There has been much talk recently in the SQL Server community about writing introductory level blog posts. Posts that a new person just starting a career as a data professional using Microsoft's SQL Server data platform could use to learn all about the product. This made me think I about writing  a series of introductory posts covering some of the basics.

Usually everyone's first thought when he or she think about is SQL Server, is it’s a database platform. Its used by businesses and organisations to structure and store its important data and used to support applications and services that support the organisation. This is very true,  SQL Server does have a very powerful and ever evolving database engine. However there is much more that falls under the 'SQL Server' umbrella than just the database engine.

Database Engine

The core of the SQL Server family is the database engine  and this what most people think of when discussing SQL Server. Microsoft SQL Server is very powerful, scalable and versatile relational database management system (RDBMS) providing a data platform for a multitude of applications and systems.
From an end user perspective they probably don't think of the database, it’s not something they would work on directly, instead they would utilise an application that connects to a database to store their important data.

The DBMS is responsible for enforcing the database structure, ensuring the data is stored correctly, the relationships between the tables are maintained and any rules in place to protect the data are enforced. It also provides the ability to recover from errors and failures of the system.

SQL Server is a relational database management system.
You may hear talk of other types of databases and data platforms; things like Hadoop and Big Data, NoSQL databases such as MongoDB and Cassandra but relational database management systems are still the most popular type of data platform.
In the latest version of SQL Server, SQL Server 2016, there has been work to integrate and allow SQL Server to work more closely with the some of these other platforms but SQL Server is still first and foremost a relational database engine.

Business Intelligence

If you are new to the world of SQL Server and databases in general, you might hear reference to the SQL Server BI stack. There are many components to the BI stack but I would consider the core components to be:
•    Reporting Services (SSRS)
•    Integration Services (SSIS)
•    Analysis Services (SSAS)

Reporting Services (SSRS)

SQL Server Reporting Services is a reporting tool. It allows you to query a database to extract data and present that data in  a nice readable format. SSRS provides a variety of tools for report authoring, in later posts we will look at creating and authoring reports.
SSRS also gives us a web based application (or it can be integrated with SharePoint) to deliver these reports to our end users and consumers of the data. SSRS can report on data stored in a multitude of different data sources including non-SQL Server databases such as Oracle and even Analysis Services.

Analysis Services (SSAS)

Analysis Services is Microsoft Online Analytical Processing server (OLAP). It allows you to build a dimensional model in a cube using standard techniques and modelling using things like dimension and fact tables (more about that later). The data is pre-processed and should help you answer analytical questions like “How many sales did we have a given on a given date?”  “Which product sold the best in a given region?”
Answering these questions by querying a relational database can take a long time because there will be many rows to 'crunch' through. Since our cubes are pre-processed the answer to the questions can provided more quickly.

Integration Services (SSIS)

Integration Services is Microsoft’s Extract Transform and Load (ETL) tool. It provides a mechanism to move, clean and load large volumes of data between different courses.  It can import and export data from a multitude of data sources and destinations. It provides a mechanism for changing or 'cleansing' the data while it is in transit before finally loading it into the destination. Again the data source and destination doesn't have to be a SQL Server database.

There are some other components that make up SQL Server such as:

• Master Data Services
• Data Quality Services
• R Integration
• Azure Cloud integration

These are probably all worth a mention here and will look at these in future posts.
Hopefully you now have a understanding of the main components that comprise SQL Server.

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