Wilford  Pagac

Wilford Pagac

1609229017

SQL vs NoSQL

They are not the same rather different types of databases

Whether there’s a new framework or a language to learn, innovation and adaptation are fundamental to development. One of the most interesting topics for today is the standard SQL database or NoSQL database.

NoSQL databases have been around for decades. At this time, companies like Google and Amazon began putting more effort into developing NoSQL databases. Many developers, architects, and designers may not be fully up to date with what NoSQL databases offer.

Thus, for an outline of the differences between the two databases, check out the following 5 key takeaways that every developer should know.

So, without any further due let’s get started!!!

  • Use of Structured Query Language
  • Relational vs Non-Relational
  • Defined outline vs Dynamic outline
  • Table vs Field
  • Use Cases





#nosql #sql #database #developer

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SQL vs NoSQL
Cayla  Erdman

Cayla Erdman

1594369800

Introduction to Structured Query Language SQL pdf

SQL stands for Structured Query Language. SQL is a scripting language expected to store, control, and inquiry information put away in social databases. The main manifestation of SQL showed up in 1974, when a gathering in IBM built up the principal model of a social database. The primary business social database was discharged by Relational Software later turning out to be Oracle.

Models for SQL exist. In any case, the SQL that can be utilized on every last one of the major RDBMS today is in various flavors. This is because of two reasons:

1. The SQL order standard is genuinely intricate, and it isn’t handy to actualize the whole standard.

2. Every database seller needs an approach to separate its item from others.

Right now, contrasts are noted where fitting.

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Juanita  Apio

Juanita Apio

1623180540

SQL vs NoSQL and SQL to NoSQL Migration

Overview

Given the choice of a Relational Database (RDBMS) vs a NoSQL database, it has become more important to select the right type of database for storing data. Not all the requirements fit in a NoSQL database or an RDBMS. RDBMSs are mainly related to managing, storing, and manipulating structured data where the data format, columns, data type, attributes, and schema are fixed, and the relationship between entities needs to be consistently maintained.

SQL is a common query language used when dealing with an RDBMS. Using an RDBMS is a choice for storing transactional data or records where the ACID (Atomicity, Consistency, Isolation, Durability) proprieties of transactions must be provided by an underlying database. An RDBMS is also a choice where the security and accessibility of data are of utmost importance. Typical use cases are financial records, financial transactions, OLTP, ERP, CRM systems, e-commerce applications, etc.

NoSQL (sometimes referred to as Not only SQL, non-SQL or non-relational) is a database that is suitable for managing data that is non-relational, i.e. not structured in tabular format or have fixed data type formats and variables that do not possess tabular relationships. There are various types of NoSQL databases that exist, like key-value, document-based, column-based, and graph-based. When it comes to scalability and performance of unstructured data, NoSQL is the obvious choice.

In a recent development, a few graph databases provide the options to store transactions adhering to ACID properties, but they are still in the early phases of adoption. Typical uses cases of NoSQL include data that is largely unstructured and needs flexibility in data models like content management, personalization, web search engines, storing large users profiles from heterogeneous sources, data streams, documents, digital communication (Storing messages, chats), big data, analytics, machine learning, and storing IoT data.

It becomes imperative to choose the right type of database, and, if required, migrate the exiting RDBMS database to NoSQL to meet the new dynamics of business requirements, scalability, and performance aspects. The below section will help in deciding the right database for your requirement.

1. Database Decision Tree

2. Migration From RDBMS Data Sources to NoSQL DBs

If there are existing RDBMS databases that are storing content, documents, files, or have unstructured data, then there are significant advantages in moving such databases to NoSQL databases. Benefits include cost benefits, performance, scalability, future proof for changes, reducing conversion jobs, and extensive supportability for analytics.

2.1 Migration Tools From Traditional RDBMS DB to AWS DynamoDB

AWS Database Migration Service (AWS DMS) can migrate data from most widely-used commercial RDBMSs and open-source databases to similar databases (homogeneous) or different database platforms (heterogeneous) including transforming RDBMS to DynamoDB or Cassandra to DynamoDB or MongoDB to DynamoDB databases.

2.2 Migration Tools for RDBMS DB to Azure Cosmos DB

The Azure Cosmos DB emulator and the Azure Cosmos DB Data Migration tool can be used to migrate from an MSSQL database to Cosmos DB. This tool can also help to migrate if the source data is in CSV or JSON object formats.

Azure Database Migration Service (DMS), Cosmos DB, and the API for MongoDB can be used to migrate MongoDB to CosmosDB.

2.3 Migration From SQL to Google DataStore

Google has NoSQL services, Cloud Datastore, and Bigtable. Cloud Datastore is now being enhanced to recently released service called Firestore.

There are not many tools and documentation support from Google on migration to Firestore or Bigtable from RDBMS databases or different NoSQL platforms. Cloud SQL, a managed RDBMS from Google, has built-in features to migrate some of traditional RDBMS to CloudSQL. Cloud Spanner is another managed RDBMS. Migrations involve mainly manual processes.

#nosql #aws #azure #sql #nosql

Cayla  Erdman

Cayla Erdman

1596441660

Welcome Back the T-SQL Debugger with SQL Complete – SQL Debugger

When you develop large chunks of T-SQL code with the help of the SQL Server Management Studio tool, it is essential to test the “Live” behavior of your code by making sure that each small piece of code works fine and being able to allocate any error message that may cause a failure within that code.

The easiest way to perform that would be to use the T-SQL debugger feature, which used to be built-in over the SQL Server Management Studio tool. But since the T-SQL debugger feature was removed completely from SQL Server Management Studio 18 and later editions, we need a replacement for that feature. This is because we cannot keep using the old versions of SSMS just to support the T-SQL Debugger feature without “enjoying” the new features and bug fixes that are released in the new SSMS versions.

If you plan to wait for SSMS to bring back the T-SQL Debugger feature, vote in the Put Debugger back into SSMS 18 to ask Microsoft to reintroduce it.

As for me, I searched for an alternative tool for a T-SQL Debugger SSMS built-in feature and found that Devart company rolled out a new T-SQL Debugger feature to version 6.4 of SQL – Complete tool. SQL Complete is an add-in for Visual Studio and SSMS that offers scripts autocompletion capabilities, which help develop and debug your SQL database project.

The SQL Debugger feature of SQL Complete allows you to check the execution of your scripts, procedures, functions, and triggers step by step by adding breakpoints to the lines where you plan to start, suspend, evaluate, step through, and then to continue the execution of your script.

You can download SQL Complete from the dbForge Download page and install it on your machine using a straight-forward installation wizard. The wizard will ask you to specify the installation path for the SQL Complete tool and the versions of SSMS and Visual Studio that you plan to install the SQL Complete on, as an add-in, from the versions that are installed on your machine, as shown below:

Once SQL Complete is fully installed on your machine, the dbForge SQL Complete installation wizard will notify you of whether the installation was completed successfully or the wizard faced any specific issue that you can troubleshoot and fix easily. If there are no issues, the wizard will provide you with an option to open the SSMS tool and start using the SQL Complete tool, as displayed below:

When you open SSMS, you will see a new “Debug” tools menu, under which you can navigate the SQL Debugger feature options. Besides, you will see a list of icons that will be used to control the debug mode of the T-SQL query at the leftmost side of the SSMS tool. If you cannot see the list, you can go to View -> Toolbars -> Debugger to make these icons visible.

During the debugging session, the SQL Debugger icons will be as follows:

The functionality of these icons within the SQL Debugger can be summarized as:

  • Adding Breakpoints to control the execution pause of the T-SQL script at a specific statement allows you to check the debugging information of the T-SQL statements such as the values for the parameters and the variables.
  • Step Into is “navigate” through the script statements one by one, allowing you to check how each statement behaves.
  • Step Over is “execute” a specific stored procedure if you are sure that it contains no error.
  • Step Out is “return” from the stored procedure, function, or trigger to the main debugging window.
  • Continue executing the script until reaching the next breakpoint.
  • Stop Debugging is “terminate” the debugging session.
  • Restart “stop and start” the current debugging session.

#sql server #sql #sql debugger #sql server #sql server stored procedure #ssms #t-sql queries

Chesley  Wehner

Chesley Wehner

1618981560

Which is better for Big Data: SQL or NoSQL

In this tutorial, we will learn all about SQL and NoSQL databases.

What is Big Data?

Big data refers to the huge volume of data being produced on the day to day basis. It is the data that is very large and complex with an enormous speed of generation.

The act of collecting, storing, and utilizing data for analysis and drawing insights has been in practice for quite a long time. But the term Big Data came into existence in the late 2000s.

Big data depend on three V’s which are as follows:

  • Volume
  • Velocity
  • Variety

The importance of data or big data lies in the fact that what you can do with it and not the amount of data you have.

#sql vs nosql #sql #nosql

Cayla  Erdman

Cayla Erdman

1596448980

The Easy Guide on How to Use Subqueries in SQL Server

Let’s say the chief credit and collections officer asks you to list down the names of people, their unpaid balances per month, and the current running balance and wants you to import this data array into Excel. The purpose is to analyze the data and come up with an offer making payments lighter to mitigate the effects of the COVID19 pandemic.

Do you opt to use a query and a nested subquery or a join? What decision will you make?

SQL Subqueries – What Are They?

Before we do a deep dive into syntax, performance impact, and caveats, why not define a subquery first?

In the simplest terms, a subquery is a query within a query. While a query that embodies a subquery is the outer query, we refer to a subquery as the inner query or inner select. And parentheses enclose a subquery similar to the structure below:

SELECT 
 col1
,col2
,(subquery) as col3
FROM table1
[JOIN table2 ON table1.col1 = table2.col2]
WHERE col1 <operator> (subquery)

We are going to look upon the following points in this post:

  • SQL subquery syntax depending on different subquery types and operators.
  • When and in what sort of statements one can use a subquery.
  • Performance implications vs. JOINs.
  • Common caveats when using SQL subqueries.

As is customary, we provide examples and illustrations to enhance understanding. But bear in mind that the main focus of this post is on subqueries in SQL Server.

Now, let’s get started.

Make SQL Subqueries That Are Self-Contained or Correlated

For one thing, subqueries are categorized based on their dependency on the outer query.

Let me describe what a self-contained subquery is.

Self-contained subqueries (or sometimes referred to as non-correlated or simple subqueries) are independent of the tables in the outer query. Let me illustrate this:

-- Get sales orders of customers from Southwest United States 
-- (TerritoryID = 4)

USE [AdventureWorks]
GO
SELECT CustomerID, SalesOrderID
FROM Sales.SalesOrderHeader
WHERE CustomerID IN (SELECT [CustomerID]
                     FROM [AdventureWorks].[Sales].[Customer]
                     WHERE TerritoryID = 4)

As demonstrated in the above code, the subquery (enclosed in parentheses below) has no references to any column in the outer query. Additionally, you can highlight the subquery in SQL Server Management Studio and execute it without getting any runtime errors.

Which, in turn, leads to easier debugging of self-contained subqueries.

The next thing to consider is correlated subqueries. Compared to its self-contained counterpart, this one has at least one column being referenced from the outer query. To clarify, I will provide an example:

USE [AdventureWorks]
GO
SELECT DISTINCT a.LastName, a.FirstName, b.BusinessEntityID
FROM Person.Person AS p
JOIN HumanResources.Employee AS e ON p.BusinessEntityID = e.BusinessEntityID
WHERE 1262000.00 IN
    (SELECT [SalesQuota]
    FROM Sales.SalesPersonQuotaHistory spq
    WHERE p.BusinessEntityID = spq.BusinessEntityID)

Were you attentive enough to notice the reference to BusinessEntityID from the Person table? Well done!

Once a column from the outer query is referenced in the subquery, it becomes a correlated subquery. One more point to consider: if you highlight a subquery and execute it, an error will occur.

And yes, you are absolutely right: this makes correlated subqueries pretty harder to debug.

To make debugging possible, follow these steps:

  • isolate the subquery.
  • replace the reference to the outer query with a constant value.

Isolating the subquery for debugging will make it look like this:

SELECT [SalesQuota]
    FROM Sales.SalesPersonQuotaHistory spq
    WHERE spq.BusinessEntityID = <constant value>

Now, let’s dig a little deeper into the output of subqueries.

Make SQL Subqueries With 3 Possible Returned Values

Well, first, let’s think of what returned values can we expect from SQL subqueries.

In fact, there are 3 possible outcomes:

  • A single value
  • Multiple values
  • Whole tables

Single Value

Let’s start with single-valued output. This type of subquery can appear anywhere in the outer query where an expression is expected, like the WHERE clause.

-- Output a single value which is the maximum or last TransactionID
USE [AdventureWorks]
GO
SELECT TransactionID, ProductID, TransactionDate, Quantity
FROM Production.TransactionHistory
WHERE TransactionID = (SELECT MAX(t.TransactionID) 
                       FROM Production.TransactionHistory t)

When you use a MAX() function, you retrieve a single value. That’s exactly what happened to our subquery above. Using the equal (=) operator tells SQL Server that you expect a single value. Another thing: if the subquery returns multiple values using the equals (=) operator, you get an error, similar to the one below:

Msg 512, Level 16, State 1, Line 20
Subquery returned more than 1 value. This is not permitted when the subquery follows =, !=, <, <= , >, >= or when the subquery is used as an expression.

Multiple Values

Next, we examine the multi-valued output. This kind of subquery returns a list of values with a single column. Additionally, operators like IN and NOT IN will expect one or more values.

-- Output multiple values which is a list of customers with lastnames that --- start with 'I'

USE [AdventureWorks]
GO
SELECT [SalesOrderID], [OrderDate], [ShipDate], [CustomerID]
FROM Sales.SalesOrderHeader
WHERE [CustomerID] IN (SELECT c.[CustomerID] FROM Sales.Customer c
INNER JOIN Person.Person p ON c.PersonID = p.BusinessEntityID
WHERE p.lastname LIKE N'I%' AND p.PersonType='SC')

Whole Table Values

And last but not least, why not delve into whole table outputs.

-- Output a table of values based on sales orders
USE [AdventureWorks]
GO
SELECT [ShipYear],
COUNT(DISTINCT [CustomerID]) AS CustomerCount
FROM (SELECT YEAR([ShipDate]) AS [ShipYear], [CustomerID] 
      FROM Sales.SalesOrderHeader) AS Shipments
GROUP BY [ShipYear]
ORDER BY [ShipYear]

Have you noticed the FROM clause?

Instead of using a table, it used a subquery. This is called a derived table or a table subquery.

And now, let me present you some ground rules when using this sort of query:

  • All columns in the subquery should have unique names. Much like a physical table, a derived table should have unique column names.
  • ORDER BY is not allowed unless TOP is also specified. That’s because the derived table represents a relational table where rows have no defined order.

In this case, a derived table has the benefits of a physical table. That’s why in our example, we can use COUNT() in one of the columns of the derived table.

That’s about all regarding subquery outputs. But before we get any further, you may have noticed that the logic behind the example for multiple values and others as well can also be done using a JOIN.

-- Output multiple values which is a list of customers with lastnames that start with 'I'
USE [AdventureWorks]
GO
SELECT o.[SalesOrderID], o.[OrderDate], o.[ShipDate], o.[CustomerID]
FROM Sales.SalesOrderHeader o
INNER JOIN Sales.Customer c on o.CustomerID = c.CustomerID
INNER JOIN Person.Person p ON c.PersonID = p.BusinessEntityID
WHERE p.LastName LIKE N'I%' AND p.PersonType = 'SC'

In fact, the output will be the same. But which one performs better?

Before we get into that, let me tell you that I have dedicated a section to this hot topic. We’ll examine it with complete execution plans and have a look at illustrations.

So, bear with me for a moment. Let’s discuss another way to place your subqueries.

#sql server #sql query #sql server #sql subqueries #t-sql statements #sql