In this article, you will learn how to create simple or complex JSON file in Informatica just in a few clicks. We will use the Powerful Export JSON Command line Builder from ZappyShell. Using this Portable / xCopy style command line you can build nested/complex JSON / XML or even CSV from single / multiple SQL Queries as Input or Select Database Tables as Source. Later you can copy generated command line from Tool and execute in any tool like Informatica / Talend / DataStage or Automation Script like Python / PowerShell.

Prerequisite

This article assumes you have access to Informatica Developer Tools and basic knowledge of command-line tools.

1. Download ZappyShell Command line utility (Click on Download Button). You can get msi and Zip (Poratble / xCopy version)
2. If you want to use the Portable version just extract ZappyShell-xCopy zip to some folder lets say (C:\ZappyShell). If you installed MSI then its usually in C:\Program Files (x86)\ZappySys
3. Double click on DbCommandBuilder.exe as listed here  to get started. At the end of this exercise, you will be able to generate something like the below JSON File in few clicks in Informatica or any other Tool / Programming Language you prefer which has option to invoke command line…
4. For all possible command line options Refer this
5. So lets get started

Sample JSON Created by ZappyShell from Informatica

Sample JSON Created by Informatica Workflow (Create From Database SQL Query or Tables)

Create Command Line to Export JSON File from Database SQL Query / Tables

In this section, we will see how to build Command Line which you can then execute in Informatica Command Time and later Schedule Export Job to Produce JSON File on a predefined schedule or manually.

1. Open DbCommandBuilder.exe and configure the Connection / Data source(s) as listed here .
2. Select JSON Export Option from the Dropdown. You can also choose XML, Excel or CSV
3. You can use the Custom Layout Option if you wish to Export using custom Output Layout else just choose tables you like to export or type SQL query.
   
4. Configure your Input Dataset(s) (can be SQL Queries or Tables) and define Desired Layout for your Output File.
5. Here is just an example of how to define an Array for a nested layout in a JSON or XML file.
6. t the end of configuration you may see command line like below (more examples listed here)Example: Simple mode Export (Microsoft SQL Server)
   

   db.exe export customers --json -o c:\data\cust.json --connstr "Data Source=localhost;Initial Catalog=Northwind;Integrated Security=SSPI;" -y

   Example: For Simple mode Export (MySQL SQL Server – ODBC Driver)
   

   export customers --out c:\data\customers.json --json --overwrite --connstr "Driver={MySQL ODBC 5.2 ANSI Driver};Server=your_server_name_or_ip;Port=3306;Uid=user1;pwd=SomeP@ssword;Database=your_db_name"

   Example: For custom layout mode Export
   
   

   export --map c:\data\MySampleMap.xml -o c:\data\customers.json --json --connstr "Data Source=localhost;Initial Catalog=Northwind;Integrated Security=SSPI;" -y

7. Once the Command line is created you can click Run to test and then copy it into tools like Informatica Command Task (see next section).

Export / Create JSON File In Informatica

1. Once the command line is prepared (see previous section) we can open Informatica Developer (D)
2. Connect to your Repository and create a new workflow
3. Drag new Command Task, from Toolbox and double click to edit
   

   Informatica Command Task

4. Enter the sample command line we obtained from the previous step
5. Save the workflow and run it… That’s it you will see the JSON file generated

Export / Create XML File In Informatica

Same Steps as above except choose File format as XML and you will see output like the below once you run the command line.

Export / Create XML File In Informatica

Export / Create CSV File In Informatica

Same Steps as above except choose File format as CSV and you will see output like the below once you run the command line.

Generate CSV Files in Informatica

Export / Create Excel File In Informatica

Same Steps as above except choose File format as Excel and you will see output like the below once you run the command line.

Conclusion

In this post, we just saw how easy it is to generate JSON / XML / Excel or CSV files in tools like Informatica, Talend, python, PowerShell in few clicks without coding. You can schedule job to generate JSON / XML files from Database tables or SQL Query. Download ZappyShell Today and try it to see what else you can do with it.

The post Export / Create JSON File in Informatica (from SQL Query / Database Tables) appeared first on ZappySys Blog.

There are several tricks related to converting SQL to CSV. For example, dates, working with variables, converting text, working with stored procedures. In this article, we will work with several tips to convert SQL Server data to the CSV format.

For this article, we will use the following ZappySys component:

SSIS Export CSV File Task

How to convert SQL to CSV using specific dates

1. First, to convert the date to a specific format, you can use the Export CSV task.
2. This task allows you to convert SQL Server tables into CSV files. We will show how to set the date to a specific format:
   

   Export from SSIS SQL Server to CSV in a specific date format
3. The query used for this purpose is the following:
   

   SELECT [OrderID] ,FORMAT([OrderDate], 'dd/MM/yyyy') as mydateformat FROM [dbo].[Orders]

4. As you can see, you can send custom queries in the Export CSV task.
   In this example, we utilize the T-SQL Format function, which enables us to convert the date to a custom format. In this example, we are converting the OrderDate column into the format dd/MM/yyyy.
5. Also, specify the destination path:
   

   Export path csv
6. Finally, run the package. In this example, we converted the date to the format dd/MM/yyyy.

How to convert SQL Server to CSV using dynamic column names

1. A common question is to have column names based on SSIS variables. This time, we will show how to do this.
2. First of all, we will need an SSIS variable that will contain the column name:
   

   SSIS variable with a column name
3. Secondly, we need to use the value of the variable as the column name. We will use the alias (AS in SQL) to create a custom name.
   

   SSIS column name

4. The SQL query will look like this:
   

   SELECT [ShipperID] as {{User::column}} FROM [Northwind].[dbo].[Shippers]

5. The previous example used the SSIS variable named column to store the name of the column. If the Variable value is ‘column1’, the name of the column will be ‘column1’.

How to convert SQL Server to CSV to change the decimal point to a decimal comma

1. Most of the countries in the world use the decimal point or the decimal comma. For decimals, some countries use the comma and others the period. In this new example, we will show how to convert decimal data into a comma.
2. In order to convert, we will convert and work with a text data type. We will use the following script to create a table with data:
   

   create table decimalpoint
   (
   decimal nvarchar(50)
   )
   
   insert into decimalpoint values 
   ('3,45'),
   ('3,44'),
   ('6,56')

3. Secondly, we will use the following T-SQL Sentence: select replace(decimal,’,’,’.’) as mydecimal from decimalpoint.
   

   select replace(decimal,',','.') from decimalpoint

4. In addition, we will create a path to export.
5. If everything is fine, the CSV file created will convert the period decimal to a coma decimal.

How to export data from a parameterized SQL Server stored procedure into a
CSV file

1. In this new example, we will use a stored procedure with parameters and store the results into a CSV file.
2.  First of all, we will use the following parameterized stored procedure:
   

   create procedure [dbo].[Sales by Year]
   @Beginning_Date DateTime, @Ending_Date DateTime AS
   SELECT Orders.ShippedDate, Orders.OrderID, "Order Subtotals".Subtotal, 
   DATENAME(yy,ShippedDate) AS Year
   FROM Orders INNER JOIN "Order Subtotals" 
   ON Orders.OrderID = "Order Subtotals".OrderID
   WHERE Orders.ShippedDate Between @Beginning_Date And @Ending_Date
   GO

3. Secondly, we will invoke the stored procedure like this:
   

   Invoice stored procedure and export to csv

4. Finally, if everything is fine, the data will be exported to the CSV file after running the package.

Conclusion

In this article, we demonstrate how to export data to CSV from SQL Server using various date formats. We also learned how to change the types of decimals used. For more fun, please download our components.

The post How to convert SQL to CSV in SSIS appeared first on ZappySys Blog.

In this article, we will cover the points on how to load 10 million rows from SQL Server to Snowflake in just 3 minutes.

Snowflake is a data warehousing platform that resides in a cloud. Basically, it is a data warehouse software exposed as a service. It allows integrating many data sources via internal Snowflake Partner apps and loading them into the Snowflake storage engine. Another part of Snowflake is a computing engine that is responsible for serving your SQL queries. Both engines can work independently thus users that are querying a data warehouse are not affected by a data load that is happening at the same time. Snowflake is an elastic service, which means you pay only for the resources used. Specifically, you pay only for each second of the processing time you use.

To show how data loading works in Snowflake, we will take 10 million rows from SQL Server and load them into Snowflake using SSIS and ZappySys SSIS PowerPack. These SSIS PowerPack connectors will be used to achieve the task:

Export CSV File Task

Step-by-step – How to load 10 million rows from SQL Server to Snowflake in 3 minutes

Getting started

To achieve the goal, we will use a slightly modified Northwind database (example database from Microsoft) and SnowSQL – a command-line tool provided by Snowflake. So in the first steps, you will need to install the Northwind database and SnowSQL. After that, we will proceed to create a table in Snowflake – a table we will load the data into – and a file format which will be used to load data from the Snowflake staging area into the destination table. Once that’s complete, we will proceed in creating an SSIS package, adding and configuring the connectors, and finally running it to get the results. Let’s proceed!

Install a Northwind database

Download and run the creation script of a modified Northwind database. The only thing that was modified is a “CustomersForSnowflake” view added which returns 10M rows.

Install and configure SnowSQL command-line tool

Download and install SnowSQL; you will find the instructions on how to do that in https://docs.snowflake.net/manuals/user-guide/snowsql-install-config.html.

Once installed, you will need to configure the default Snowflake account name, user name, and password:

1. Go to Windows File Explorer and enter this path:
   %USERPROFILE%\.snowsql\
   Usually, when entered the path looks similar to this one:
   C:\Users\myUserName\.snowsql
2. Find a file named config and open it.
3. Then configure your credentials:

Create a table in Snowflake

Login to your Snowflake account, open a Worksheet and execute this query:

create or replace table Customers(
ID number,
CustomerID nchar(5),
CompanyName nvarchar(40),
ContactName nvarchar(30),
ContactTitle nvarchar(30),
Address nvarchar(60),
City nvarchar(15),
Region nvarchar(15),
PostalCode nvarchar(10),
Country nvarchar(15),
Phone nvarchar(24),
Fax nvarchar(24)
)

We will load 10M customers from the Northwind database and load them into this table.

Create a file format in Snowflake

Then in the same Worksheet create a file format for zipped CSV files by executing this query:

create or replace file format GzipCsvFormat
type = csv
field_delimiter = ','
null_if = ('NULL', 'null')
empty_field_as_null = true
compression = gzip
field_optionally_enclosed_by = '"'

We will use this file format when loading data from a Snowflake stage to the Snowflake destination table. This file format defines CSV format which is used by ZappySys Export CSV Task in SSIS.

Create an SSIS package

We are ready to create a new SSIS package and load some data into Snowflake. We are going to use two methods of how we load data into Snowflake. In one method we will load data into Snowflake’s local storage, while in the second one we will stage data in Amazon S3 bucket. Decide how you want to stage files, and then choose the appropriate workflow when creating a new package:

Loading 10 million rows from SQL Server to Snowflake

Add & configure ZappySys Export CSV Task

Firstly, drag and drop ZappySys Export CSV Task from SSIS toolbox and follow the instructions on how to configure it:

Configure source

1. Proceed with configuring the data source, from which you will be exporting data. We will use the Northwind database as an example:
   

   Export CSV Task: configuring OLE DB Connection to export data from SQL Server to Snowflake

2. Then move on and select it and enter the query or table name you want to export data from:
   

   Export CSV Task: configuring source SQL query for data loading from SQL Server to Snowflake

   We will be using this query:
   select top 10000000 * from CustomersForSnowflake

3. Then in the Split Options tab split the exported CSV into many files, e.g. into 50 MB chunks:
   

   Using data split options in the Export CSV Task

   NOTE: Snowflake recommends having files that are 10-100 MB in size when compressed. So it is perfectly safe to configure the value to 100 MB and above.

Configure target

Depending on which staging approach you chose to use, set the export target to a local path or S3 bucket:

Using local Snowflake storage

Just set the appropriate Save Mode and a file path:

Saving files locally to upload them to Snowflake local stage later

Using S3 storage

For storing staging files in S3, follow these steps:

1. Set Save Mode to Save to Connection and select <New ZS-AWS-STORAGE Connection>:
   

   Export CSV Task: configuring Amazon S3 endpoint as the target for data export

2. Then select S3 as Storage Service and fill in Access and Secret Key, select your region (optional):
   

   Configuring Amazon Connection Manager to store staging data in S3

3. Then in the Export CSV Task window, in Target tab configure it similarly:
   

   Export CSV Task: configuring target to store staging data in S3

   Another option you may consider is to use Amazon Storage Task or Azure Storage Task to upload files to S3 in a separate step after the Export CSV Step. For this perform the following steps.

   1. On the Target tab, set Save Mode to Save to Path (Local Disk) instead of Connection to S3 or Azure.
   2. Drag & Drop Amazon Storage Task or Azure Storage Task  and connect with the previous Export CSV Step
   3. Configure Storage Task to upload Local files to S3 or Azure Blob Storage.
   4. Continue to the next section for more instructions.

Add Execute Process Task to create a staging area

We are ready to add Execute Process Task to create a staging area in Snowflake. Again, depending on where you will store staging files, Snowflake’s local storage or Amazon S3, use one of the approaches below:

Create Snowflake local staging

Creating a local stage in Snowflake using SnowSQL command-line tool and SSIS

File path:

C:\Program Files\Snowflake SnowSQL\snowsql.exe

Arguments:

-q "CREATE OR REPLACE STAGE CustomersStaging" -d DEMO_DB -s Public

Create Amazon S3 staging

Creating an Amazon S3 stage in Snowflake using SnowSQL command-line tool and SSISFile path:

C:\Program Files\Snowflake SnowSQL\snowsql.exe

Arguments:

CREATE OR REPLACE STAGE CustomersStaging url='s3://your-bucket-name/destinationFolder/' credentials=(aws_key_id='AKIAXXXXXXXXXXXXXXXX' aws_secret_key='6p1ayaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaLXz88')

Add Execute Process Task to upload files to the staging area (local staging approach only)

We still need to upload CSV files that were saved locally for local Snowflake staging. So add another Execute Process Task and configure it similarly:

Uploading local CSV files to Snowflake stage using SnowSQL command-line tool and SSISFile path:

C:\Program Files\Snowflake SnowSQL\snowsql.exe

Arguments:

-q "PUT file://e:/temp/*.csv @DEMO_DB.PUBLIC.CustomersStaging"

Add Execute Process Task to copy files from the Snowflake/Amazon S3 stage to Snowflake table

Finally, add the final Execute Process Task to issue a command to load files from the stage into a real Snowflake table:

Copying data from a staging area to a Snowflake table using SSIS

File path:

C:\Program Files\Snowflake SnowSQL\snowsql.exe

Arguments:

-q "COPY INTO CUSTOMERS FROM @CustomersStaging file_format = (format_name = 'GZIPCSVFORMAT')" -d DEMO_DB -s Public

Execute the package

We are ready to execute the package, so just run it. In this example you see the workflow when using Amazon S3 as stage:

Executing an SSIS package to load data from Snowflake to SQL Server

The Results

Once the package executes, we can check the results:

Successful execution of SSIS package when loading data from Snowflake to SQL Server

If we go to Snowflake and execute the SELECT query, we see all 10M customers loaded:

Successful loading of 10 million rows from SQL Server to Snowflake using SSIS and ZappySys SSIS PowerPack

All done in less than 3 mins:

Conclusion

We achieved just what we aimed for – we loaded 10 million rows from SQL Server to Snowflake; all done in less than 3 minutes! Two approaches were taken into consideration: in one we staged CSV files in the local Snowflake stage and in the other we staged files in the Amazon S3 bucket. To accomplish this we used ZappySys Export CSV Task, which allowed us to export data from the SQL Server view to CSV format, split files into smaller ones, zip them and store them locally or upload to the Amazon S3 bucket. Finally, we used a standard SSIS Task “Execute Process Task” to issue Snowflake commands such as creating the stage area in Snowflake, uploading CSVs into the local stage and loading data into the Snowflake table.

Download a sample package

Load 10M rows from SQL Server into Snowflake.dtsx.zip

References

https://docs.snowflake.net/manuals/user-guide/data-load-bulk.html

https://docs.snowflake.net/manuals/user-guide/snowsql.html

https://docs.snowflake.net/manuals/user-guide/data-load-s3-create-stage.html

https://docs.snowflake.net/manuals/sql-reference/sql/create-file-format.html

https://docs.snowflake.net/manuals/sql-reference/sql/put.html

https://docs.snowflake.net/manuals/sql-reference/sql/copy-into-table.html

The post Load 10M rows from SQL Server to Snowflake in 3 minutes appeared first on ZappySys Blog.

In our previous blog we saw How to write data into CSV file in SSIS (GZip / Split). Now in this blog, we will see How to Pivot CSV Data in SSIS using CSV Source. It also supports Pivot mode so you can convert single CSV string value into Rows. In this article we also see how make some CSV columns as parent and not consider it in pivot and load it as it is using JSON Generator and JSON Parser Transform Pivot Option.

In nutshell, this post will focus on how to Pivot CSV Data into multiple columns and rows.

How to Pivot CSV Data in SSIS using CSV File Source

Let´s start with an example. In this SSIS CSV Parser Transform example, we will Parse database column string into multiple columns and rows.

1. First of All, Drag and drop Data Flow Task from SSIS Toolbox and double click it to edit.
   

   Drag and Drop : SSIS Data Flow Task from SSIS Toolbox

2. Furthermore, drag and drop the CSV Source and Double click on it to configure it.
   

   Drag and Drop : CSV Source from SSIS Toolbox

3. Enter the CSV file path and select desire column delimiter most common in CSV is comma and check the First Row as headers and click on preview.
   

   CSV Source Preview

4. Now, go to Pivot Columns to Rows Tab and checked Enable Pivoting and click on Preview Data to view the data.
   

   CSV Source Pivoted Data Preview

5. That’s it we see how to read data from CSV File and also Pivot the CSV Data using CSV Source.
6. Now In the next section we will see how to how make some CSV columns as parent and not consider it in pivot and load it as it is.

How make some CSV columns as parent.

Sometimes you just need to make child columns in to rows and want to leave parent level columns as it is.

1. First of All, Drag and drop Data Flow Task from SSIS Toolbox and double click it to edit.
   

   Drag and Drop : SSIS Data Flow Task from SSIS Toolbox

2. Secondly, drag and drop the CSV Source and Double click on it to configure it.
   

   Drag and Drop : CSV Source from SSIS Toolbox

3. Furthermore, enter the file path and click on preview.
   

   CSV Source Preview

4. For example if we want to pivot Email and Phone only in pivoting and wants to exclude parent level columns FirstName and LastName as it is.
5. For that drag and drop JSON Generator Transform and Select Output Mode as Output One JSON Per Parent Input Row  and Add Unbound Nested Element and named it as Persons and click on OK.
   

   JSON Generator Add Unbound Nested Element

6. Now add that two parent level fields FirstName and LastName in the Persons.
   

   JSON Generator Add Multiple Elements

7. Now select Person Element and right click and Add Unbound Nested Element and named it as Contacts and click on OK.
   

   JSON Generator Add Unbound Nested Element

8. Now add that two child level fields Email and Phone in the Contacts.
   

   JSON Generator Add Multiple Elements

9. Therefore JSON Generator Transform looks like below screen. Now Copy that Sample Preview JSON String to use it in the JSON Parser Transom as sample string.
   

   JSON Generator Transform – Sample Data

10. Similarly, drag and drop JSON Parser Transform and select the Input JSON Column and Paste that copied Sample JSON String and select the Contacts filter to read the Child data and click on Include Parent.
    

    JSON Parser Transform – Select Filter

11. Now go to Pivot Columns to Rows Tab and checked the Enable Pivoting and click on the Preview.
    

    JSON Parser Transform – Enable Pivoting

12. Finally, we are ready to load this data.
    

    CSV Pivoted Data

13. Let’s load this data in the SQL Server.

Load Pivoted CSV File data into SQL Server

ZappySys SSIS PowerPack makes it easy to load data from various sources such as REST, SOAP, JSON, XML, CSV or from other source into SQL Server, or PostgreSQL, or Amazon Redshift, or other targets. The Upsert Destination component allows you to automatically insert new records and update existing ones based on key columns. Below are the detailed steps to configure it.

Step 1: Add Upsert Destination to Data Flow

1. Drag and drop the Upsert Destination component from the SSIS Toolbox.
2. Connect your source component (e.g., JSON / REST / Other Source) to the Upsert Destination.

SSIS - Data Flow - Drang and Drop Upsert Destination Component

Step 2: Configure Target Connection

1. Double-click the Upsert Destination component to open the configuration window.
2. Under Connection, select an existing target connection or click NEW to create a new connection.
   • Example: SQL Server, or PostgreSQL, or Amazon Redshift.

Step 3: Select or Create Target Table

1. In the Target Table dropdown, select the table where you want to load data.
2. Optionally, click NEW to create a new table based on the source columns.

Configure SSIS Upsert Destination Connection - Loading data (REST / SOAP / JSON / XML /CSV) into SQL Server or other target using SSIS

Step 4: Map Columns

1. Go to the Mappings tab.
2. Click Auto Map to map source columns to target columns by name.
3. Ensure you check the Primary key column(s) that will determine whether a record is inserted or updated.
4. You can manually adjust the mappings if necessary.

SSIS Upsert Destination - Columns Mappings

Step 5: Save Settings

• Click OK to save the Upsert Destination configuration.

Step 6: Optional: Add Logging or Analysis

• You may add extra destination components to log the number of inserted vs. updated records for monitoring or auditing purposes.

Step 7: Execute the Package

• Run your SSIS package and verify that the data is correctly inserted and updated in the target table.

SSIS Upsert Destination Execution

Conclusion

After all, we saw you how to read CSV file data, pivot the data and Similarly, using SSIS JSON Generator and Parser Transform Pivot the CSV Data excluding some columns from pivoting.  To explore many other scenarios not discussed in this article download SSIS PowerPack from here (includes 70+ Components).

References

Finally, you can use the following URL for more information.

• Help File: CSV Source

The post How to Pivot CSV Data in SSIS appeared first on ZappySys Blog.

In our previous blog, we saw how to write data to a CSV file in SSIS (GZip/Split). In this blog, we will parse a CSV string into multiple columns and rows using the SSIS CSV Parser Transform (helpful for extracting data from a raw CSV string stored as a database column or from another source). It also supports Pivot mode, allowing you to convert a single CSV string value into Rows.

In a nutshell, this post will focus on parsing a CSV string into multiple columns and rows using the CSV Parser Transform.

Prerequisite

1. First, you will need to have SSIS installed
2. Secondly, make sure to have SSDT
3. Finally, do not forget to install ZappySys SSIS PowerPack

How to Extract Data from SQL Server Table and Parse a string into multiple columns and rows.

Let’s start with an example. In this SSIS CSV Parser Transform example, we will parse a database column containing a CSV string into multiple columns and rows.

1. First of all, drag and drop the Data Flow Task from the SSIS Toolbox and double-click it to edit.
   

   Drag and Drop SSIS Data Flow Task from SSIS Toolbox
2. Furthermore, drag and drop the OLE DB Source.
   

   OLE DB Source – Drag and Drop
3. Double-click the OLE DB Source to configure it, then click OK.
   

   Configure OLE DB Source Editor

4. Now drag and drop the CSV Parser Transform, then connect it to the OLE DB source.
5. Double-click on CSV Parser Transform and select that column from the drop-down, enter sample data, and click on Preview.
   

   CSV Parser Transform Preview

6. That’s it, now you can use that data in your desired destination.
   

   CSV Parser Transform result

SSIS CSV Parser Transform – Pivot Option (Normalize CSV string)

If Pivot Option is enabled, you will see two extra columns in the output (Pivot_Name and Pivot_Value).

Property name will appear in the “Pivot_Name” column, and property value will appear in the ”Pivot_Value’’ column. This option is beneficial when your data has a dynamic number of attributes and you want to store them as rows rather than columns—parent level.

SSIS CSV Parser Transform – Pivot Option (Normalize CSV string)

Conclusion

After all, we saw how to parse a string into multiple columns and rows using the SSIS CSV Parser Transform from a SQL Server Table using an OLE DB Connection.  To explore many other scenarios not discussed in this article, download SSIS PowerPack from here (includes 70+ Components).

References

Finally, you can use the following URL for more information.

• Help File: CSV Parser Transform

The post Parse CSV string into multiple columns and rows using SSIS appeared first on ZappySys Blog.

There might be a case when you have many CSV, JSON or XML files in Amazon S3 bucket and you want them to be imported straight into a SQL Server table. Here come ZappySys ODBC PowerPack and ZappySys Data Gateway (part of ODBC PowerPack) which will enable you to accomplish that. ZappySys ODBC PowerPack includes powerful Amazon S3 CSV, Amazon S3 JSON and Amazon S3 XML drivers that let you connect to an S3 bucket and read the contents from the files in the bucket. Bringing in ZappySys Data Gateway allows doing that right from a SQL Server. Let’s begin and see how to import Amazon S3 files into SQL Server.

These drivers are related to this article:

Amazon S3 Driver (for XML Files)

Amazon S3 Driver (for JSON Files)

Amazon S3 Driver (for CSV Files)

Prerequisites

1. Have an AWS account.
2. Existing Amazon S3 bucket.
3. SQL Server instance installed (can be a SQL Server Express LocalDB instance).
4. SQL Server Management Studio (SSMS) installed.
5. ZappySys ODBC PowerPack installed (must be installed on a Windows machine; can be a different machine than SQL Server is installed on).

Getting started

We will start from a simple example of how to load contacts from a JSON that are located in Amazon S3 bucket, then we will proceed by loading contacts from many XML files that are compressed in ZIP, and finally, we will traverse many folders and subfolders to gather CSVs and load contacts from them.

Step-by-Step – Import JSON file located at Amazon S3 into SQL Server

Open and configure ZappySys Data Gateway

Create an Amazon S3 JSON data source in ZappySys Data Gateway

1. The first thing you will have to do is to create a data source in ZappySys Data Gateway. Just click Add button, give the data source a name, e.g. “MyContactsJSON“, and then select Native – ZappySys Amazon S3 JSON Driver:
   

   Adding a JSON data source in ZappySys Data Gateway to load it from Amazon S3 bucket into SQL Server

2. Then click Edit and add the Data Gateway user you created in Users tab. We will use this user later when adding a Linked Server to the Data Gateway to authenticate:
   

   Adding a user to the newly created Amazon S3 JSON data source

3. Once you do that, then click Edit to configure the data source:
   

   Preparing to configure the Amazon S3 JSON data source to load a JSON from Amazon S3 bucket to SQL Server

4. When a window opens, click Click here to Configure the Connection and enter your Access Key and Secret Key:
   

   Configuring the authentication to Amazon S3 bucket in ZappySys Data Gateway

5. After that, select a JSON file you want to load, and then click Select Filter button to choose data you want to be displayed in SQL Server:
   

   Configuring the JSON data source to load a JSON from Amazon S3 bucket into SQL Server

6. Go to the Preview tab and click the Preview Data button to make sure everything is configured correctly and preview the results:
   

   Previewing JSON data in the data source based on Amazon S3 JSON Driver

Now you are ready to create a Linked Server and connect to the data source you just created. If you used “MyContactsJSON” as the data source name, make sure to use the same name when creating a Linked Server.

Set up a SQL Server Linked Server

Execute the SQL query

Once you created the Linked Server to ZappySys Data Gateway, you are ready to execute the SQL query and load data into SQL Server. Supposedly, you created the Linked Server with the name “GATEWAY”, then open SSMS and execute this query:

SELECT * INTO MyContacts FROM openquery([GATEWAY], 'SELECT * FROM $')
SELECT * FROM MyContacts

The results

You should see similar results after you execute the query:

The results of getting JSON data straight from Amazon S3 bucket using ZappySys Amazon S3 JSON driver

Now we are ready to proceed to the next section and import many XML files at once.

Step-by-Step – Import many XML files located at Amazon S3 into SQL Server

Overview

Supposedly, you have many XMLs where each one is zipped and you want to load them all into a SQL Server table:

Zipped XML files located in Amazon S3 bucket to be loaded into SQL Server

The first thing you will have to do is to create a data source, based on ZappySys Amazon S3 XML Driver. Let’s proceed and just do that.

Create an Amazon S3 XML data source in ZappySys Data Gateway

Follow the same steps as in Create an Amazon S3 JSON data source in ZappySys Data Gateway section when adding a new data source, except for these two steps:

1. When adding a data source select Native – ZappySys Amazon S3 XML Driver as Connector Type:
   

   Choosing “ZappySys Amazon S3 XML Driver” to load XMLs to SQL Server

2. Then click Edit and edit the data source similarly like in this window:
   

   Configuring data source based on ZappySys Amazon S3 XML Driver to load XMLs into SQL Server

Now we are ready to set up a Linked Server to this newly created data source.

Set up a SQL Server Linked Server

Follow the very same steps from the section above where we added the Linked Server to a JSON data source, except that change Datasource property of the Linked Server to match the name of the Amazon S3 XML data source you created. Once you do that, you are ready to load XMLs into your SQL Server.

Execute the SQL query

Again, if you created the Linked Server with name “GATEWAY”, execute this SQL query in SSMS:

SELECT * INTO MyContacts FROM openquery([GATEWAY], 'SELECT * FROM $')
SELECT * FROM MyContacts

The results

You should see a similar view once you execute the query:

The data of many XMLs loaded from Amazon S3 bucket into SQL Server

Now we are ready to move to the next section and recursively scan CSVs and load them into SQL Server.

Step-by-Step – Import CSV file located at Amazon S3 into SQL Server

Overview

Let’s say you have many CSVs that are located in a folder tree, including subfolders and you want them all in your SQL Server:

Many CSV files located in Amazon S3 bucket folders and subfolders to be loaded into SQL Server

We will follow the same steps to accomplish that as we did for loading JSON and XMLs.

Create an Amazon S3 CSV data source in ZappySys Data Gateway

Follow the steps as in Create an Amazon S3 JSON data source in ZappySys Data Gateway section when adding a new data source, except for these two steps:

1. When adding a CSV data source select Native – ZappySys Amazon S3 CSV Driver as Connector Type:
   

   Choosing “ZappySys Amazon S3 CSV Driver” to load CSVs to SQL Server

2. Then click Edit and edit the data source similarly:
   

   Configuring data source based on ZappySys Amazon S3 CSV Driver to load CSVs into SQL Server

Now we are ready to proceed and add a Linked Server to this data source.

Set up a SQL Server Linked Server

Again, follow the very same steps from the section above where we added the Linked Server to a JSON data source, except that change Datasource property of the Linked Server to match the name of the Amazon S3 CSV data source we created. Once you do that, you are ready to load CSVs into your SQL Server.

Execute the SQL query

Once again, if you created the Linked Server with name “GATEWAY”, execute this SQL query in SSMS:

SELECT * INTO MyContacts FROM openquery([GATEWAY], 'SELECT * FROM $')
SELECT * FROM MyContacts

The results

You should see a similar view, once you execute the query:

The results of loading many CSVs from Amazon S3 bucket into SQL Server

Conclusion

We had a goal to import Amazon S3 files into SQL Server from SQL Server itself. We learned how to load JSON, XML and CSV files into SQL Server, which some of them were zipped and were located at different folders and subfolders. It was possible to accomplish that using ODBC PowerPack, ZappySys Data Gateway and ODBC PowerPack drivers – Amazon S3 JSON Driver, Amazon S3 XML Driver, and Amazon S3 CSV Driver. At first, we created data sources in the Data Gateway, then we created Linked Servers in SQL Server and connected to ZappySys Data Gateway, which finally made it possible to load data from Amazon S3 bucket straight from SQL queries in SSMS.

Check Amazon S3 Integration with Other BI Tools (Power BI, Excel, SSRS, MS Access, etc.)

ZappySys ODBC Drivers built using ODBC standard which is widely adopted by industry for a long time. Which mean the majority of BI Tools / Database Engines / ETL Tools already there will support native / 3rd party ODBC Drivers. Below is the small list of most popular tools / programming languages our Drivers support. If your tool / programming language doesn't appear in the below list, which means we have not documented use case but as long as your tool supports ODBC Standard, our drivers should work fine.  

BI / Reporting Tools Integration	ETL Tools Integration	Programming Languages Integration
Microsoft Power BI Tableau SSRS (SQL Reporting Services) QlikView /Qlik Sense MicroStrategy Google Sheet Microsoft Excel Microsoft Access Oracle OBIEE Many more (not in this list).....	Informatica PowerCenter (Windows) Informatica Cloud SSIS (SQL Integration Services) SQL Server Talend Data Studio Pentaho Kettle Oracle OBIEE Many more (not in this list).....	Visual Studio C# C++ JAVA Python PHP PowerShell T-SQL (Using Linked Server)

 

The post Import Amazon S3 files into SQL Server (CSV/JSON/XML Driver) appeared first on ZappySys Blog.

There might be a case when you have many CSV, JSON or XML files in Azure Blob and you want them to be imported straight into a SQL Server table. Here come ZappySys ODBC PowerPack and ZappySys Data Gateway (part of ODBC PowerPack) which will enable you to accomplish that. ZappySys ODBC PowerPack includes powerful Azure Blob CSV, Azure Blob JSON and Azure Blob XML drivers that let you connect to an Azure Storage Explorer and read the contents from the files in the Container. Bringing in ZappySys Data Gateway allows doing that right from a SQL Server. Let’s begin and see how to Import Azure Blob files into SQL Server (CSV/JSON/XML Driver).

These drivers are related to this article:

Azure Blob Driver (for CSV Files)

Azure Blob Driver (for JSON Files)

Azure Blob Driver (for XML Files)

Prerequisites

1. Firstly, Download and install Microsoft Azure Storage Emulator and Microsoft Azure Storage Explorer.
2. SQL Server instance installed (can be a SQL Server Express LocalDB instance).
3. SQL Server Management Studio (SSMS) installed.
4. ZappySys ODBC PowerPack installed (must be installed on a Windows machine; can be a different machine than SQL Server is installed on).

NOTE: If you want to use Live account (Azure Storage) then you can skip Step #3

Getting started

In order to start, we will show several examples. ZappySys includes Data Gateway that will help you in reading data of Azure Blob files. Here we are showing you is, how to Import Azure Blob files into SQL Server (CSV/JSON/XML Driver).

You can connect to Azure Storage Service from SSIS, you will need Storage Account Name and Access Key. Ask your SysAdmin or responsible person to provide that information to you. Click here to read more about how to get your Storage Account Name and Access Key. Here are sample Credentials.

Account Name: mystorageaccount
Access Key: Eby8vdM02xNOcqFlqUwJPLlmEtlCDXJ1OUzFT50uSRZ6IFsuFq2UVErCz4I6tq/K1SZFPTOtr/KBHBeksoGMGw==

If you don’t have Azure Storage account then you can try offline mode on your local machine. You can just download Azure Storage Emulator and start testing.

Setup Azure Storage Explorer

Setup Azure Storage client tools we will start from a simple example of how to load Invoice Data from a CSV that is located in Azure Blob, then we will load JSON contacts data from them and finally, we will proceed by loading contacts from many XML files those are compressed in ZIP.

1. Once you have downloaded and installed storage emulator You can launch Microsoft Azure Storage Emulator from its Physical location or from the desktop or start menu shortcut.

   

   Azure Storage Emulator Physical Location

2. If you can see the below-attached Command Prompt screen after Emulator started. Then you can proceed to start Microsoft Azure Storage Explorer as the Azure Storage Emulator is started successfully.

   

   Command Prompt Screen after Microsoft Azure Storage Emulator Started

3. Now, you have to download and install Microsoft Azure Storage Explorer and then you can launch Microsoft Azure Storage Explorer from its Physical location or from the desktop or start menu shortcut.

   

   Microsoft Azure Storage Explorer Location.

4. For Creating a Blob Container, First of all, you need to go to Microsoft Storage Explorer Window. Then you can go through like this way (Storage Accounts –> (Development) –> Blob Containers).
   

   Microsoft Azure Storage Explorer: Create a Blob Container

Open and Configure ZappySys Data Gateway

Import CSV file located at Azure Blob into SQL Server

Overview

Let’s say you have CSV file that is located in Azure Blob Container, and you want them all data into your SQL Server. We will follow the same steps for that we will do for import JSON and XML file into SQL Server.

Azure Blob Container – CSV File Located

Create an Azure Blob CSV data source in ZappySys Data Gateway

1. The first thing you will have to do is to create a data source in ZappySys Data Gateway. In the Data Source tab, Just click on Add button, give the data source a name, e.g. “MyInvoiceCSV“, and then select Native – ZappySys Azure Blob CSV Driver.
   

   Create Data Source – Azure Blob CSV Driver

2. Then click on Edit and add the Data Gateway user you created in the Users tab. We will use this user later when adding a Linked Server to the Data Gateway to authenticate:
3. Now, click on the Edit button to configure “MyInvoiceCSV” Azure Blob CSV Data Source.
   

   Configure Data Gateway

4. When a window open, click on Click here to Configure the Connection and select Use the Microsoft Azure Storage Emulator. If you have Online Storage Account and Account Key then Select Enter Storage Account Credentials and enter it.
   
   

   Create Azure Storage Connection

5. Now, Select Azure Blob Container and file from it and In the Data Format / Compression (Zip / GZip) tab set suitable file Compression Format (Zip or GZip).
   

   ZappySys Azure Blob CSV – Configure Driver

6. Go to the Preview tab and click on the Preview Data button to make sure everything is configured correctly and preview the results.
   

   ZappySys Azure Blob CSV – Preview Data

7. Now, Click on OK button also Save and Restart Service.
   

   ZappySys Data Gateway Service – Save and Restart

8. That’s all, now we are ready to proceed and add a Linked Server to this data source.

Set up a SQL Server Linked Server

Execute the SQL query

Once again, if you created the Linked Server with name “GATEWAY”, execute this SQL query in SSMS:

SELECT * INTO MyInvoices FROM OPENQUERY([GATEWAY], 'SELECT * FROM $')
SELECT * FROM MyInvoices

The results

You should see a similar view, once you execute the query:

The results of loading many CSVs from Azure Blob into SQL Server

Import JSON file located at Azure Blob into SQL Server

Overview

Let’s say you have JSON file that is located in Azure Blob Container, and you want them all data into your SQL Server. We will follow the same steps of the import CSV file into SQL Server and we will do for XML file into SQL Server.

Create an Azure Blob JSON data source in ZappySys Data Gateway

Follow the steps as in Create an Azure Blob CSV data source in ZappySys Data Gateway section when adding a new data source, except for these two steps:

1. The first thing you will have to do is to create a data source in ZappySys Data Gateway. Just click Add button, give the data source a name, e.g. “MyContactsJSON“, and then select Native – ZappySys Azure Blob JSON Driver.
   

   ZappySys Data Gateway – Create New Data Source
2. After that, select a JSON file from container you want to load, and then click Select Filter button to choose data you want to be displayed in SQL Server.
   

   Configuring the JSON data source to load a JSON from Azure Blob container into SQL Server

Now you are ready to create a Linked Server and connect to the data source you just created. If you used “MyContactsJSON” as the data source name, make sure to use the same name when creating a Linked Server.

Set up a SQL Server Linked Server

Again, follow the very same steps from the section above where we added the Linked Server to a CSV data source, except that change Catalog property of the Linked Server to match the name of the Azure Blob JSON data source we created. Once you do that, you are ready to load JSON into your SQL Server.

Execute the SQL query

Once you created the Linked Server to ZappySys Data Gateway, you are ready to execute the SQL query and load data into SQL Server. Supposedly, you created the Linked Server with the name “GATEWAY”, then open SSMS and execute the following query:

SELECT * INTO MyContacts FROM openquery([GATEWAY], 'SELECT * FROM $')
SELECT * FROM MyContacts

The results

You should see similar results after you execute the query:

The results of getting JSON data straight from Azure Blob JSON

Now we are ready to proceed to the next section and import many XML files at once.

Import many XML files located at Azure Blob into SQL Server

Overview

Supposedly, you have many XMLs where each one is zipped and you want to load them all into a SQL Server table.

Zipped XML files located in Azure Blob container to be loaded into SQL Server

The first thing you will have to do is to create a data source, based on ZappySys Azure Blob XML Driver. Let’s proceed and just do that.

Create an Azure Blob XML data source in ZappySys Data Gateway

Follow the same steps as in Create an Azure Blob JSON data source in ZappySys Data Gateway section when adding a new data source, except for these two steps:

1. When adding a data source select Native – ZappySys Azure Blob XML Driver as Connector Type.
   

   Choosing “ZappySys Azure Blob XML Driver” to load XMLs to SQL Server
2. Then click on Edit and select Azure Blob Container and file from it and In the Data Format / Compression (Zip / GZip) tab set suitable file Compression Format (Zip or GZip).
   

   Configuring data source based on ZappySys Azure Blob XML Driver to load XMLs into SQL Server

3. Now we are ready to set up a Linked Server to this newly created data source.

Set up a SQL Server Linked Server

Follow the very same steps from the section above where we added the Linked Server to a CSV data source, except that change Catalog property of the Linked Server to match the name of the Azure Blob XML data source you created. Once you do that, you are ready to load XMLs into your SQL Server.

Execute the SQL query

Again, if you created the Linked Server with name “GATEWAY”, execute the following SQL query in SSMS:

SELECT * INTO MyContacts FROM openquery([GATEWAY], 'SELECT * FROM $')
SELECT * FROM MyContacts

The results

You should see a similar view once you execute the query:

The data of many XMLs loaded from Azure Blob Container into SQL Server

Now we are ready to move to the next section and recursively scan CSVs and load them into SQL Server.

Conclusion

We had a goal to import Azure Blob files into SQL Server from SQL Server itself. We learned how to Import Azure Blob files into SQL Server (CSV/JSON/XML Driver), which some of them were zipped and were located at different folders and subfolders. It was possible to accomplish that using ODBC PowerPack, ZappySys Data Gateway, and ODBC PowerPack drivers – Azure Blob JSON Driver, Azure Blob XML Driver, and Azure Blob CSV Driver. At first, we created data sources in the Data Gateway, then we created Linked Servers in SQL Server and connected to ZappySys Data Gateway, which finally made it possible to load data from Azure Blob straight from SQL queries in SSMS.

References

Finally, you can use the following links for more information:

• Azure Blob CSV Driver
• Azure Blob JSON Driver
• Azure Blob XML Driver

The post Import Azure Blob files into SQL Server (CSV/JSON/XML Driver) appeared first on ZappySys Blog.

In our previous blog, we saw how to read/write Azure Blob Storage Files in SSIS (CSV, JSON, XML Format files). Now in this blog, we will see how to Read/Write Azure Table Storage data in SSIS. To illustrate, we will use ZappySys SSIS PowerPack, which includes several tasks to import/export data from multiples sources to multiple destinations like flat files, Azure, AWS, databases, Office files and more. They are Coding free, drag and drop high-performance suite of Custom SSIS Components and SSIS Tasks. If you like to perform other operations on Azure Blob Storage Files (e.g. Download, Upload, Create, Delete) then check these articles.

Component Mentioned in this article

	Azure Table Storage Source
	Azure Table Storage Destination

Prerequisite

1. First, you will need to have SSIS installed
2. Secondly, make sure to have SSDT
3. Download and install Microsoft Azure Storage Emulator
4. Download and install Microsoft Azure Storage Explorer
5. Finally, do not forget to install ZappySys SSIS PowerPack

NOTE: If you want to use Live account (Azure Blob Storage) then you can skip Step #3

What is Azure Table Storage?

Azure Table storage stores large amounts of structured data. The service is a NoSQL datastore which accepts authenticated calls from inside and outside the Azure cloud. Azure tables are ideal for storing structured, non-relational data. Common uses of Table storage include:

Microsoft Azure Table Storage Introduction

• Storing TBs of structured data capable of serving web-scale applications
• Storing datasets that don’t require complex joins, foreign keys, or stored procedures and can be denormalized for fast access
• Quickly querying data using a clustered index
• Accessing data using the OData protocol and LINQ queries with WCF Data Service .NET Libraries

You can use Table storage to store and query huge sets of structured, non-relational data, and your tables will scale as demand increases.

Getting Started

In order to start, we will show several examples. Zappysys includes an SSIS Azure Table Storage Source. Here we are showing you is, how to Read/Write Azure Table Storage data in SSIS.

You can connect to your Azure Storage Account by entering your storage account credentials. Here I am showing an example of the use of the local Azure Storage Emulator.

Setup Azure Storage client tools and Create Table

1. Once you have downloaded and installed a storage emulator You can launch Microsoft Azure Storage Emulator from its Physical location or from the desktop or start menu shortcut.
   

   Azure Storage Emulator Physical Location

2. If you can see the below-attached Command Prompt screen after Emulator started. Then you can proceed to start Microsoft Azure Storage Explorer as the Azure Storage Emulator is started successfully.
   

   Command Prompt Screen after Microsoft Azure Storage Emulator Started

3. Now, you have to download and install Microsoft Azure Storage Explorer and then you can launch Microsoft Azure Storage Explorer from its Physical location or from the desktop or start menu shortcut.
   

   Microsoft Azure Storage Explorer Location.

4. If you don’t have Azure Storage account then it’s easy to get FREE Trial Azure Storage account or use your MSDN to get credit each month which will be more than enough for real testing. If you prefer to avoid all this hassle then Microsoft provides another great way to test Azure Storage functionality is totally offline mode on your local machine. You can just download Azure Storage Emulator and start testing.
5.  Click here to learn more about how to configure Azure Storage Emulator for Testing and Development purposes. In order to connect to Azure Storage Service from SSIS, you will need Storage AccountName and an AccessKey. Ask your SysAdmin or responsible person to provide that information to you. Here are sample Account Name and Access Key(this is just example key which may differ in your case)
   

   Account Name: mystorageaccount 
   Access Key: Eby8vdM02xNOcqFlqUwJPLlmEtlCDXJ1OUzFT50uSRZ6IFsuFq2UVErCz4I6tq/K1SZFPTOtr/KBHBeksoGMGw==

6. For Creating a Tables, First of all, you need to go to Microsoft Storage Explorer Window. Then you can go through like this way (Storage Accounts –> Emulator – Default Ports(Key) –> Tables. Right-click on Tables and Select Create Table and put your table name you want.
   

   Create Tables in Azure Table Storage Emulator

Write data using ZS Azure Table Storage Destination

In this tutorial, you will learn how to write data into Azure Table Storage. You can use ZS DummyData Source for practice it(In this case it’s from SQL Server Source). So, Let’s start with an example. In this SSIS we will write data into Azure Table Storage using ZS Azure Table Storage Destination.

1. Once you have created Azure Storage Table and you got valid Account Name and AccessKey (for the local emulator you don’t need this) you may proceed to create a new SSIS package. In BIDS/SSDT create new SSIS package
2. From the SSIS toolbox drag and drop Data Flow Task on the control flow designer surface.
   

   Drag and Drop SSIS Data Flow Task from SSIS Toolbox

3. Double click on the DataFlow task to see DataFlow designer surface.
4. Here, In Visual Studio, drag and drop the OLE DB Source and Azure Table Storage Destination in the design surface and join the components with the blue arrow.
   

   SSIS Azure Table Storage Destination – Drag and Drop

5. Now, we need two connection, OLE DB Connection, and Azure Storage Connection. Click here to create Azure Storage Connection.

How to create OLE DB Connection

1. Let’s, Right-click on Connection Managers Panel to Create OLE DB Connection, so you can use Source and Context Menu will appear, Select New OLE DB Connection from the Context Menu.
   

   OLE DB Connection – Create New Connection

2. Now, click on New Button to create Connection.
   

   OLE DB Connection – Create New Connection

3. Let’s Configure Connection Manager, just Follow steps one by one as we have created.
   

   OLE DB Connection – Configure Connection

4. Click on the OK button to save configure Setting UI.

Write data into Azure Storage Table in SSIS

1. Double click on OLE DB Source for configure it.
2. Let’s Configure in Connection Manager, just follow the below image steps.
   

   OLE DB Source – Configure

3. Click OK to Save OLE DB Source Editor UI Settings.
4. Now double click Azure Table Storage Destination to configure it.
5. On [Connection Manager] tab select Azure Storage Connection manager.
   

   ZS Azure Table Storage Destination – Connection Manager

6. Click on [Component Properties] tab and select AccessMode=Table and Pick destination Table from the Tables dropdown list.
   
   If you choose Reload option then target table is first truncated before inserting records so be careful with Reload option.
   

   ZS Azure Table Storage Destination – Component Properties

7. Click on [Mappings] tab and verify mapping. Drop CustomerID and RecordID mappings and reconnect as shown below. This is just to make sure PrimaryKey columns are mapped.
   

   ZS Azure Table Storage Destination – Column Mappings

8. Click OK to save settings.
9. Execute the package and verify the target data.
   

   ZS Azure Table Storage Destination – Execute the Package

   

   ZS Azure Table Storage – Verify Customers Table Data

Read data using ZS Azure Table Storage Source

In this tutorial, you will learn how to read data from Azure Table Storage in SSIS using the ZS Azure Table Storage Source adapter. In this SSIS we will read data with SQL Query and Table Mode. So Let’s begin it.

1. You may proceed to create a new SSIS package. In BIDS/SSDT create a new SSIS package.
2. From the SSIS toolbox drag and drop Data Flow Task on the control flow designer surface.
   

   SSIS Data Flow Task – Drag and Drop

3. Double click on the DataFlow task to see DataFlow designer surface.
4. From the SSIS toolbox drag and drop Azure Table Storage Source on the data flow designer surface.
   

   ZS Azure Table Storage Source – Drag and Drop

5. Now, we need an Azure Storage connection. Click here to Create Connection.

Extract data with Query Mode

1. Here, we are getting data using Query Mode with Dynamic expression value.
2. Lets, create a Variable with correct DataType and Value, use the following image.
   

   SSIS Variables – Create Variable and Store Value

3. Double click Azure Table Storage Source to configure it.
4. Select the connection we have created before, set AccessMode to Query. You can use the following SQL Query and select Variable. Click here for more Azure Table Storage Query Examples.
   

   SELECT * FROM Customers
   WHERE Country = '{{User::varCountry}}'

   

   ZS Azure Table Storage Source – Configure Query Mode

5. Click on the Preview button to see Data Preview and OK button to save configure setting UI.
6. From the SSIS toolbox drag and drop Trash Destination on the data flow designer surface.
   

   ZS Trash Destination – Drag and Drop

7. Now single click on the Azure Table Storage Source, once you see the blue arrow from the source … connect it to Trash Destination.
8. Double click on ZS Trash Destination to Configure it.
   

   ZS Trash Destination – Configure

9. Click on the OK button to save Trash Destination configure setting UI.
10. Execute the package and verify source data in the data viewer.
    

    SSIS ZS Azure Table Storage Source – Execute the Package

Extract data with Table Mode

1. So, Double click Azure Table Storage Source to configure it.
2. In the Azure Table Storage Source, Select the connection we have created, set AccessMode to Table and Select Table from the Tables dropdown.
   

   SSIS ZS Azure Table Source – Configure Table Mode

3. Click on the Preview button to see Data Preview and OK button to save configure setting UI.
4. From the SSIS toolbox drag and drop Trash Destination on the data flow designer surface.
   

   ZS Trash Destination – Drag and Drop

5. Now single click on the Azure Table Storage Source, once you see the blue arrow from the source … connect it to Trash Destination.
6. Double click on ZS Trash Destination to Configure it.
   

   ZS Trash Destination – Configure

7. Click on the OK button to save Trash Destination configure setting UI.
8. In the last, Execute the package and verify source data in the data viewer.
   

   ZS Azure Table Storage Source – Execute the Package

Conclusion

After all, In this blog, we learned how to write data in Azure Table Storage in SSIS. We used the Azure Table Storage Destination for write and Azure Table Storage Source to read data from Microsoft Azure Table Storage using Query and Table mode. You can download SSIS PowerPack here to try many other scenarios not discussed in this blog along with 70+ other components.

References

There are a few details you should use the following links for more information:

• Azure Table Storage Destination
• Azure Table Storage Source
• ZS Trash Destination
• Microsoft Azure Table Storage
• Help File:
  • Azure Table Storage Destination
  • Azure Table Storage Source
  • ZS Trash Destination

The post How to Read/Write Azure Table Storage data in SSIS appeared first on ZappySys Blog.

In this article, we will see how to read How to write data into CSV file in SSIS from SQL Server. We will also explain how to compress (gzip) and split  CSV files into multiple files. To illustrate, we will use ZappySys SSIS PowerPack, which includes several tasks to import/export data from multiples sources to multiple destinations like flat files, Azure, AWS, databases, Office files and more. They are Coding free, drag and drop high-performance suite of Custom SSIS Components and SSIS Tasks. We will use the ZS CSV File Destination component for this article.

What is CSV File Destination?

CSV File Destination can be used to write data in CSV / TSV file format. You can also split large files by row count or size at runtime. It also supports writing files directly in a compressed format such as GZip (*.gz).

Prerequisites

Before we perform the steps listed in this article, you will need to make sure the following prerequisites are met:

1. SSIS designer installed. Sometimes it is referred to as BIDS or SSDT (download it from Microsoft site).
2. Basic knowledge of SSIS package development using Microsoft SQL Server Integration Services.
3. Make sure ZappySys SSIS PowerPack is installed (download it).

Component Mentioned in this article

CSV File Destination

How to Extract Data from SQL Server Table to CSV File Destination.

In this tutorial, we are going to load data from SQL Server Table using OLE DB Source and ZS CSV File Destination. You can use ZS DummyData Source too for practice it.

1. Firstly, you need to Download and Install SSIS ZappySys PowerPack.
2. Once you finished the first step, Open Visual Studio and Create New SSIS Package Project.
3. Now, Drag and Drop SSIS Data Flow Task from SSIS Toolbox.
   

   Drag and Drop SSIS Data Flow Task from SSIS Toolbox

4. Double click on the Data Flow task to see the Data Flow designer surface.
5. Here, In Visual Studio, drag and drop the OLE DB Source and CSV File Destination in the design surface and join the tasks with the blue arrow.
   

   SSIS OLE DB Source and CSV File Destination – Drag and Drop

6. Here, you can use our ZS Data Source too.
7. But, we are going through OLE DB Source so need OLE DB Connection and File Connection for CSV File Destination.

How to create OLE DB Connection.

1. Lets, create the first OLE DB Connection.
2. Let’s, Right-click on Connection Managers Panel to Create OLE DB Connection, so you can use Source and Context Menu will appear, Select New OLE DB Connection from the Context Menu.
   

   Create OLE DB Connection

3. Now, click on New Button to create Connection.
   

   Create New OLE DB Connection

4. Let’s Configure Connection Manager, just Follow steps one by one as we have created.
   

   Configure OLE DB Connection Manager

5. Click on Test Connection to see correct configure it.
6. Click on OK button to save connection configure setting UI.

How to Create a File Connection.

1. Let’s, Right-click on Connection Managers Panel to Create File Connection, so you can use Destination Path, and Context Menu will appear, Select New Connection from the Context Menu. In the File Connection Manager, select the Usage type and specify the path of the CSV file.
   CSV File: You can download good one CSV file from here for practice.
   

   https://zappysys.com/downloads/files/test/invoices.csv
   https://zappysys.com/downloads/files/test/invoices.csv.gz
   https://zappysys.com/downloads/files/test/invoices.csv.zip

   

   Create a New File Connection

2. Click on OK button to save connection configure setting UI.

Let’s see how to write data from SQL Server Table to CSV File.

1. Double click on OLE DB Source for configure it.
2. Let’s Configure in Connection Manager, just follow below image steps.
   

   Configure OLE DB Source Editor

3. Click OK to Save OLE DB Source Editor UI Settings.
4. Let’s configure CSV File Destination, Double click on it, In the File Connection Manager, select the file connection created before.
   

   SSIS ZS CSV File Destination – Configure Connection Manager Tab

5. In the Component Properties, set the Append property to False, enter the File Path and set the Overwrite file if exists to True.
   

   SSIS ZS CSV File Destination – Configure Component Properties Tab

6. In the Input Columns Tab, check the columns that you want as load data.
   

   SSIS ZS CSV File Destination – Configure Input column Tab

7. Click on OK button to save configure setting UI.
8. Finally, run the package and the data will be exported from source to destination immediately.
   

   SSIS ZS CSV FIle Destination – Execute the Package

How to do file split using CSV File Destination.

1. Double click on CSV File Destination to configure it.
2. In the Component Properties set Enable File Split to True and File Split Mode BySize.
   

   SSIS ZS CSV File Destination – File Split

3. Click OK button to save configure setting UI.
4. That’s all, Now you can run or execute the package and you will see numbers of CSV file in Destination Folder.

How to do file compression using CSV File Destination.

1. Double click on CSV File Destination to configure it.
2. In the Component Properties set Compression Type to GZip or Zip and File Split Mode BySize.
   

   SSIS ZS CSV File Destination – Compression Type

3. Click OK button to save configure setting UI. and You can Run or Execute.

How to do the maximum number of rows per file using CSV File Destination.

1. Double click on CSV File Destination to configure it.
2. In the Component Properties, you can set Max Rows Per File and Max Size Per File as per your need.
   

   SSIS ZS CSV File Destination – Max Rows Per File

3. Click OK button to save configure setting UI. and You can Run or Execute.

Conclusion

After all, we saw you how to write data into CSV File from SQL Server Table using OLE DB Connection, File Connection, and load into CSV File. We also learned, how to do various things like split CSV File, Compression File and the maximum number of rows per file of data. To explore many other scenarios not discussed in this article download SSIS PowerPack from here (includes 70+ Components).

References

Finally, you can use the following URL for more information.

• Help File: CSV File Destination

The post How to write data into CSV file in SSIS (GZip / Split) appeared first on ZappySys Blog.

ZappySys provides high performance drag and drop connectors for PostgreSQL Integration. In this post you will see how to Load PostgreSQL Table Data to CSV File using SSIS PostgreSQL Source. PostgreSQL Source supports SQL language to query PostgreSQL data. PostgreSQL is a powerful, open source relational database with strong reputation for reliability, feature robustness, and performance.

In nutshell, this post will focus on how to Load PostgreSQL Table Data and write into CSV File using Trash Destination in few clicks. You can also use the CSV Destination instead of Trash destination if you wants more option like compressed file gzip (*.gz) or zip, wants to splitting the file by row count of file size and more.

Prerequisite

1. First, you will need to have SSIS installed
2. Secondly, make sure to have SSDT
3. Thirdly, You have obtained PostgreSQL Credential.
4. Finally, do not forget to install ZappySys SSIS PowerPack

Components Mentioned in this article

	SSIS PostgreSQL Source
	CSV Destination
	Trash Destination

What is PostgreSQL?

PostgreSQL is a powerful, open source object-relational database system that uses and extends the SQL language combined with many features that safely store and scale the most complicated data workloads. The origins of PostgreSQL date back to 1986 as part of the POSTGRES project at the University of California at Berkeley and has more than 30 years of active development on the core platform.

PostgreSQL has earned a strong reputation for its proven architecture, reliability, data integrity, robust feature set, extensibility, and the dedication of the open source community behind the software to consistently deliver performant and innovative solutions. PostgreSQL runs on all major operating systems, has been ACID-compliant since 2001, and has powerful add-ons such as the popular PostGIS geospatial database extender. It is no surprise that PostgreSQL has become the open source relational database of choice for many people and organisations.

Getting started with using PostgreSQL has never been easier – pick a project you want to build, and let PostgreSQL safely and robustly store your data.

Download and Install PostgreSQL

Please go to below download link and download the Windows version Binary package and install it and follow the steps from the installer.

https://www.postgresql.org/download/

PostgreSQL Download

Create PostgreSQL Database and Table

1. To do this, we will first open the pgAdmin Panel.
   

   Open PostgreSQL pgAdmin Panel

2. It will open the pgAdmin Panel in the browser. Select Server and Right click on Databases to create database.
   

   PostgreSQL Select Create Database

3. Enter the Database name and click on Save to create it.
   

   Create PostgreSQL Database

4. Now select the created database under it expand Schemas and right click on Tables and click on create Table.
   

   PostgreSQL Select Create Table

5. Now enter the table name and go to columns tab.
   

   PostgreSQL Enter Table Name

6. Add the columns by clicking add button and give the desire column name and select the datatype for the same.
   

   PostgreSQL Add Table Columns

7. Now, let’s insert some data using Query Editor.
   

   PostgreSQL Insert Data in Table

8. That’s it, now let’s move forward to read the data from this table using SSIS PostgreSQL Source in the next the section.
   

   PostgreSQL Select Query Data

Getting Started

In order to start, we will show several examples. ZappySys includes an SSIS PostgreSQL Source that will help you extract large amount of data from a PostgreSQL Database. PostgreSQL Source supports Multiple modes (i.e. Table or Query) to read data from PostgreSQL Database and many more operations. Here we are showing you is, How to read data from PostgreSQL collection.

You can connect to your PostgreSQL database by entering your PostgreSQL credentials.

Load PostgreSQL Table Data to CSV File

Let´s start with an example. In this SSIS PostgreSQL Source Task example, we will load the PostgreSQL table data to CSV file.

1. First of All, Drag and drop Data Flow Task from SSIS Toolbox and double click it to edit.
   

   Drag and Drop : Data Flow Task from SSIS Toolbox

2. Drag and Drop PostgreSQL Source Task from the SSIS Toolbox and double click on it to edit it.
   

   PostgreSQL Source

3. Let’s create a connection for PostgreSQL Database.
   

   Create PostgreSQL Database Connection

4. Let’s select the Table mode and select the table and click on preview.
   

   PostgreSQL Table Preview

5. You can also use the query mode to filter the data and do more operation using query. You can find our several Examples also by clicking on <<View Examples>>.
   

   PostgreSQL Query Preview

6. Now let’s add Trash destination Task and edit it and enter the CSV File Path.
   

   Trash Destination : Setup CSV File Path

7. That’s it now execute the package and it will load data from PostgreSQL Table to CSV File.
   

   PostgreSQL : CSV Data

Conclusion

Above all, in this blog, we learned how to Read data from PostgreSQL in SSIS. We used SSIS PostgreSQL Source to read the data from PostgreSQL Table and load data into CSV File. You can download SSIS PowerPack here to try many other scenarios not discussed in this blog along with 70+ other components.

References

Finally, you can use the following links for more information:

• About PostgreSQL
• PostgreSQL Documentation
• PostgreSQL – SELECT Query
• Help File : PostgreSQL Source

The post Load PostgreSQL Table Data to CSV File in SSIS appeared first on ZappySys Blog.