in a way that makes sense to business users but still ensures accuracy of data aggregates and relationships.

    (Video) Azure Tutorial || Azure Data warehouse solution || Part -1

  • Planning and setting up your data orchestration. Consider how to copy data from the source transactional system to the data warehouse, and when to move historical data from operational data stores into the warehouse.

  • Maintaining or improving data quality by cleaning the data as it is imported into the warehouse.

Data warehousing in Azure

You may have one or more sources of data, whether from customer transactions or business applications. This data is traditionally stored in one or more OLTP databases. The data could be persisted in other storage mediums such as network shares, Azure Storage Blobs, or a data lake. The data could also be stored by the data warehouse itself or in a relational database such as Azure SQL Database. The purpose of the analytical data store layer is to satisfy queries issued by analytics and reporting tools against the data warehouse. In Azure, this analytical store capability can be met with Azure Synapse, or with Azure HDInsight using Hive or Interactive Query. In addition, you will need some level of orchestration to move or copy data from data storage to the data warehouse, which can be done using Azure Data Factory or Oozie on Azure HDInsight.

There are several options for implementing a data warehouse in Azure, depending on your needs. The following lists are broken into two categories, symmetric multiprocessing (SMP) and massively parallel processing (MPP).


  • Azure SQL Database
  • SQL Server in a virtual machine


  • Azure Synapse Analytics (formerly Azure Data Warehouse)
  • Apache Hive on HDInsight
  • Interactive Query (Hive LLAP) on HDInsight

As a general rule, SMP-based warehouses are best suited for small to medium data sets (up to 4-100 TB), while MPP is often used for big data. The delineation between small/medium and big data partly has to do with your organization's definition and supporting infrastructure. (See Choosing an OLTP data store.)

Beyond data sizes, the type of workload pattern is likely to be a greater determining factor. For example, complex queries may be too slow for an SMP solution, and require an MPP solution instead. MPP-based systems usually have a performance penalty with small data sizes, because of how jobs are distributed and consolidated across nodes. If your data sizes already exceed 1 TB and are expected to continually grow, consider selecting an MPP solution. However, if your data sizes are smaller, but your workloads are exceeding the available resources of your SMP solution, then MPP may be your best option as well.

The data accessed or stored by your data warehouse could come from a number of data sources, including a data lake, such as Azure Data Lake Storage. For a video session that compares the different strengths of MPP services that can use Azure Data Lake, see Azure Data Lake and Azure Data Warehouse: Applying Modern Practices to Your App.

SMP systems are characterized by a single instance of a relational database management system sharing all resources (CPU/Memory/Disk). You can scale up an SMP system. For SQL Server running on a VM, you can scale up the VM size. For Azure SQL Database, you can scale up by selecting a different service tier.

MPP systems can be scaled out by adding more compute nodes (which have their own CPU, memory, and I/O subsystems). There are physical limitations to scaling up a server, at which point scaling out is more desirable, depending on the workload. However, the differences in querying, modeling, and data partitioning mean that MPP solutions require a different skill set.

(Video) Enterprise BI with SQL Data Warehouse and Azure Data Factory

When deciding which SMP solution to use, see A closer look at Azure SQL Database and SQL Server on Azure VMs.

Azure Synapse (formerly Azure SQL Data Warehouse) can also be used for small and medium datasets, where the workload is compute and memory intensive. Read more about Azure Synapse patterns and common scenarios:

Key selection criteria

To narrow the choices, start by answering these questions:

  • Do you want a managed service rather than managing your own servers?

  • Are you working with extremely large data sets or highly complex, long-running queries? If yes, consider an MPP option.

  • For a large data set, is the data source structured or unstructured? Unstructured data may need to be processed in a big data environment such as Spark on HDInsight, Azure Databricks, Hive LLAP on HDInsight, or Azure Data Lake Analytics. All of these can serve as ELT (Extract, Load, Transform) and ETL (Extract, Transform, Load) engines. They can output the processed data into structured data, making it easier to load into Azure Synapse or one of the other options. For structured data, Azure Synapse has a performance tier called Optimized for Compute, for compute-intensive workloads requiring ultra-high performance.

  • Do you want to separate your historical data from your current, operational data? If so, select one of the options where orchestration is required. These are standalone warehouses optimized for heavy read access, and are best suited as a separate historical data store.

    (Video) Introducing the modern data warehouse solution pattern with Azure SQL Data Warehouse

  • Do you need to integrate data from several sources, beyond your OLTP data store? If so, consider options that easily integrate multiple data sources.

  • Do you have a multitenancy requirement? If so, Azure Synapse is not ideal for this requirement. For more information, see Azure Synapse Patterns and Anti-Patterns.

  • Do you prefer a relational data store? If so, choose an option with a relational data store, but also note that you can use a tool like PolyBase to query non-relational data stores if needed. If you decide to use PolyBase, however, run performance tests against your unstructured data sets for your workload.

  • Do you have real-time reporting requirements? If you require rapid query response times on high volumes of singleton inserts, choose an option that supports real-time reporting.

  • Do you need to support a large number of concurrent users and connections? The ability to support a number of concurrent users/connections depends on several factors.

    • For Azure SQL Database, refer to the documented resource limits based on your service tier.

    • SQL Server allows a maximum of 32,767 user connections. When running on a VM, performance will depend on the VM size and other factors.

    • Azure Synapse has limits on concurrent queries and concurrent connections. For more information, see Concurrency and workload management in Azure Synapse. Consider using complementary services, such as Azure Analysis Services, to overcome limits in Azure Synapse.

  • What sort of workload do you have? In general, MPP-based warehouse solutions are best suited for analytical, batch-oriented workloads. If your workloads are transactional by nature, with many small read/write operations or multiple row-by-row operations, consider using one of the SMP options. One exception to this guideline is when using stream processing on an HDInsight cluster, such as Spark Streaming, and storing the data within a Hive table.

Capability Matrix

The following tables summarize the key differences in capabilities.

General capabilities

CapabilityAzure SQL DatabaseSQL Server (VM)Azure SynapseApache Hive on HDInsightHive LLAP on HDInsight
Is managed serviceYesNoYesYes 1Yes 1
Requires data orchestration (holds copy of data/historical data)NoNoYesYesYes
Easily integrate multiple data sourcesNoNoYesYesYes
Supports pausing computeNoNoYesNo 2No 2
Relational data storeYesYesYesNoNo
Real-time reportingYesYesNoNoYes
Flexible backup restore pointsYesYesNo 3Yes 4Yes 4

[1] Manual configuration and scaling.

(Video) Microsoft's Modern Data Warehouse Architecture

[2] HDInsight clusters can be deleted when not needed, and then re-created. Attach an external data store to your cluster so your data is retained when you delete your cluster. You can use Azure Data Factory to automate your cluster's lifecycle by creating an on-demand HDInsight cluster to process your workload, then delete it once the processing is complete.

[3] With Azure Synapse, you can restore a database to any available restore point within the last seven days. Snapshots start every four to eight hours and are available for seven days. When a snapshot is older than seven days, it expires and its restore point is no longer available.

[4] Consider using an external Hive metastore that can be backed up and restored as needed. Standard backup and restore options that apply to Blob Storage or Data Lake Storage can be used for the data, or third-party HDInsight backup and restore solutions, such as Imanis Data can be used for greater flexibility and ease of use.

Scalability capabilities

CapabilityAzure SQL DatabaseSQL Server (VM)Azure SynapseApache Hive on HDInsightHive LLAP on HDInsight
Redundant regional servers for high availabilityYesYesYesNoNo
Supports query scale out (distributed queries)NoNoYesYesYes
Dynamic scalabilityYesNoYes 1NoNo
Supports in-memory caching of dataYesYesYesYesYes

[1] Azure Synapse allows you to scale up or down by adjusting the number of data warehouse units (DWUs). See Manage compute power in Azure Synapse.

Security capabilities

CapabilityAzure SQL DatabaseSQL Server in a virtual machineAzure SynapseApache Hive on HDInsightHive LLAP on HDInsight
AuthenticationSQL / Azure Active Directory (Azure AD)SQL / Azure AD / Active DirectorySQL / Azure ADlocal / Azure AD 1local / Azure AD 1
AuthorizationYesYesYesYesYes 1
AuditingYesYesYesYesYes 1
Data encryption at restYes 2Yes 2Yes 2Yes 2Yes 1
Row-level securityYesYesYesNoYes 1
Supports firewallsYesYesYesYesYes 3
Dynamic data maskingYesYesYesNoYes 1

[1] Requires using a domain-joined HDInsight cluster.

[2] Requires using Transparent Data Encryption (TDE) to encrypt and decrypt your data at rest.

[3] Supported when used within an Azure Virtual Network.


This article is maintained by Microsoft. It was originally written by the following contributors.

Principal author:

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Next steps

Read more about securing your data warehouse:

(Video) Design & Deploy Modern Data Warehouses using Azure Synapse Analytics

  • Securing your SQL Database
  • Secure a database in Azure Synapse
  • Extend Azure HDInsight using an Azure Virtual Network
  • Enterprise-level Hadoop security with domain-joined HDInsight clusters
  • Enterprise BI in Azure with Azure Synapse Analytics
  • Automated enterprise BI with Azure Synapse and Azure Data Factory
  • Logical data warehouse with Azure Synapse serverless SQL pools
  • Enterprise data warehouse


What is the data warehouse in Azure? ›

A data warehouse is a centralized repository of integrated data from one or more disparate sources. Data warehouses store current and historical data and are used for reporting and analysis of the data.

Which service would you use to create data warehouse in Azure? ›

Azure SQL Database is an intelligent, scalable, relational database service built for the cloud. In this solution, SQL Database holds the enterprise data warehouse and performs ETL/ELT activities that use stored procedures.

What are the 3 data warehouse architectures? ›

Data Warehouses usually have a three-level (tier) architecture that includes: Bottom Tier (Data Warehouse Server) Middle Tier (OLAP Server) Top Tier (Front end Tools).

Which database architecture is typically used for data warehouses? ›

Three-Tier Data Warehouse Architecture

This is the most widely used Architecture of Data Warehouse. It consists of the Top, Middle and Bottom Tier. Bottom Tier: The database of the Datawarehouse servers as the bottom tier.

What are the 5 components of data warehouse? ›

What are the key components of a data warehouse? A typical data warehouse has four main components: a central database, ETL (extract, transform, load) tools, metadata, and access tools. All of these components are engineered for speed so that you can get results quickly and analyze data on the fly.

Which data warehouse architecture is best? ›

Advantages of Top-Down Approach

Since the data marts are created from the datawarehouse, provides consistent dimensional view of data marts. Also, this model is considered as the strongest model for business changes.

What is the difference between Azure SQL and Azure data warehouse? ›

Azure SQL Database also scales for OLTP, as different pricing tiers typically scale to give you more query throughput and not so much data (the current maximum is 1TB, and in some regions 4TB). Azure SQL Data Warehouse is optimized for performing data analytics tasks, and working with large amounts of data.

What is data warehousing architecture? ›

A data warehouse architecture is a method of defining the overall architecture of data communication processing and presentation that exist for end-clients computing within the enterprise. Each data warehouse is different, but all are characterized by standard vital components.

How many data warehouse models are there from the architecture? ›

From the architecture point of view, there are three data warehouse models: the enterprise warehouse, the data mart, and the virtual warehouse.

What is data warehouse explain 3 tier architecture of data warehouse? ›

The Three-Tier Data Warehouse Architecture is the commonly used Data Warehouse design in order to build a Data Warehouse by including the required Data Warehouse Schema Model, the required OLAP server type, and the required front-end tools for Reporting or Analysis purposes, which as the name suggests contains three ...

What are the 3 characteristics of data warehouse? ›

Characteristics Of A Data Warehouse. The four characteristics of a data warehouse, also called features of a data warehouse, include SUBJECT ORIENTED, TIME VARIANT, INTEGRATED and NON-VOLATILE. The three prominent ones among these are. INTEGRATED, TIME VARIANT, NON VOLATILE.

What type of system is Azure SQL data warehouse? ›

Microsoft Azure SQL Data Warehouse is a relational database management system developed by Microsoft.

What is difference between data warehouse and database? ›

What is a database vs. a data warehouse? A database stores the current data required to power an application whereas a data warehouse stores current and historical data for one or more systems in a predefined and fixed schema for the purpose of analyzing the data.

What is the main principle of data warehousing? ›

First Data Warehouse Principle: Data Quality Reigns Supreme

Data warehouses are only useful and valuable to the extent that the data within is trusted by the business stakeholders. To ensure this, frameworks that automatically capture and correct (where possible) data quality issues have to be built.

How ETL works in data warehouse? ›

ETL, which stands for extract, transform and load, is a data integration process that combines data from multiple data sources into a single, consistent data store that is loaded into a data warehouse or other target system.

What is data warehousing with example? ›

Data Warehousing integrates data and information collected from various sources into one comprehensive database. For example, a data warehouse might combine customer information from an organization's point-of-sale systems, its mailing lists, website, and comment cards.

Is Azure SQL data warehouse? ›

Azure SQL Data Warehouse is a managed Data Warehouse-as-a Service (DWaaS) offering provided by Microsoft Azure. A data warehouse is a federated repository for data collected by an enterprise's operational systems. Data systems emphasize the capturing of data from different sources for both access and analysis.

Does Azure have an ETL tool? ›

With Azure Data Factory, it's fast and easy to build code-free or code-centric ETL and ELT processes.

What happened to Azure data warehouse? ›

On November fourth, we announced Azure Synapse Analytics, the next evolution of Azure SQL Data Warehouse. Azure Synapse is a limitless analytics service that brings together enterprise data warehousing and Big Data analytics.

Which is the most popular data model for a data warehouse? ›

Star schema data model is widely used to develop or build a data warehouse and dimensional data marts.

Is Azure data warehouse a relational database? ›

Azure SQL Data Warehouse (SQL DW) is a cloud-based Platform-as-a-Service (PaaS) offering from Microsoft. It is a large-scale, distributed, MPP (massively parallel processing) relational database technology in the same class of competitors as Amazon Redshift or Snowflake.

What is the difference between Azure Data Lake and Azure data warehouse? ›

A data lake contains all an organization's data in a raw, unstructured form, and can store the data indefinitely — for immediate or future use. A data warehouse contains structured data that has been cleaned and processed, ready for strategic analysis based on predefined business needs.

Is Azure SQL OLTP or OLAP? ›

In Azure, data held in OLTP systems such as Azure SQL Database is copied into the OLAP system, such as Azure Analysis Services.

What are the 3 layers in ETL? ›

ETL stands for Extract, Transform, and Load.

What are the two types of schemas used in the data warehouse? ›

In a data warehouse, a schema is used to define the way to organize the system with all the database entities (fact tables, dimension tables) and their logical association. Here are the different types of Schemas in DW: Star Schema. SnowFlake Schema.

What are the 3 components of 3 tier architectures? ›

A 3-tier application architecture is a modular client-server architecture that consists of a presentation tier, an application tier and a data tier.

What is OLAP in data warehousing? ›

What is OLAP? OLAP (for online analytical processing) is software for performing multidimensional analysis at high speeds on large volumes of data from a data warehouse, data mart, or some other unified, centralized data store.

What are the 3 types of SQL database server architecture? ›

Three primary components make up SQL Server architecture: Protocol Layer, Relational Engine, and Storage Engine.

How many types of databases are in Azure? ›

Azure offers a choice of fully managed relational, NoSQL, and in-memory databases, spanning proprietary and open-source engines, to fit the needs of modern app developers. Infrastructure management—including scalability, availability, and security—is automated, saving you time and money.

What is the difference between SQL database and SQL data warehouse? ›

What are the differences between a database and a data warehouse? A database is any collection of data organized for storage, accessibility, and retrieval. A data warehouse is a type of database the integrates copies of transaction data from disparate source systems and provisions them for analytical use.

Why use Azure data warehouse? ›

The advantages that come with Azure SQL Data Warehouse include: Cost effective pay-as-you-go model when compared to the cost of an organization implementing their own enterprise-level data warehouse. Leverages Azure cloud compute and storage resources. Scalable compute power.

What is data warehouse explain? ›

A data warehouse is a central repository of information that can be analyzed to make more informed decisions. Data flows into a data warehouse from transactional systems, relational databases, and other sources, typically on a regular cadence.

What is data warehouse with example? ›

Data Warehousing integrates data and information collected from various sources into one comprehensive database. For example, a data warehouse might combine customer information from an organization's point-of-sale systems, its mailing lists, website, and comment cards.

What is data warehouse in architecture? ›

A data warehouse is a collection of databases that stores and organizes data in a systematic way. A data warehouse architecture consists of three main components: a data warehouse, an analytical framework, and an integration layer. The data warehouse is the central repository for all the data.

Is Azure data Factory an ETL? ›

With Azure Data Factory, it's fast and easy to build code-free or code-centric ETL and ELT processes. In this scenario, learn how to create code-free pipelines within an intuitive visual environment. In today's data-driven world, big data processing is a critical task for every organization.

What is difference between database and datawarehouse? ›

A database stores the current data required to power an application whereas a data warehouse stores current and historical data for one or more systems in a predefined and fixed schema for the purpose of analyzing the data.

Is SQL a data warehouse? ›

SQL Data Warehouse is a cloud-based Enterprise Data Warehouse (EDW) that leverages Massively Parallel Processing (MPP) to quickly run complex queries across petabytes of data. Use SQL Data Warehouse as a key component of a big data solution.


1. Modern Data Warehouse in Azure - episode 2 - Storage and Data Warehouse
2. Selecting a Data Warehousing Technology in the Azure Cloud
3. Building Your First Azure SQL Data Warehouse
(Pragmatic Works)
4. Database vs Data Warehouse vs Data Lake | What is the Difference?
(Alex The Analyst)
5. Jean Joseph: Building End-To-End Modern Data Warehouse with Azure Synapse Analytics
(Data Toboggan)
6. Moving your Data Warehouse to the Cloud | DevOps Lab
(DevOps on Azure)
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