Everywhere JSON so why not in SQL SERVER–New feature in SQL SERVER 2016

If you are a developer then surely you might have used JSON (JavaScript Object Notation) but, if not then don’t worry you might use sooner than later. JSON is kind of ecosystem which is most popular in the various area for exchanging the data. If you talk about charting solution, AJAX, Mobile services or any 3rd party integration then generally JSON is the first choice of the developers.


If you see nowadays most of the NOSQL database like Microsoft Azure Document DB, MONGODB etc. also using JSON ecosystem and some of them are based on JSON.


As it is such a popular growing system So, why not in SQL SERVER?

In SQL SERVER 2016 JSON introduced. This we can say a step or bridge between NON-relation database and relational database by Microsoft SQL SERVER


SQL Server 2016 providing following capabilities when you are using JSON

  1. Parse JSON by relation query
  2. Insert & update  JSON using query
  3. Store JSON in database


If you see it then conceptually it is similar to XML data type which you might use in SQL SERVER.

The good thing  in SQL SERVER 2016 for JSON there is no Native data type.  This will help in migration from any NOSQL to SQL SERVER.


SQL server provides bidirectional JSON formatting which you can utilize in a various way. Suppose data is coming from the external source in the JSON format then you can parse it and store in table structure (if required) in another case external source require data in JSON format while data in SQL SERVER in tabular format so both the purpose can easily solve with  SQL SERVER’s JSON feature.


Now, let’s jump directly to the practical to check JSON capabilities in SQL SERVER



It is similar to  FOR XML AUTO.  It will return JSON object of selected column where column name is treated as a Key or in other words we can say it will format the query result in JSON.



when you run above command the result will be like as shown in below figure.




It’s exactly like JSON auto the only difference is instead of SQL SERVER we have full control over the format. JSON Auto take predefined column schema while with JSON path we can create a complex object.

For example, we are using AdventureWorks Sales order table and joining that with product table to get sub-node. If you see in below image we have added Root node as well. This root Node can be added in JSON auto as well if required.



Now, when you run the above query we can get complex JSON object as follows


3) IsJSON function:-

By the name, it is clear that this is a validating function.

To cross check whether the provided string is a valid JSON or not we can run ISJSON.




  By the name, it is clear that if you want to get the value of the particular key of JSON then you can use this beautiful function which is JSON_VALUE.


5) OPENJSON function:-

This is a very beautiful function which you can use to parse external schema. Suppose, you got a JSON string from a mobile service which you will directly pass to SQL Sever and SQL SERVER stored procedure will do rest of the operation to parse it. The parsing and other operation can be easily handled by OPENJSON. The only tweak here that it required database compatibility level 130   which you need to do (if not compatible with level 130)



There are many other interesting things which we will cover later.

Please, provide your inputs.



To understand RLS (ROW LEVEL SECURITY) let’s understand the different problems first.

Problem 1 Suppose, you have a Multi-tenant e-commerce website and different companies registered on your website and you have centralized single database for all the client. Now as a product owner it is your responsibility that one tenant’s data should not be available to another tenant.  This is a very common problem.

2. Now, Suppose you have hospital database in which you have login user of different doctors & nurses. Now, your challenge is to show data to doctor or nurses to their relevant patient to whom they are giving treatment, not any other patient data should be available .

Here, limiting the user’s access to only certain rows of the data in database many have various reasons like compliance standards, regulatory need or security reasons.

Now, I know you were thinking that all the above problem can be resolved at code side easily by writing custom logic. I will say here yes you are right but this is not the 100% solution.  For example, if you have 4 different application like web, mobile, console, windows (Excel) and all has their own DAL then you have to implement this custom logic to every application and suppose  tomorrow if any time a new 3rd party came which want to integrate your data  or access database directly then in such cases it is tuff to apply same logic.

So, all the above problem can be easily handle using SQL SERVER 2016’s feature which is ROW Level Security (RLS). Security is one of the key areas which is handled in SQL SERVER 2016 very seriously.  As RLS (Row Level Security) is centralized security logic so you don’t need to repeat same security logic again and again.

As the name suggested Security implemented at Row Level in SQL SERVER 2016. In the Row Level, Security data is access according to user roles. It is a centralized data access Logic.

RLS has following properties

  • Fine-grained access role ( control both read & write  access to specific rows)
  • Application transparency  ( No application changes required)
  • Centralized the access within the database
  • Easy to implement & maintain

How RLS works?

RLS   is a predicate based function which runs seamlessly every time when a SQL is run on particular table on which RLS  predicate function implemented.

There are 2 predicates  which can be implemented in RLS

1) Filter Predicate: – By the name, it is clear that it will filter the row or we can say exclude the rows which do not satisfy the predicate and stop further option like select, Update & Delete.

for example: Suppose, you want to restrict doctor to see other doctor’s patient data then in such case you can apply filter predicate.

2) Block Predicate: –  This predicate helps in implementing policy by which insert, update and delete rows will prevent which violate the filter predicate. In other words, we can say it explicitly block write operation.

For example, you have multi-tenant application and you want to restrict one tenant user to insert or update other tenant’s data. Or suppose you have sales representative who belongs to specific region so they can not insert , update or delete other region’s data.


I know you will be super excited to see the demo of this feature so. Let’s do it right away.

There are 2 basic steps to create RLS

a) Create inline table function  or we can say predicate function  and write custom logic to control user access to every row

b) create the security policy and apply it.

In this demo ,I am creating a  new table called Patients which has following schema.


Here, I have inserted 2 rows for Nurse1 & 2 rows for Nurse2


The objective is to show only those rows to Nurse1, Nurse2 in which they are the in charge and a doctor user can see entire table’s data.

To achieve this let first create 3 users  in database


Once the users are created the next step is to grant permission of select to Nurse1 & Nurse2 user and full permission to doctor user.


Now, before creating function it is a standard to create a security schema in our case we are creating a schema with name sec as shown in below figure.

Now, create a function which will have security logic. The Logic is very simple if the user is doctor Or any in charge name then return 1 else 0.


Now create a security policy to proceed further


Till now we are good to go. Now, let’s test the security policy.

Firstly, running the select query with default user “dbo.”  and we have not given permission for this user if you see fn_RLSPredicate we have not mentioned it so obviously the result would show “0” records.


Now, running the same select statement but executing with “Nurse1” login then you will find 2 records which are relevant to Nurse1 is visible.


Similarly, I am running the same statement for Nurse2 user by running command “Execute as user” so, again I will get 2 records


Now, running the same statement with Doctor user and as per our expectation, it should show all 4 records.


So, as you can see we have achieved the goal using RLS (Row Level Security) feature. Now, next thing which might occur in your mind how to disable this policy if required then doesn’t worry it is very simple. Just alter the security policy and make state = off as shown in below figure.


I hope till now we are good to work on RLS. In next couple of post, we will dig deeper in RLS.

Please, share your thought for RLS.

Isn’t it easy to mask your data with Dynamic data Masking #5

Data security is always one of the important points which can not be ignored. Nowadays if you are working for any specific domain like Banking or Healthcare then there are a lot of compliance rules which you have to follow.

Data Masking is one of the best ways to help you to secure your sensitive data by a dynamic mask encryption.

This is one of the best features of SQL SERVER 2016 which I personally like most.

With the help of Dynamic Data Masking, you are just applying a mask to your sensitive data.  for example, if your system is storing SSN data then it should be visible to privileged or we can say authorized user only.

Dynamic Data Masking has following features:-

1) It masked the Sensitive data.

2) There will be no impact on functions & Stored Procedures and other SQL statement after applying this.

3) Applying the Data Masking is super easy.

4) You can allow any database user/role to see unmasked data by just simple Grant & Revoke Statement .

5) Data is not physically changed.

6) It is just on the fly obfuscation of data query result .

7) It is just  a T-SQL command with basic syntax.

Now , let us understand how to implement it.

Data masking implementation is very easy and below is the syntax for it.


Here, if you see the syntax is very simple the only new thing is MASKED and with (function=function name) only.

The function is nothing but the way to mask the data. SQL SERVER 2016 has following  different functions to mask the data

1) Default() function:- This is basic masking with the help of this function you can easily mask any field.

for example, your first name or last name field can be masked like XXXX etc.

2) Email() function :- If your column is email type or you we can say if you store Email in your column then you should use the Email() function for masking.

for example, your email can be mask like  RXXXX@XXXX.com

3) Partial () function:- With the help of this function you can mask specific data length and exclude some part of data from masking logic. for example, 123-4567-789 is your phone number then with partial masking feature you can mask like 12X-XXXX-7XX.

4) Random() function – By the name it is clear that you can mask the data with any random number range we will see more below in the hands on.

Remove Masking :- This is also possible that you applied a masking to a column and later on you don’t want that masking. So , don’t worry it very easy to remove masking from a column. below is the syntax for same.


Now, let’s understand this by an example.

In the example we are using a new database “SecureDataMask” in this database we are creating a tblSecureEmployee as shown in below figure.


Now, in this table, we are inserting couple of data for testing as shown below


Now we are applying different masking on this table’s column

1) Default Masking : In the table, we are applying default masking on LastName


2) Email Masking :- In the table, we are going to apply Email masking to email column below is the syntax for it.


3) Partial Masking:- For SSN we are going to apply custom masking. below is the syntax for same. Here as we aware that SSN is 11 characters long in our database. we applied the partial masking to show first two & last two characters in original value and rest other in the mask.


4) Random Number Masking :-  In our table, we are going to apply Random number masking to Securepin column as shown below.


Here, so far we are done with all the masking now.  let me run the select statement to test it.


If you see the data is still in the original state because I logged in using  privilege account “SA”. now, to test the masking let me create a new user account.


After creating the account we are trying to log-in with a new account as shown in below screen.


After our successful log in, we will run the select statement on same database’s table as we did earlier. If you see below snap you will find that we got masked data for LastName, Email, SSN, and securePin.


Now, it might be a rare case but suppose you want to remove the mask from any column on which you applied masking then don’t worry it is super easy.

Suppose, from the same table we don’t want mask on the LastName then below is the syntax for same.


Now, let me run the same select statement seeMask_user. You will find the Last Name is unmasked now.


From above few changes you can secure your data via Dynamic masking and as mentioned above there will be no impact on your existing function ,stored procedure because data is not physically changed.

I hope you may like this feature.   Please, share your input for same.

Enjoy !!


The Evolution of DATEDIFFBIG in SQL SERVER 2016 #4

In the series of SQL SERVER 2016, this is a new post. in this post, we will discuss DATEDIFF_BIG and how it is helpful.

So, before jumping into directly in technical details, we all know that time is very important and every second valuable and countable but sometimes every microsecond & nanosecond is also countable Smile . For such operations in which every microsecond & nanosecond is countable, we can use DATEDIFF_BIG function.

As you aware the BIGINT range is from -9,223,372,036,854,775,808 to 9,223,372,036,854,775,807.  Here if any difference (Micro & Nano) second is out of the the mentioned range then DATEDIFF returns that value else return error(Obviously).

Below is the basic syntax if DATEDIFF_BIG although it is similar to DATEDIFF. We can say it is a extended version of DATEDIFF.

DATEDIFF_BIG( datePart, start Date, End date)

The value of datePart is same like DATEDIFF function.

For example if you want to collect millisecond difference then use ms, microsecond then mcs and for nanosecond ns.

As per the MSDN   for the Millisecond, the maximum difference between start date & end date is 24 days, 20 hours, 21 minutes and 23,647 seconds. For Second, the maximum difference is  68 years.  

Now, let see why this DATEDIFF_BIG introduced so, I am running a DATEDIFF  function in SQL SERVER 2012 and see what we get after running that query.




You can see in above query we got an error of overflow.

Now, we are calculating the same difference from DATEDIFF_BIG in SQL SERVER 2016. See, below snap for same.




Isn’t it great ? Although, I am scarred with those applications who calculate milliseconds Sad smile.

Anyways, it is good to know feature.

Do provide your feedback for the post it is very valuable for us.

RJ !!!

Here Comes New Idea of Split String in SQL SERVER 2016 #3

In the Series of SQL SERVER 2016, this is another post. Before Jumping in detail just think if you have a comma or other separator string and if you have to split it by separator field then for such task  in previous SQL SERVER versions either you will write a function which split the string and return desire values in a column  or

you will use XML function or  might be different custom functions.

Let me explain this with below example. Suppose you have a string like below

DECLARE @FriendList AS VARCHAR(1000)

SET @FriendList =’Ravi,Suyash,Vaibhav,Shyam,Pankaj,Rajul,Javed’


Now you want output like below



Then in such cases, you will  follow 2 approaches (their might be other as well)

Approach 1:- Write  a function like below  and use it.


And once this function is created you can use like below


Approach 2 :- You can use XML option in SQL SERVER as  shown in below


So, the good news is now in SQL SERVER 2016 you don’t need to write  so many lines to split any string. In SQL SERVER 2016 a new string function is Introduced which is


The use of this function is very easy and below is the syntax

STRING_SPLIT (string, separator)

Now, let me show you same output using STRING_SPLIT function


Isn’t it easy ?

I hope you will like this easy way to split the string.

Provide your feedback.

RJ !!!

Compress & Decompress–new Feature in SQL SERVER 2016 #2

This is another article in the series of SQL SERVER 2016 Journey . I am pretty much sure you might aware of Gzip Compression algorithm. If not then try  this link.


So, SQL SERVER 2016 introduce this two awesome functions for Compress & Decompress the data.

Before SQL SERVER 2016 version we have data compression feature like Page & Row compression (check Previous post for it Link )which is different then this column value compression.


In SQL SERVER 2016 Compress function,  data compression is done via GZIP algorithm and return VARBINARY(MAX).


Below is the simple syntax of Compress function


Compress (Expression)

Here Expression can be nvarchar(n), nvarchar(max), varchar(n), varchar(max), varbinary(n), varbinary(max), char(n), nchar(n), or binary(n)


Decompress function is just opposite of  compress function. It is used to decompress the value of VARBINARY which is converted using Compress function. The only tweak is you need to cast the output of Decompress function  in specific data type to make it readable (if using varchar ,nvarchar compression) .


below is the simple syntax of Decompress
Decompress (Compressed string)


Let’s understand this via an example as shown below .



In this example I have taken 3 tables with exact same schema & data


  1. 1) IndiandotnetFriends
  2. 2) IndiandotnetFriends_Compress
  3. 3) IndiandotneFriends_Decompress


You can see  snap in which we are inserting same data.

As the name suggested in first table normal data from Adventureworks’s person table.

In second table we are inserting compressed value of first Name  and in 3rd table we are inserting decompress value of First Name from the Compressed table.

Now, let’s check compress  & decompress table data



Now, Your might thinking that the output of both compress and decompress is not readable.

So you are right to make data readable of Decompress table we need to type cast.

See below snap for same.




Till now we know how to use this Compress & Decompress function. Now, let me share the benefit of using Compress. if you see below snap you will find that data length of compress is comparatively less than normal and decompressed data length .




Obviously, compression helps you somewhere in the overall performance of your application.

The good point is  you can pass the compress data to your .net application and decompress using GzipStream as well.


The only thing which we need to take care is type casting. Suppose your base column which compressed is VARCHAR then you need to typecast again in VARCHAR.


Now, next question is where we can use this functions. So,  we can use in compressing large object like binary data in which we save jpg, pdf , word document etc..


I hope you will be excited in using this function.


Please, share your input.

DROP IF Exists- A cool feature in SQL SERVER 2016

In the Series of SQL SERVER 2016 journey, this is our new article. In this article, we are sharing a new cool feature which introduced in SQL SERVER 2016 which is DROP IF EXISTS (DIE) .

In our development many times it happens that we need to drop a table and as a best practice we write the following syntax as shown in below figure


Now, in SQL SERVER 2016 the same task is super easy. You can write the following syntax to drop the table object



The best part is if suppose the object does not exist then  here will be no error execution will continue.

Let me share one more example of Dropping a stored procedure.


Similar, way we can write for following data objects and with the following syntax












View :-





I like this feature I am sure you will also like this.

Please, do share your feedback for blog post.

Enjoy !!

Unbelievable SQL SERVER 2016 Feature you should aware.


Although, I know I am bit late to share this thing on our blog but it says in Indian proverb “Der aai durust aai” means it’s OK you came late but you came that is more important.

Anyways, so you all might aware that Microsoft launched SQL SERVER 2016 officially in June 2016.

You can download the SQL Server 2016 via Link.

Obviously, this is a new revolution in SQL SERVER series. You will find many great features in this version.

This post is beginning to explore all those great features and we will do deep dive in all those features. In this, post we briefly introducing those features. so, without wasting time let me share a brief introduction.



Is this surprising to you ? Obviously, yes. As you might aware that most of the NO SQL database use either JSON or XML. As XML feature already exists in SQL SERVER so this was time for JSON. You can play with JSON in SQL SERVER 2016.


2)Always Encrypted :-

If you talk about security this one the best feature. Now, you are thinking what it means. So, It means that the data in the SQL SERVER reside always in encrypted format and SQL server can perform the operation on the encrypted data without decrypting it. The encryption key can be exist in some other system. With this, feature you can secure you ensure that your data is secure from the person like DBA / Developer as well. These guys also can’t see the actual data. Isn’t it neat ?


3) Row Level Security :-

This is another interesting feature which helpful especially to the developers  who needs to write extra code to check this. Let me explain this with an example suppose you have a sales team who do market research and you want to restrict that each sales manager can see only those data which entered by him only in such cases you don’t need to write specific condition in your code. It can be achieved by Row Level Security.


4) “R” in SQL SERVER :-

For the data scientist, it is a great NEWS. As Resolution Analytics is purchased by Microsoft and It is incorporated in SQL SERVER. You can run R analysis query in SQL Server.


5) Temporal Table:-

The Temporal table holds the old version of ROWS of a table. It means that it maintain a copy of the old rows in the table whenever there is an update on the main table.


6) PolyBase :-

With the help of this feature, you can access data which exist in Azure Blob or Hadoop cluster using the same SQL server. In the nutshell, we can say this is the technology which combines both relational & non-relational database in a single umbrella which is SQL SERVER. You can run the query directly on external data like Hadoop or Azure blob storage.


7) Stretch Database:-

I am pretty much sure by the name you can guess this feature. So, with the Stretch database you have can store your part of the data in the cloud which depends upon your need. You can say most recent transactional data you can store in your local environment and other old data you can store in Azure.


8)Query store :-

Another interesting feature to help you in identifying  performance drag using Query store. When you enable this feature it automatically captures a history of queries , plans,  and statics and retain them for review and resolve the performance issues.


9) Mobile report:-

As mentioned earlier this is the revolution in SQL SERVER 2016. In SSRS there are many important changes introduced. Now we can import Power BI report in SSRS and apart from this you can create a mobile report which you can run on Mobile.


Now, we started officially SQL SERVER 2016 tutorial series.


Moving forward we are going to discuss all these features in details and also the couple of new things which introduced in SQL SERVER 2016.



Happy VijayDashmi.

How to use SORT control in SSIS ? TIP #115

Dear Friends,

In the series of Learn SSIS step by step this is the 5th post. Now from this post we are going to use each transformation control one by one.

So, lets start with simplest one transformation control which is “Sort”.

By the name it is clear data it will sort the data which is provided to it and give sorted output as a result. Lets understand this by below step by step example. (Here I am not writing step to start visual studio and create a new project, I am pretty much sure you are aware of this now.)

Before going further let me tell you want we are going to do  here. We will have a input result (which will be a text file) and then sort it and save in another file.

Step 1:-I renamed package to SortPackage if you want you can rename your package as well. Now drag drop a Data Flow task as shown below and double click DATA flow task


Step 2:- When you click it you will get a data flow screen where you can drag drop FLAT file source. You can choose source assistance as well. Now configure this flat file source. Means give the path of the file which you want to have as a input source. I am taking a text file which contain fruits name in different order. as shown in below figure


Right click flat file source click on EDIT option and follow the screens


Step 3:- Now once the file is configure. We have to drag drop SORT control from SSIS tool box as shown in below figure.


I also added the output of flat file source to sort control. As shown in above figure. Now configure it. Click on Sort control and you will get following screen. As show in below screen you can sort the data on any column and in any direction like Ascending or descending.

Right there is only one input column which is name so we are sorting name in Ascending order as shown below.


Step 4:- Now once this configuration is done we will save the data in new file with new name which will be sortedFruits. Now to achieve this drag drop the destination control. So I took same float file destination as shown in below figure


Step 5:- Now configure this file destination. Which means where we need to save this file and what are the columns which we required. so In current case there is only one column which is Name (Fruit name) and I am saving this file at same place with sortFruits name. so lets configure it by clicking right click on flat file destination and click on edit button


Step 6:- Once this is configured we need to run this package by pressing F5.When you run hit F5 and everything going right then in execution screen at each step you will find green right check image as shown in below screen


Step 7: Now to cross check we will first see whether that file is created or not. If created then whether we have sorted data or not. So lets open the flat files.


So, if you see at provided destination location a file is created and the data inside this file is sorted file.

I hope you like this first transformation control. till then Enjoy!!!

Thanks !!


How to create first basic package with SSIS ? tip #113

Dear friends,

In last post #112 we understood WWH (What ,Why & How ) of SSIS. Now , lets move now real quick in practical session where we will try to create a basic simple package.

The example which we are creating is well known Export data from SQL SERVER to a flat file.

Step 1:-  Open SQL SERVER Data Tool from start menu


Step 2:- Once it is open create a new project by clicking new project option. You have to select proper template as highlighted in below figure and give a name to project. As shown in below figure


Step 3:- Now drag drop data flow task control from SSIS toolbox. You can give customize message by click control’s text. I prefer this habit so down the line if  after few month or years if you need to do some maintenance or logic change you don’t need to think a lot for why this control is for.


Step 4:- Now double click on Data flow control or click on data flow tab. Now on this area you have to drag drop source assistance. When you drag drop it you will get a pop as shown below.



The screen source assistance is the way by which we can select the data source on which we need to perform operation.  As you are seeing in the image there are different data sources

Like SQL SERVER, Excel, Flat file, Oracle.

Although, you can select other sources also from SSIS toolbox as shown in below screen (As you are seeing there are various individual sources exists in toolbox itself so either use source assistance or drag drop individual source.It is worthless to explain here that excel source for excel file, flat file source for flat file and so on.


In this example we are selecting SQL SERVER. When you select Source Type then you have to configure connection Manager.For this we have to select “NEW” in connection manager panel and click OK button.

You will get below screen where you can give all the information related to  SQL SERVER by which our package can connect with that data source. below I am using my SQL SERVER installed on my machine and using AdventureWorks database as shown in below image.



Now once connection is setup. Now we have to export a particular table data in a flat file. but you are wondering which table or data which we are going to export.

Step 5:- Now to select data which whether it is entire table, or stored procedure output , or view output or just simple SQL query. for this we need to double click on OLEDB data source and then we will get following screen.


Now ,here we can choose data access mode either table or view, or SQL command ,SQL command with variable. To make this first example easy we are choosing table or view and selecting “Product table “ in below drop down for Name of the table or the view.

Step 6:- Now once you have selected table or view you can select specific columns which we need to export in flat file. For this we have to select columns option available on left side. when you click it you will get below screen.


As shown in above figure you can check uncheck the columns which you need to export in flat file. we can rename the column name as well (as I did standard Cost to MRP). If you see below image


Here I am not explaining errorout option in detail in general sense just think it is configuration step if something failed.

Step 8:-

Now, we have source which we need to export in flat file, for this we may require a destination file in which we can store the data. So, Now we drag drop destination control which will be a flat file destination control as shown in below figure.


Step 9:-  Now in above image you are seeing there are 2 arrows which is just flow direction means where the data needs to flow. Obviously in our case the data needs to flow from oledb source to flat file destination. So what we  need to do drag the blue arrow and release it on flat file destination as shown below.



Step 10 :- I don’t know whether you noticed or not but let me tell you here. If you see above figure data is flowing from oledb source to flat file destination which is good but on same time there is cross image in red color which means there is some error in the control. So guess what is the error ?

I think you picked right the destination is not configured. So to do this we need to double click the flat file destination.

Step 11: When you double click you will get below screen. In which you need to configure the file location and file format like whether you want a delimiter file, fixed length file and many other option as shown in below figure. In our example we are using delimiter file option.


Step 12:- When you hit OK you will get following screen where you need to configure as shown in below figure. You need to give file location with file path. if you want different delimiter the you can choose that also.


Step 12:- Now press OK you will get flat file destination editor in which you can select mapping option and just check it for your query whether all the selected columns from source are aligning or not.


Step 12:- Once we done with this you will see the cross image in red disappear. If you are still seeing this it means there is something going wrong with configuration.

Now if everything is good then we can run our first own created package by pressing F5 or with Start option in IDE.

Step 13:-  If everything is correct you will get right check in green apart from this you might be interested how many rows transfer from source to destination so that information also can be found. see below image for detail.


In our case we moved 504 rows. Now lets cross check at the destination location as well whether the file is created or not with these 504 rows.


WOW , we did it . We created our first simplest package which is export data from SQL to flat file.

I hope you enjoyed the learning. In next step we will do something more advance. mean while I request you all to do same practice and try to use excel instead of flat file.

Please do write your inputs. Let me know whether you are enjoying this series or not.

Enjoy !!!