As Azure offerings mature and Azure becomes less bleeding edge and more cutting edge, many of us are getting ready to entertain the idea of jumping on the Azure wagon. However, when I first started to wrap my head around Azure offerings, I needed to learn a few basics. Is anyone out there with me? For example:
1. How does my current infrastructure match up to current Azure technologies?
2. What is the difference between DWIs, DTUs and DSUs?
3. If I don’t have TBs of data, is Azure even for me?
If you are looking at Azure for the first time, I am going to assume for a moment that you may be just a tiny bit like me. So, let’s start with some basics. First comes a new set of acronyms!
Acronym & Link |
Definition |
The Short Version of “What is it?” |
Not an acronym |
This is the name for Microsoft's cloud computing platform |
|
Azure Data Factory |
A data integration service; used to migrate from on-premises to Azure DW |
|
Analytics Data Platform |
Formerly PDW |
|
Create External Table As SELECT |
How APS and Azure DW interact (query, export or import data) when working with Hadoop or Azure Storage Blobs |
|
Create Table As SELECT |
How APS and Azure DW interact (select, copy, move) data, create a columnstore index or choose a different hash distribution |
|
Database as a Service |
Often used in reference to Azure Database. See chart below. |
|
Data Management Gateway |
Used by Power BI and Data Factory to read or move data from on-premises data stores to the cloud |
|
DMS |
Data Movement Service |
A Windows service used by Azure DW to run each query in parallel |
Database Stretch Unit |
Used for pricing SQL Server Stretch Database |
|
Database Stretch Unit |
How compute usage is billed by Stretch Database |
|
Database Transaction Unit (Azure DB) |
Used for pricing Azure Database |
|
Data Warehouse Units (Azure DW) |
Used for pricing Azure Data Warehouse |
|
Elastic Database Transaction Unit |
Use for pricing Azure DB elastic pools (when multiple databases share a common set of resources) |
|
ETL |
Extract, Transform and Load |
A typical SMP way of data integration |
ELT |
Extract, Load and Transform |
A typical MPP way of data integration |
HDFS |
Hadoop Distributed File System |
Polybase is Microsoft’s bridge between Hadoop and SQL Server allowing you to use SSMS to access unstructured Hadoop (blob) storage. |
Infrastructure as a Service |
Often used in reference to SQL Server in Azure Virtual Machines |
|
Machine Learning |
Microsoft's cloud-based predictive analytics solution |
|
Massively Parallel Processing (APS and Azure DW) |
Architecture that sits under Microsoft's APS and Azure DW technologies; allows for processing TBs of data, both relational and non-relational; associated with column store indexes |
|
Platform as a Service |
Often used in reference to Azure Database. See chart below |
|
PDW |
Parallel Data Warehouse |
Now APS |
Software as a Service (Azure DBs) |
Often used in reference to Azure Database |
|
Symmetrically Parallel System (SQL Server 2016, Azure DB and SQL Server on Azure Virtual Machines) |
What we all know and love; think “row store” although column store arrived with SQL 2012. |
Next, let’s take a high level look at the difference between Azure DW (Data Warehouse) and Azure DB (Database). After all, we can currently store both OLAP and OLTP on the same SQL Server instance we have now, correct? Well, the difference is first hardware, but then also the method SQL Server uses to both store and retrieve data – this is the short version. You cannot take a current SQL Server 2015 ENT based data-anything and drop it into APS or Azure DW without first writing some CTAS scripts, considering your index strategy and (smartly) selecting a distribution key. Don’t let this discourage you, however. Please review the following table:
Azure Data Warehouse |
Azure Database |
OLAP |
OLTP |
MPP |
SMP |
Sold in DWIs (Data Warehouse Units) |
Sold in DTUs (Database Transaction Units) |
1 DWU = |
7.5 DTUs |
Optimized for analytics |
Optimized for SaaS App Development |
Limited t-sql features |
All t-sql features |
Migrate from APS (Analytics Platform System) formerly known as PDW (Parallel Data Warehouse) |
Migrate from on-premises SQL Server Databases (including those OLAP databases smaller than 1 TB) |
Billed Hourly |
Billed Hourly |
Decrease costs by pausing compute resources whereby not paying when not needed. |
Decrease costs by adjusting performance and scale, but you cannot pause it |
Think “column store” |
Think “row store” although column store indexes did become available beginning in SQL 2012 |
CTAS() or CETAS() |
INSERT INTO… |
Choose this when you have > 1 TB of data or unstructured data |
Choose this when you have < 1 TB of data or when do not need the added benefit of MPP. |
There is a pretty good write-up by MSDN comparing the differences between Azure DB and SQL Server in Azure VMs <click here>, so I won’t reiterate it. Assuming that the reader does not have an APS system humming in their back computer room, right now and for the majority of us, a good place to start is with a small Azure Database. I am going to pretend to be a Nike commercial this year and “Just do it!” This is not a marriage, after all, although now that I have said it, Azure DB does have some limitations. Read about them here, but this is not an excuse not to try!
If you want help beginning, you know who to call. (No… not Azure Busters!) Reach out to Pragmatic Works at www.pragmaticworks.com, or contact our sales team at sales@pragmaticworks.com.
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