SQL Server Data Tools

SQL is chosen as one of the demanding databases that let the database administrators work effectively on fetching and maintaining the information.
SQL Server Data Tools (SSDT) supports the traditional Business Intelligence Development Studio (BIDS) project types for SQL Server Analysis Services (SSAS), Reporting Services (SSRS), and Integration Services (SSIS), in addition to the new database tooling. Here are some of the challenges that developers face when designing databases.

SQL - Amazing features in SQL Server 2016

Microsoft SQL Server 2016 is now generally available to everyone from Jun 2016. It is fully loaded with several new features, including more speed, more security and more value. It provides a secure, scalable database platform that has everything built in, from advanced analytics to unparalleled in-memory performance.

SQL - Real Time Operational Analytics
This is very highly recommended feature of SQL Server 2016 to do the real-time operational analytic. In this feature, you have to ability to run both analytics operations and OLTP workloads on the same database tables at the same time. If you are doing real time analytical operation on the same database (with OLTP workloads) then you have the ability to eliminate the need for ETL and a data warehouse.

With SQL Server 2016, the addition of new report types in SSRS is accompanied by an entirely new web experience for Reporting Services allowing users to access both paginated and mobile reports in one centralized location. By understanding of the business needs, the paginated reports, the interactive reports and the mobile reports are also integrated in SQL Server Reporting Services 2016 released. 

In current era of cloud and data virtualisation, data is coming from every directions which has been put tremendous pressure on the traditional data warehouse. So, we must have to understand the data warehouse to handle the exponentially growing volume of data, the variety of semi-structured and unstructured data types, and the velocity of real-time data processing.  The Microsoft modern data warehouse solution can easily integrate traditional data warehouse with unstructured big data and capable to handle data of all sizes and types, with real-time performance.

R Services is the latest feature of SQL Server 2016 which is fully loaded with accelerating innovations such as allowing building the smart applications with R, deploying intelligent applications that uncover new insight to retrieve predictions and visuals using Transact-SQL. Integration R with SQL Server means brings together the best of the two worlds where R Services support in-database analytics allowing users to keep the data in SQL Server 2016 and have applications call R scripts via a T-SQL system stored procedure. 
R scripts execute in a secure fashion on the SQL Server box so you are protected from R code trying to reach over to the Internet or to other jobs running on the SQL Server. Customers can benefit from the included ScaleR algorithm library, a set of functions that provide equivalents for routine tasks performed in R. Data Scientists have multiple tools for R such as R IDE, R Studio or R Tools for Visual Studio. To control resources and to assign permissions, Server Administrators can use the familiar SQL Server Management Studio to manage R Services.

Dependencies By its very nature, the database is full of dependencies between different kinds of schema objects. This complicates development, as even the simplest changes can very quickly become very complex when dependencies are involved.

Late Error Detection You can spend a lot of time building complex scripts, only to find out that there are problems when you try to deploy them to the database. Or, your script may deploy without errors, but you have an issue somewhere that doesn’t manifest itself until the user encounters a run-time error.

Drift“ Detection The database is a constantly moving target. After deployment, it’s fairly common for a DBA to come along and tweak or patch something in the production database; for example, adding indexes to improve query performance against particular tables. When the environments fall out of sync, the database is in a different state than you and your application expect it to be—and those differences need to be identified and reconciled.

Versioning Developers have grown so accustomed to working with change scripts that it makes you wonder, where the definition of the database is? Of course you can rely on it being in the database, but where is it from the standpoint of preserving and protecting it? How do you maintain the definition across different versions of your application? It’s very difficult to revert to a point in time and recall an earlier version of the database that matches up with an earlier version of an application. So you can’t easily synchronize versions and version history between your database and application.

Deployment Then there are the challenges of targeting different versions, including most recently, SQL Azure. You may need to deploy the same database out to different locations, and must account for varying compatibility levels when different locations are running different versions of SQL Server (such as SQL Server 2005, 2008, 2008 R2, 2012, and SQL Azure).
SQL Server 2014 delivers in-memory capabilities for OLTP and data warehousing as well as disaster recovery, backup, and hybrid architecture solutions with Microsoft Azure.
SQL Server 2016 delivers breakthrough mission critical performance, deeper insights across any data on many devices, and enables the power of hyper-scale cloud to unlock new hybrid scenarios.

SQL- In-Memory OLTP

In-Memory OLTP (Online Transaction Processing) is a standard feature that promises breakthroughs in performance and this feature was introduced with SQL Server 2014 with a big step forward in Microsoft's database capabilities. An in-memory table is used for high-scale, high-speed ingest and acts as a kind of data shock absorber where data can be buffered, transformed and delivered to a variety of applications and analyses.

SQL - Keywords, Identifiers, and Constants 
The ISO standard SQL language has many keywords. Some are designated as reserved words and others as non-reserved words. In ISO SQL, reserved words cannot be used as identifiers for database objects, such as tables, columns, and so on. They must be mapped to a formal representation, a logical model, which consists of a set of integrity constraints. An SQL statements which are made up of words that are keywords, identifiers, and constants. Avoid using ISO reserved keywords for object names and identifiers.  

SQL - Move SQL Server Database Files

Due to lack of space on the disk or database server relocation requirements are the main key points to move our data files from one drive location to the other location. Sometimes, we need to follow the standard best practises to secure our data from any disaster on the server and want to move our data files on the secured drive. 
Key Points- Before moving our data files from one drive location to another location, we should have done the following things- SQL - Move SQL Server Database Files

SQL – Tables

In relational databases and flat file databases, a table is a set of data elements (values) using a model of vertical columns (identifiable by name) and horizontal rows, the cell being the unit where a row and column intersect. A table has a specified number of columns, but can have any number of rows. Tables are uniquely identified by their names and are comprised of columns and rows. Columns contain the column name, data type, and any other attributes for the column.

SQL - Index Scan Vs Table Scan
In absence of the indexes, A table scan will work on the data pages and this scanning starts from the first page to the last page for the data and in this way, scanned table stands on a heap and these data rows have their own pages. In the table scan, every row of data goes into the data scanning. If we have the huge data in the table and no index is not there then the query execution cost will increase due to high volume table scan.

SQL - Queries Tuning and Optimization Techniques
In SQL, it is very difficult to write complex SQL queries involving joins across many (at least 3-4) tables and involving several nested conditions because a SQL statement, once it reaches a certain level of complexity, is basically a little program in and of itself. A database index is a data structure that improves the speed of operations on a database table.

SQL - Schema
A Schema is a collection of database objects which includes logical structures such as views, stored procedures, databases, triggers etc.. The most usable advantage is that the schema can be used to simplify managing permissions on tables and other objects. To understand schema in a glance such as a container of various type of objects and we can assign user login permissions to a single schema so that the user can only access the objects they are authorised to access. A database can consist of hundreds of schema and each schema can have hundreds of tables, stored procedures, databases, triggers.


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