Abstract  This article describes a TSQL JSON parser and provides the source. It is also designed to illustrate a number of string manipulation techniques and also eliminate the issues while dealing with the JSON document containing special symbols like (“/” , ”-”....) in T-SQL. With it you can do things like this to extract the data from a JSON file or document which contains noise and complexities.    Summary For Implementation The code for the JSON Parser will run in SQL Server 2005,  and even in SQL Server 2000 (note: some modifications are necessary). First the function stores all strings in the temporary table, even the name of the elements, since they are 'escapes' in a different way, and may contain, unescaped, brackets, Special Characters which denote objects or lists. These are replaced in the json string by tokens which represent the strings. After this fetch all the json keywords and values for further processing by using the regular expressions, various string functions and a list of SQL queries and variables to store the values for a particular object. And at the last function will return a whole table which contains rows and columns with no noise in the values as the other tables in the particular database.   Figure 1:- Json Input   Figure 2:- Function Output Background TSQL isn’t really designed for doing complex string parsing which contains special characters and particularly where strings represent nested data structures such as XML, JSON, or XHTML.   You can do it but it is not a pretty sight; but If you ever want to do it anyway ? (note You can now do this rather more easily using SQL Server 2016’s built-in JSON support.) But If the SQL Server version is older or not compatible with the built-in JSON support then you can use this customized function to get the desired output by parsing any type of json document.  There is so much stuff behind that all happens to you. For example, it could be that DBA doesn’t allow a CLR, or you lack the necessary skills with procedural scripting. Sometimes, there isn’t any application, or you want to run code unobtrusively across databases or servers.   The Traditional way for dealing with data like this is to let a separate business layer parse a JSON ‘document’ into some meaningful structure(Like Tree) and then update the database by making a series of calls and lots of sql procedures. This is pretty, but can get more complicated and headache if you need to ensure that the updates to the database are wrapped into one transaction so that if anything goes wrong or any issues occur, then the whole transaction can be rolled back. This is why a TSQL approach has advantages.  Adjacency list tables have the same structure whatever the data in them. This means that you can define a single Table-Valued  Type and pass data structures around between stored procedures.  Converting the data to Hierarchical table form will be different for each application, but is easy with a TSQL. You can, alternatively, convert the hierarchical table into JSON and interrogate that with SQL.   JSON format JSON is one of the most popular lightweight markup languages, and is probably the best choice for transfer of object data from a web page. JSON is designed to be as lightweight as possible and so it has only two structures. The first, delimited by curly brackets, is a collection of Key/value pairs, separated by commas. The key is followed by a colon. The first snag for TSQL is that the curly or square brackets are not ‘escaped’ within a string, so that there is no way of partitioning a JSON ‘document’ simply. It is difficult to  differentiate a bracket used as the delimiter of an array or structure, and one that is within a string. The second complication is that, unlike YAML, the datatypes of values can’t be explicitly declared. You have to pass them out from applying the rules from the JSON Specification.   Implementation The JSON outputter is a great deal simpler, since one can be sure of the input, but essentially it does the reverse process, working from the root of the json document to the leaves. The only complication is working out the indent of the formatted output string. In the implementation, you’ll see a fairly heavy use of PATINDEX.This uses a RegEx. However, it is all we have, and can be pressed into service by chopping the string it is searching (if only it had an optional third parameter like CHARINDEX that specified the index of the start position of the search!). The STUFF function is also important for this sort of string-manipulation work. CREATE FUNCTION [Platform].[parseJSON] (@JSON NVARCHAR(MAX)) RETURNS @hierarchy TABLE ( Element_ID INT IDENTITY(1, 1) NOT NULL /* internal surrogate primary key gives the order of parsing and the list order */ ,SequenceNo [int] NULL /* the place in the sequence for the element */ ,Parent_ID INT NULL /* if the element has a parent then it is in this column. The document is the ultimate parent, so you can get the structure from recursing from the document */ ,[Object_ID] INT NULL /* each list or object has an object id. This ties all elements to a parent. Lists are treated as objects here */ ,[Name] NVARCHAR(2000) NULL /* the Name of the object */ ,StringValue NVARCHAR(MAX) NOT NULL /*the string representation of the value of the element. */ ,ValueType VARCHAR(10) NOT NULL /* the declared type of the value represented as a string in StringValue*/ ) AS BEGIN DECLARE @FirstObject INT --the index of the first open bracket found in the JSON string ,@OpenDelimiter INT --the index of the next open bracket found in the JSON string ,@NextOpenDelimiter INT --the index of subsequent open bracket found in the JSON string ,@NextCloseDelimiter INT --the index of subsequent close bracket found in the JSON string ,@Type NVARCHAR(10) --whether it denotes an object or an array ,@NextCloseDelimiterChar CHAR(1) --either a '}' or a ']' ,@Contents NVARCHAR(MAX) --the unparsed contents of the bracketed expression ,@Start INT --index of the start of the token that you are parsing ,@end INT --index of the end of the token that you are parsing ,@param INT --the parameter at the end of the next Object/Array token ,@EndOfName INT --the index of the start of the parameter at end of Object/Array token ,@token NVARCHAR(200) --either a string or object ,@value NVARCHAR(MAX) -- the value as a string ,@SequenceNo INT -- the sequence number within a list ,@Name NVARCHAR(200) --the Name as a string ,@Parent_ID INT --the next parent ID to allocate ,@lenJSON INT --the current length of the JSON String ,@characters NCHAR(36) --used to convert hex to decimal ,@result BIGINT --the value of the hex symbol being parsed ,@index SMALLINT --used for parsing the hex value ,@Escape INT --the index of the next escape character /* in this temporary table we keep all strings, even the Names of the elements, since they are 'escaped' in a different way, and may contain, unescaped, brackets denoting objects or lists. These are replaced in the JSON string by tokens representing the string */ DECLARE @Strings TABLE ( String_ID INT IDENTITY(1, 1) ,StringValue NVARCHAR(MAX) ) IF ISNULL(@JSON, '') = '' RETURN SELECT @characters = '0123456789abcdefghijklmnopqrstuvwxyz' --initialise the characters to convert hex to ascii ,@SequenceNo = 0 --set the sequence no. to something sensible. ,@Parent_ID = 0; /* firstly we process all strings. This is done because [{} and ] aren't escaped in strings, which complicates an iterative parse. */ WHILE 1 = 1 --forever until there is nothing more to do BEGIN SELECT @start = PATINDEX('%[^a-zA-Z]["]%', @json collate SQL_Latin1_General_CP850_Bin);--next delimited string IF @start = 0 BREAK --no more so drop through the WHILE loop IF SUBSTRING(@json, @start + 1, 1) = '"' BEGIN --Delimited Name SET @start = @Start + 1; SET @end = PATINDEX('%[^\]["]%', RIGHT(@json, LEN(@json + '|') - @start) collate SQL_Latin1_General_CP850_Bin); END IF @end = 0 --either the end or no end delimiter to last string BEGIN -- check if ending with a double slash... SET @end = PATINDEX('%[\][\]["]%', RIGHT(@json, LEN(@json + '|') - @start) collate SQL_Latin1_General_CP850_Bin); IF @end = 0 --we really have reached the end BEGIN BREAK --assume all tokens found END END SELECT @token = SUBSTRING(@json, @start + 1, @end - 1) --now put in the escaped control characters SELECT @token = REPLACE(@token, FromString, ToString) FROM ( SELECT '\b' ,CHAR(08) UNION ALL SELECT '\f' ,CHAR(12) UNION ALL SELECT '\n' ,CHAR(10) UNION ALL SELECT '\r' ,CHAR(13) UNION ALL SELECT '\t' ,CHAR(09) UNION ALL SELECT '\"' ,'"' UNION ALL SELECT '\/' ,'/' ) substitutions(FromString, ToString) SELECT @token = Replace(@token, '\\', '\') SELECT @result = 0 ,@escape = 1 --Begin to take out any hex escape codes WHILE @escape > 0 BEGIN SELECT @index = 0 --find the next hex escape sequence ,@escape = PATINDEX('%\x[0-9a-f][0-9a-f][0-9a-f][0-9a-f]%', @token collate SQL_Latin1_General_CP850_Bin) IF @escape > 0 --if there is one BEGIN WHILE @index < 4 --there are always four digits to a \x sequence BEGIN SELECT --determine its value @result = @result + POWER(16, @index) * (CHARINDEX(SUBSTRING(@token, @escape + 2 + 3 - @index, 1), @characters) - 1) ,@index = @index + 1; END -- and replace the hex sequence by its unicode value SELECT @token = STUFF(@token, @escape, 6, NCHAR(@result)) END END --now store the string away INSERT INTO @Strings (StringValue) SELECT @token -- and replace the string with a token SELECT @JSON = STUFF(@json, @start, @end + 1, '@string' + CONVERT(NCHAR(5), @@identity)) END -- all strings are now removed. Now we find the first leaf. WHILE 1 = 1 --forever until there is nothing more to do BEGIN SELECT @Parent_ID = @Parent_ID + 1 --find the first object or list by looking for the open bracket SELECT @FirstObject = PATINDEX('%[{[[]%', @json collate SQL_Latin1_General_CP850_Bin) --object or array IF @FirstObject = 0 BREAK IF (SUBSTRING(@json, @FirstObject, 1) = '{') SELECT @NextCloseDelimiterChar = '}' ,@type = 'object' ELSE SELECT @NextCloseDelimiterChar = ']' ,@type = 'array' SELECT @OpenDelimiter = @firstObject WHILE 1 = 1 --find the innermost object or list... BEGIN SELECT @lenJSON = LEN(@JSON + '|') - 1 --find the matching close-delimiter proceeding after the open-delimiter SELECT @NextCloseDelimiter = CHARINDEX(@NextCloseDelimiterChar, @json, @OpenDelimiter + 1) --is there an intervening open-delimiter of either type SELECT @NextOpenDelimiter = PATINDEX('%[{[[]%', RIGHT(@json, @lenJSON - @OpenDelimiter) collate SQL_Latin1_General_CP850_Bin) --object IF @NextOpenDelimiter = 0 BREAK SELECT @NextOpenDelimiter = @NextOpenDelimiter + @OpenDelimiter IF @NextCloseDelimiter < @NextOpenDelimiter BREAK IF SUBSTRING(@json, @NextOpenDelimiter, 1) = '{' SELECT @NextCloseDelimiterChar = '}' ,@type = 'object' ELSE SELECT @NextCloseDelimiterChar = ']' ,@type = 'array' SELECT @OpenDelimiter = @NextOpenDelimiter END ---and parse out the list or Name/value pairs SELECT @contents = SUBSTRING(@json, @OpenDelimiter + 1, @NextCloseDelimiter - @OpenDelimiter - 1) SELECT @JSON = STUFF(@json, @OpenDelimiter, @NextCloseDelimiter - @OpenDelimiter + 1, '@' + @type + CONVERT(NCHAR(5), @Parent_ID)) WHILE (PATINDEX('%[A-Za-z0-9@+.e]%', @contents collate SQL_Latin1_General_CP850_Bin)) <> 0 BEGIN IF @Type = 'object' --it will be a 0-n list containing a string followed by a string, number,boolean, or null BEGIN SELECT @SequenceNo = 0 ,@end = CHARINDEX(':', ' ' + @contents) --if there is anything, it will be a string-based Name. SELECT @start = PATINDEX('%[^A-Za-z@][@]%', ' ' + @contents collate SQL_Latin1_General_CP850_Bin) --AAAAAAAA SELECT @token = RTrim(Substring(' ' + @contents, @start + 1, @End - @Start - 1)) ,@endofName = PATINDEX('%[0-9]%', @token collate SQL_Latin1_General_CP850_Bin) ,@param = RIGHT(@token, LEN(@token) - @endofName + 1) SELECT @token = LEFT(@token, @endofName - 1) ,@Contents = RIGHT(' ' + @contents, LEN(' ' + @contents + '|') - @end - 1) SELECT @Name = StringValue FROM @strings WHERE string_id = @param --fetch the Name END ELSE SELECT @Name = NULL ,@SequenceNo = @SequenceNo + 1 SELECT @end = CHARINDEX(',', @contents) -- a string-token, object-token, list-token, number,boolean, or null IF @end = 0 --HR Engineering notation bugfix start IF ISNUMERIC(@contents) = 1 SELECT @end = LEN(@contents) + 1 ELSE --HR Engineering notation bugfix end SELECT @end = PATINDEX('%[A-Za-z0-9@+.e][^A-Za-z0-9@+.e]%', @contents + ' ' collate SQL_Latin1_General_CP850_Bin) + 1 SELECT @start = PATINDEX('%[^A-Za-z0-9@+.e][-A-Za-z0-9@+.e]%', ' ' + @contents collate SQL_Latin1_General_CP850_Bin) --select @start,@end, LEN(@contents+'|'), @contents SELECT @Value = RTRIM(SUBSTRING(@contents, @start, @End - @Start)) ,@Contents = RIGHT(@contents + ' ', LEN(@contents + '|') - @end) IF SUBSTRING(@value, 1, 7) = '@object' INSERT INTO @hierarchy ( [Name] ,SequenceNo ,Parent_ID ,StringValue ,[Object_ID] ,ValueType ) SELECT @Name ,@SequenceNo ,@Parent_ID ,SUBSTRING(@value, 8, 5) ,SUBSTRING(@value, 8, 5) ,'object' ELSE IF SUBSTRING(@value, 1, 6) = '@array' INSERT INTO @hierarchy ( [Name] ,SequenceNo ,Parent_ID ,StringValue ,[Object_ID] ,ValueType ) SELECT @Name ,@SequenceNo ,@Parent_ID ,SUBSTRING(@value, 7, 5) ,SUBSTRING(@value, 7, 5) ,'array' ELSE IF SUBSTRING(@value, 1, 7) = '@string' INSERT INTO @hierarchy ( [Name] ,SequenceNo ,Parent_ID ,StringValue ,ValueType ) SELECT @Name ,@SequenceNo ,@Parent_ID ,StringValue ,'string' FROM @strings WHERE string_id = SUBSTRING(@value, 8, 5) ELSE IF @value IN ('true', 'false') INSERT INTO @hierarchy ( [Name] ,SequenceNo ,Parent_ID ,StringValue ,ValueType ) SELECT @Name ,@SequenceNo ,@Parent_ID ,@value ,'boolean' ELSE IF @value = 'null' INSERT INTO @hierarchy ( [Name] ,SequenceNo ,Parent_ID ,StringValue ,ValueType ) SELECT @Name ,@SequenceNo ,@Parent_ID ,@value ,'null' ELSE IF PATINDEX('%[^0-9-]%', @value collate SQL_Latin1_General_CP850_Bin) > 0 INSERT INTO @hierarchy ( [Name] ,SequenceNo ,Parent_ID ,StringValue ,ValueType ) SELECT @Name ,@SequenceNo ,@Parent_ID ,@value ,'real' ELSE INSERT INTO @hierarchy ( [Name] ,SequenceNo ,Parent_ID ,StringValue ,ValueType ) SELECT @Name ,@SequenceNo ,@Parent_ID ,@value ,'int' IF @Contents = ' ' SELECT @SequenceNo = 0 END END INSERT INTO @hierarchy ( [Name] ,SequenceNo ,Parent_ID ,StringValue ,[Object_ID] ,ValueType ) SELECT '-' ,1 ,NULL ,'' ,@Parent_ID - 1 ,@type RETURN END Code Snippet 1:- ParseJson Function   Closure The so-called ‘impedance-mismatch’ between applications and databases is an illusion. if the developer has understood the data correctly then there is less complexity  while processing it. But has been trickier with other formats such as JSON. By using techniques like this, it should be possible to liberate the application or website from having to do the mapping from the object model to the relational, and spraying the database with ad-hoc T-SQL  that uses the fact/dimension tables or updateable views.  If the database can be provided with the JSON, or the Table-Valued parameter, then there is a better chance of  maintaining full transactional integrity for the more complex updates. The database developer already has the tools to do the work with XML, but why not the simpler, and more practical JSON? I hope these routines get you started with experimenting with all this for your requirements.  

1. To create a login SQL server, Navigate to Security > Logins   2. In the next screen, Enter    a. Login Name    b. Select SQL Server authentication    c. Enter Password for MS SQL create a user with a password   You can also create a login using the T-SQL command for SQL server create login and user.    CREATE LOGIN MyLogin WITH PASSWORD = MsSQL   3. Give Full Access for Demo Login   Login is created If we refresh the Logins, then we can view Login.   How To Create a User? You can use any of the following two ways:      · Using T-SQL      · Using SQL Server Management Studio   Providing limited access only to a certain Database You will be creating a user for the Events27_production database.   1. Connect to SQL server to create a new user       a. Connect to SQL Server then expand the Databases folder from the Object          Explorer.       b. Identify the database for which you need to create the user and expand it.       c. Expand its Security folder.       d. Right-click the Users folder then choose "New User…"    2. Enter User details, you will get the following screen,      a. Enter the desired Username      b. Enter the Login name (created earlier)      User is created for that specific Database.   Create User using T-SQL     create user <user-name> for login <login-name>     create user DemoUser for login Demo   Assigning limited permission to a user in SQL Server Permissions refer to the rules that govern the levels of access that users have on the secured SQL Server resources. SQL Server allows you to grant, revoke and deny such permissions. There are two ways to give SQL server user permissions:  1. Connect to your SQL Server instance and expand the folders from the Object Explorer as shown below. Right-click on the name of the user   2. In the next screen,     a. Click the Securable option from the left.     b. Click on Search   3. In the next window,     a. Select "All Objects belonging to the Schema."     b. Select Schema name as "dbo" 4. Grant or Revoke permission of a specific table or DB object       a. Identify Table you want to Grant Permission       b. In Explicit Permission select Grant   The user DemoUser is granted SELECT permission on final_backup_tidx_sctionSponsors.   Grant Permissions using T-SQL use <database-name> grant <permission-name> on <object-name> to <username\principle>   Use Events27_production Go Grant Select on final_backup_tidx_sctionSponsors to DemoUser 5. Providing ROLE to a specific user:     a. In the object explorer expand the databases and security folder.     b. Expand Roles and right-click on Database Role.     c. Click on New database role. Then a new pop-up window is open.     d. In the General tab enter the role name and click on ok.  6. Refresh the roles. In below screenshot shows the role   Remove Login from SQL Server:    1. To drop login SQL server, Navigate to Security > Logins    2. Select the desired login and click on Delete     Drop Login using T-SQL     DROP LOGIN Demo;  

What is SSIS ? SSIS is a platform for data integration and workflow applications. It features a data warehousing tool used for data extraction, transformation, and loading (ETL). The tool may also be used to automate maintenance of SQL Server databases and updates to multidimensional cube data. SQL Server Integration Service (SSIS) is a component of the Microsoft SQL Server database software that can be used to execute a wide range of data migration tasks. SSIS is a fast & flexible data warehousing tool used for data extraction, loading and transformation like cleaning, aggregating, merging data, etc. It makes it easy to move data from one database to another database. SSIS can extract data from a wide variety of sources like SQL Server databases, Excel files, Oracle and DB2 databases, etc. SSIS also includes graphical tools & wizards for performing workflow functions like sending email messages, FTP operations, data sources, and destinations. Features of SSIS: Organized and lookup transformations Tight integration with other Microsoft SQL family Provides rich Studio Environments Provides a lot of data integration functions for better transformations High-speed data connectivity   Why SSIS? Extract, Transform, and Load (ETL) data from SQL Server to a file and also from file to SQL. Sending an email. Download the File from FTP. Rename ,Delete , Move File From Defined Path. It allows you to join tables from different databases (SQL, Oracle, etc...) and from potentially different servers.   How SSIS Works? SSIS consists of three major components, mainly: Operational Data: An operational data store (ODS) is a database designed to integrate data from multiple sources for additional operations on the data. This is the place where most of the data used in the current operation is housed before it’s transferred to the data warehouse for longer-term storage or archiving. ETL process: ETL is a process to Extract, Transform and Load the data. Extract, Transform and Load (ETL) is the process of extracting the data from various sources, transforming this data to meet your requirement and then loading into a target data warehouse. ETL provides a ONE STOP SOLUTION for all these problems. Extract: Extraction is the process of extracting the data from various homogeneous or heterogeneous data sources based on different validation points. Transformation: In transformation, entire data is analyzed and various functions are applied on it in order to load the data to the target database in a cleaned and general format. Load: Loading is the process of loading the processed data to a target data repository using minimal resources. Data Warehouse Data Warehouse captures the data from diverse sources for useful analysis and access. Data warehousing is a large set of data accumulated which is used for assembling and managing data from various sources for the purpose of answering business questions. Hence, helps in making decisions.   How to install SSDT(Sql Server Data Tools)? Prerequisite and environment Setup for SSIS Project For Starting SSIS we need 2 Studios SQL Server Data Tools (SSDT) for developing the Integration Services packages that a business solution requires. SQL Server Data Tools (SSDT) provides the Integration Services project in which you create packages. Installation Steps: Download SSDT setup from Microsoft website.   URL: https://docs.microsoft.com/en-us/sql/ssdt/previous-releases-of-sql-server-data-tools-ssdt-and-ssdt-bi?view=sql-server-ver15 When you open the .exe file, you will be asked to restart the system before installation. So, restart first and Run Setup. And press Next. It will show the tools required and the features such as SQL Server Database, SSAS(SQL Server Analysis Services), SSRS(SQL Server Reporting Services) and SSIS(SQL Server Integration Services). Make sure you check SSIS and click the “install” button. Refer the below screenshot for the same.   We will see following contents In SSIS: Variables Connection Manager SSIS Toolbox Container Tasks Data Flow Task   Variable: Variables store values that a SSIS package and its containers, tasks, and event handlers can use at runtime.   System variables : Defined by Integration Services SSIS provides a set of system variables that store information about the running package and its objects. These variables can be used in expressions and property expressions to customize packages, containers, tasks, and event handlers.   User-Defined variables : Defined by Package Developers   How to create user - define  variable?   How to set expression for variable   Connection Manager: SSIS provides different types of connection managers that enable packages to connect to a variety of data sources and servers: There are built-in connection managers that Setup installs when you install Integration Services. There are connection managers that are available for download from the Microsoft website. You can create your own custom connection manager if the existing connection managers do not meet your needs.   Let's see how we can add Connection Manager. 1)Solution Explorer > Connection Managers > New Connection Manager . You can see the list of connection managers for different type of connections.   2)Add connection manager.   After adding your connection. you can see the all connection here.   SSIS Toolbox: Steps: Menu bar > SSIS > select SSIS Toolbox. now, you can see SSIS Toolbox on the left side. SSIS Toolbox have list of tasks and containers that you can perform.   List of Containers: For each Loop Container : Runs a control flow repeatedly by using an enumerator. For Loop Container : Runs a control flow repeatedly by testing a condition. Sequence Container : Groups tasks and containers into control flows that are subsets of the package control flow.   List of Task: Data Flow Task The task that runs data flows to extract data, apply column level transformations, and load data.   Data Preparation Tasks These tasks do the following processes: copy files and directories; download files and data; run Web methods; apply operations to XML documents; and profile data for cleansing.   Workflow Tasks The tasks that communicate with other processes to run packages, run programs or batch files, send and receive messages between packages, send e-mail messages, read Windows Management Instrumentation (WMI) data, and watch for WMI events.   SQL Server Tasks The tasks that access, copy, insert, delete, and modify SQL Server objects and data.   Scripting Tasks The tasks that extend package functionality by using scripts.   Analysis Services Tasks The tasks that create, modify, delete, and process Analysis Services objects.   Maintenance Tasks The tasks that perform administrative functions such as backing up and shrinking SQL Server databases, rebuilding and reorganizing indexes, and running SQL Server Agent jobs. You can add task/container by dragging the task/container from SSIS toolbox to design area.   Data Flow Task : Drag the Data Flow task from SSIS Toolbox to design area and double click on it. You are now in Data flow tab. Now you can see that SSIS Toolbox has different components.   Type: Source : from where you want your data. Destination : it is where you want to move your data. Transformation : It is Operation that perform ETL(Extract, Transform, Load)   Conclusion: SQL Server Integration Services provide tasks to transform and validate data during the load process and transformations to insert data into your destination. Rather than create a stored procedure with T-SQL to validate or change data, is good to know about the different SSIS tasks and how they can be used.   RELATED BLOGS: Create SSIS Data Flow Task Package Programmatically 

Apache Kafka is the numerous common buffer solution deployed together with the ELK Stack. Kafka is deployed within the logs delivery and the indexing units, acting as a segregation unit for the data being collected: In this blog, we’ll see how to deploy all the components required to set up a resilient logs pipeline with Apache Kafka and ELK Stack: Filebeat – collects logs and forwards them to a Kafka topic. Kafka – brokers the data flow and queues it. Logstash – aggregates the data from the Kafka topic, processes it and ships to Elasticsearch. Elasticsearch – indexes the data. Kibana – for analyzing the data.   My environment: To perform the steps below, I set up a single Ubuntu 18.04 VM machine on AWS EC2 using local storage. In real-life scenarios, you will probably have all these components running on separate machines. I started the instance in the public subnet of a VPC and then set up a security group to enable access from anywhere using SSH and TCP 5601 (for Kibana). Using Apache Access Logs for the pipeline, you can use VPC Flow Logs, ALB Access logs etc. We will start by installing the main component in the stack — Elasticsearch. Login to your Ubuntu system using sudo privileges. For the remote Ubuntu server using ssh to access it. Windows users can use putty or Powershell to log in to Ubuntu system. Elasticsearch requires Java to run on any system. Make sure your system has Java installed by running the following command. This command will show you the current Java version. sudo apt install openjdk-11-jdk-headless Check the installation is successful or not by the below command ~$ java — versionopenjdk 11.0.3 2019–04–16OpenJDK Runtime Environment (build 11.0.3+7-Ubuntu-1ubuntu218.04.1)OpenJDK 64-Bit Server VM (build 11.0.3+7-Ubuntu-1ubuntu218.04.1, mixed mode, sharing) Finally, I added a new elastic IP address and associated it with the running instance. The example logs used for the tutorial are Apache access logs.   Step 1: Installing Elasticsearch We will start by installing the main component in the stack — Elasticsearch. Since version 7.x, Elasticsearch is bundled with Java so we can jump right ahead with adding Elastic’s signing key: Download and install the public signing key: wget -qO - https://artifacts.elastic.co/GPG-KEY-elasticsearch | sudo apt-key add - Now you may need to install the apt-transport-https package on Debian before proceeding: sudo apt-get install apt-transport-https echo "deb https://artifacts.elastic.co/packages/7.x/apt stable main" | sudo tee -a /etc/apt/sources.list.d/elastic-7.x.list Our next step is to add the repository definition to our system: echo “deb https://artifacts.elastic.co/packages/7.x/apt stable main” | sudo tee -a /etc/apt/sources.list.d/elastic-7.x.list You can install the Elasticsearch Debian package with: sudo apt-get update && sudo apt-get install elasticsearch Before we bootstrap Elasticsearch, we need to apply some basic configurations using the Elasticsearch configuration file at: /etc/elasticsearch/elasticsearch.yml: sudo su nano /etc/elasticsearch/elasticsearch.yml Since we are installing Elasticsearch on AWS, we will bind Elasticsearch to the localhost. Also, we need to define the private IP of our EC2 instance as a master-eligible node: network.host: "localhost" http.port:9200 cluster.initial_master_nodes: ["<InstancePrivateIP"] Save the file and run Elasticsearch with: sudo service elasticsearch start To confirm that everything is working as expected, point curl to: http://localhost:9200, and you should see something like the following output (give Elasticsearch a minute or two before you start to worry about not seeing any response): {   "name" : "elasticsearch",   "cluster_name" : "elasticsearch",   "cluster_uuid" : "W_Ky1DL3QL2vgu3sdafyag",   "version" : {     "number" : "7.2.0",     "build_flavor" : "default",     "build_type" : "deb",     "build_hash" : "508c38a",     "build_date" : "2019-06-20T15:54:18.811730Z",     "build_snapshot" : false,     "lucene_version" : "8.0.0",     "minimum_wire_compatibility_version" : "6.8.0",     "minimum_index_compatibility_version" : "6.0.0-beta1"   },   "tagline" : "You Know, for Search" }   Step 2: Installing Logstash Next up, the “L” in ELK — Logstash. Logstash and installing it is easy. Just type the following command. sudo apt-get install logstash -y Next, we will configure a Logstash pipeline that pulls our logs from a Kafka topic, processes these logs and ships them on to Elasticsearch for indexing. Verify Java is installed: java -version openjdk version "1.8.0_191" OpenJDK Runtime Environment (build 1.8.0_191-8u191-b12-2ubuntu0.16.04.1-b12) OpenJDK 64-Bit Server VM (build 25.191-b12, mixed mode) Let’s create a new config file: Since we already defined the repository in the system, all we have to do to install Logstash is run: sudo nano /etc/logstash/conf.d/apache.conf Next, we will configure a Logstash pipeline that pulls our logs from a Kafka topic, processes these logs, and ships them on to Elasticsearch for indexing. Let’s create a new config file: input {   kafka {     bootstrap_servers => "localhost:9092"     topics => "apache"     } } filter {     grok {       match => { "message" => "%{COMBINEDAPACHELOG}" }     }     date {     match => [ "timestamp" , "dd/MMM/yyyy:HH:mm:ss Z" ]     }   geoip {       source => "clientip"     } } output {   elasticsearch {     hosts => ["localhost:9200"]   } } As you can see — we’re using the Logstash Kafka input plugin to define the Kafka host and the topic we want Logstash to pull from. We’re applying some filtering to the logs and we’re shipping the data to our local Elasticsearch instance.   Step 3: Installing Kibana Let’s move on to the next component in the ELK Stack — Kibana. As before, we will use a simple apt command to install Kibana: sudo apt-get install kibana We will then open up the Kibana configuration file at: /etc/kibana/kibana.yml, and make sure we have the correct configurations defined: server.port: 5601 server.host: "<INSTANCE_PRIVATE_IP>" elasticsearch.hosts: ["http://<INSTANCE_PRIVATE_IP>:9200"] Then enable and start the Kibana service: sudo systemctl enable kibana sudo systemctl start kibana We would need to install Firebeat. Use: sudo apt install filebeat   Open up Kibana in your browser with http://<PUBLIC_IP>:5601. You will be presented with the Kibana home page.

In this article, we will review how to create a data flow task package of SSIS in Console Application with an example. Requirements Microsoft Visual Studio 2017 SQL Server 2014 SSDT Article  Done with the above requirements? Let's start by launching Microsoft Visual Studio 2017. Create a new Console Project with .Net Core.  After creating a new project, provide a proper name for it. In Project Explorer import relevant references and ensure that you have declared namespaces as below: using Microsoft.SqlServer.Dts.Pipeline.Wrapper; using Microsoft.SqlServer.Dts.Runtime; using RuntimeWrapper = Microsoft.SqlServer.Dts.Runtime.Wrapper;   To import above namespaces we need to import below refrences.   We need to keep in mind that, above all references should have same version.   After importing namespaces, ask user for the source connection string, destination connection string and table that will be copied to destination. string sourceConnectionString, destinationConnectionString, tableName; Console.Write("Enter Source Database Connection String: "); sourceConnectionString = Console.ReadLine(); Console.Write("Enter Destination Database Connection String: "); destinationConnectionString = Console.ReadLine(); Console.Write("Enter Table Name: "); tableName = Console.ReadLine();   After Declaration, create instance of Application and Package. Application app = new Application(); Package Mipk = new Package(); Mipk.Name = "DatabaseToDatabase";   Create OLEDB Source Connection Manager to the package. ConnectionManager connSource; connSource = Mipk.Connections.Add("ADO.NET:SQL"); connSource.ConnectionString = sourceConnectionString; connSource.Name = "ADO NET DB Source Connection";   Create OLEDB Destination Connection Manager to the package. ConnectionManager connDestination; connDestination= Mipk.Connections.Add("ADO.NET:SQL"); connDestination.ConnectionString = destinationConnectionString; connDestination.Name = "ADO NET DB Destination Connection";   Insert a data flow task to the package. Executable e = Mipk.Executables.Add("STOCK:PipelineTask"); TaskHost thMainPipe = (TaskHost)e; thMainPipe.Name = "DFT Database To Database"; MainPipe df = thMainPipe.InnerObject as MainPipe;   Assign OLEDB Source Component to the Data Flow Task. IDTSComponentMetaData100 conexionAOrigen = df.ComponentMetaDataCollection.New(); conexionAOrigen.ComponentClassID = "Microsoft.SqlServer.Dts.Pipeline.DataReaderSourceAdapter, Microsoft.SqlServer.ADONETSrc, Version=14.0.0.0, Culture=neutral, PublicKeyToken=89845dcd8080cc91"; conexionAOrigen.Name = "ADO NET Source";   Get Design time instance of the component and initialize it. CManagedComponentWrapper instance = conexionAOrigen.Instantiate(); instance.ProvideComponentProperties();   Specify the Connection Manager. conexionAOrigen.RuntimeConnectionCollection[0].ConnectionManager = DtsConvert.GetExtendedInterface(connSource); conexionAOrigen.RuntimeConnectionCollection[0].ConnectionManagerID = connSource.ID;   Set the custom properties. instance.SetComponentProperty("AccessMode", 0); instance.SetComponentProperty("TableOrViewName", "\"dbo\".\"" + tableName + "\"");   Reinitialize the source metadata. instance.AcquireConnections(null); instance.ReinitializeMetaData(); instance.ReleaseConnections();   Now, Add Destination Component to the Data Flow Task. IDTSComponentMetaData100 conexionADestination = df.ComponentMetaDataCollection.New(); conexionADestination.ComponentClassID = "Microsoft.SqlServer.Dts.Pipeline.ADONETDestination, Microsoft.SqlServer.ADONETDest, Version=14.0.0.0, Culture=neutral, PublicKeyToken=89845dcd8080cc91"; conexionADestination.Name = "ADO NET Destination";   Get Design time instance of the component and initialize it. CManagedComponentWrapper instanceDest = conexionADestination.Instantiate(); instanceDest.ProvideComponentProperties();   Specify the Connection Manager. conexionADestination.RuntimeConnectionCollection[0].ConnectionManager = DtsConvert.GetExtendedInterface(connDestination); conexionADestination.RuntimeConnectionCollection[0].ConnectionManagerID = connDestination.ID;   Set the custom properties. instanceDest.SetComponentProperty("TableOrViewName", "\"dbo\".\"" + tableName + "\"");   Connect the source to destination component: IDTSPath100 union = df.PathCollection.New(); union.AttachPathAndPropagateNotifications(conexionAOrigen.OutputCollection[0], conexionADestination.InputCollection[0]);   Reinitialize the destination metadata. instanceDest.AcquireConnections(null); instanceDest.ReinitializeMetaData(); instanceDest.ReleaseConnections();   Map Source input Columns and Destination Columns foreach (IDTSOutputColumn100 col in conexionAOrigen.OutputCollection[0].OutputColumnCollection) {     for (int i = 0; i < conexionADestination.InputCollection[0].ExternalMetadataColumnCollection.Count; i++)     {         string c = conexionADestination.InputCollection[0].ExternalMetadataColumnCollection[i].Name;         if (c.ToUpper() == col.Name.ToUpper())         {             IDTSInputColumn100 column = conexionADestination.InputCollection[0].InputColumnCollection.New();             column.LineageID = col.ID;             column.ExternalMetadataColumnID = conexionADestination.InputCollection[0].ExternalMetadataColumnCollection[i].ID;         }     } }   Save Package into the file system. app.SaveToXml(@"D:\Workspace\SSIS\Test_DB_To_DB.dtsx", Mipk, null);   Execute package. Mipk.Execute(); Conclusion In this article, we have explained one of the alternatives for creating SSIS packages using .NET console application. In case you have any questions, please feel free to ask in the comment section below.   RELATED BLOGS: Basics of SSIS(SQL Server Integration Service)

In this article, we will review on DELETE AND UPDATE CASCADE rules in SQL Server foreign key with different examples. DELETE CASCADE: When we create a foreign key using this option, it deletes the referencing rows in the child table when the referenced row is deleted in the parent table which has a primary key. UPDATE CASCADE: When we create a foreign key using UPDATE CASCADE the referencing rows are updated in the child table when the referenced row is updated in the parent table which has a primary key. We will be discussing the following topics in this article: Creating DELETE CASCADE and UPDATE CASCADE rule in a foreign key using T-SQL script Triggers on a table with DELETE or UPDATE cascading foreign key Let us see how to create a foreign key with DELETE and UPDATE CASCADE rules along with few examples.   Creating a foreign key with DELETE and UPDATE CASCADE rules Please refer to the below T-SQL script which creates a parent, child table and a foreign key on the child table with DELETE CASCADE rule.   Insert some sample data using below T-SQL script.   Now, Check Records.   Now I deleted a row in the parent table with CountryID =1 which also deletes the rows in the child table which has CountryID =1.   Please refer to the below T-SQL script to create a foreign key with UPDATE CASCADE rule.   Now update CountryID in the Countries for a row which also updates the referencing rows in the child table States.   Following is the T-SQL script which creates a foreign key with cascade as UPDATE and DELETE rules. To know the update and delete actions in the foreign key, query sys.foreign_keys view. Replace the constraint name in the script.   The below image shows that a DELETE CASCADE action and UPDATE CASCADE action is defined on the foreign key. Let’s move forward and check the behavior of delete and update rules the foreign keys on a child table which acts as parent table to another child table. The below example demonstrates this scenario. In this case, “Countries” is the parent table of the “States” table and the “States” table is the parent table of Cities table.   We will create a foreign key now with cascade as delete rule on States table which references to CountryID in parent table Countries.   Now on the Cities table, create a foreign key without a DELETE CASCADE rule. If we try to delete a record with CountryID = 3, it will throw an error as delete on parent table “Countries” tries to delete the referencing rows in the child table States. But on Cities table, we have a foreign key constraint with no action for delete and the referenced value still exists in the table.   The delete fails at the second foreign key.   When we create the second foreign key with cascade as delete rule then the above delete command runs successfully by deleting records in the child table “States” which in turn deletes records in the second child table “Cities”.   Triggers on a table with delete cascade or update cascade foreign key An instead of an update trigger cannot be created on the table if a foreign key on with UPDATE CASCADE already exists on the table. It throws an error “Cannot create INSTEAD OF DELETE or INSTEAD OF UPDATE TRIGGER ‘trigger name’ on table ‘table name’. This is because the table has a FOREIGN KEY with cascading DELETE or UPDATE.” Similarly, we cannot create INSTEAD OF DELETE trigger on the table when a foreign key CASCADE DELETE rule already exists on the table.   Conclusion In this article, we explored a few examples on DELETE CASCADE and UPDATE CASCADE rules in SQL Server foreign key. In case you have any questions, please feel free to ask in the comment section below.

What is table partitioning in SQL? Table partitioning is a way to divide a large table into smaller, more manageable parts without having to create separate tables for each part. Data in a partitioned table is physically stored in groups of rows called partitions and each partition can be accessed and maintained separately. Partitioning is not visible to end-users, a partitioned table behaves like one logical table when queried. Data in a partitioned table is partitioned based on a single column, the partition column often called the partition key. Only one column can be used as the partition column, but it is possible to use a computed column. The partition scheme maps the logical partitions to physical filegroups. It is possible to map each partition to its own filegroup or all partitions to one filegroup.

We have to add the below script in the master database to restore an encrypted database. CREATE MASTER KEY ENCRYPTION BY PASSWORD = '<your_password>' CREATE CERTIFICATE <your_certificate_name> FROM File = '<path of.cer file>' WITH PRIVATE KEY (FILE = 'path of .pvk file', DECRYPTION BY PASSWORD = '<your_password>');   Now you have to do follow the normal restore process in SQL.

The most underrated but most important topic, which is a must while implementing the SQL Query, Stored Procedures, or Functions. While implementing any SQL operations knowing the syntax and structures is a good thing, but one must know optimization. Without knowledge of optimization, any developer can create DDL and DML statements, but they are not well-designed procedures. You know why? Because while executing those statements there are chances that it will take time or may create a deadlock situation. Proper joining is also considered to be part of optimization. This above is an actual execution plan flowchart. The most simple query execution flow is mentioned below: From Joins Where Group by Having clause Column list Distinct Order by Top From: First it will fetch all the records from the table mentioned after the ‘From’ keyword. Join: joins are an essential part of any SQL statements. A developer must have proper knowledge of tables; otherwise, it can cause the wrong data population of data or extended execution time. Where: It another filter applied to any query after applying joins. It is used to decrease no. of records provided filter wise. Group by: It is used for grouping the records with aggregate functions or grouping the records particular provided column-wise. Having: When we need to provide an aggregate function with a filter then we should use it in the ‘Having’ clause. Column list: While putting ‘*’, we are calling all the columns and all records from a particular table. If not necessary then we must provide only those columns which are actually useful. Distinct: It is used to remove duplicate records while fetching the details through a select statement. Order by: This is useful to sort the data ascending or descending column-wise. Top: It is used to limit the no. of records to be displayed on the screen. Above mentioned query execution flow is 1st step of the optimization ever keyword should be placed as per the above plan. The last 3 steps (7, 8, and 9) are most crucial because it will process all the records and then do operations accordingly. ‘Group by’ and ‘Having’ clauses are also taken time while execution because it uses aggregate functions in it. Along with that, it is necessary for you to know that if not necessary then don’t do for inbuilt functions. ‘Convert’ and ‘Cast’ is the most frequent built-in functions that can extend the execution because of conversion. As mentioned above Joins are the most important part of any SQL statement because a good join increases the performance where wrong can mislead you. First of all, while implementing a join please check whether tables are properly indexed or not. Indexing is very important while the creation of a table, a table must have at least one clustered index. The lessor or no use of temp tables. Temp tables tend to increase the complexities of the query because it increases the continuous use of ‘tempdb’ database. If necessary then create a clustered index on that temp table which increases the performance and doesn’t wait for the temp table to be dropped automatically, drop it when it is of no use. Go for the execution plan if the query is taking too much time, by seeing the plan we can easily fetch which query or portion of the query is taking the time. The execution plan shows which table used maximum process time from the overall time. Make your indexes unique using integer or unique identifier which increases the performances. A table must have one clustered key and can have one or more nonclustered keys. Use small data types for indexing. For the existence of any record don’t dependent on the count statement in the query. For example, Always use ‘with (nolock)’ keyword to avoid locks while fetching records from the table. Avoid the use of ‘NOT IN’ statement in where condition, instead of that you can go for let join. In the same way no need to go for the ‘IN’ statement, you can simply use inner join. Please avoid loops and cursors while creating any store procedure, because looping also causes CPU process usage and calling the same statement again and again. So avoid it is not needed. Use ‘UNION ALL’ instead of ‘UNION’ for combining two or more ‘select’ statements.