Is your data team looking for ways to move data from PostgreSQL to SQL Server? Great! The replication will help you achieve tighter integration with Microsoft products and services like Azure services. But, there are a few critical points to consider, such as schema differences between PostgreSQL and SQL Server, downtime management, and data integrity.
Let’s get into the details of replicating data from PostgreSQL to SQL server.
Methods to Connect PostgreSQL to SQL Server
There are two methods to copy data from PostgreSQL to SQL server. Let’s get into the first one.
Method 1: Connect PostgreSQL to SQL Server using Hevo
You can directly transfer data from various sources such as PostgreSQL Database, Business Intelligence tools, Data Warehouses, or a destination of your choice such as MS SQL Server in a completely hassle-free & automated manner.
Hevo is the only real-time ELT No-code Data Pipeline platform that cost-effectively automates data pipelines that are flexible to your needs. The following steps can be implemented to connect PostgreSQL to SQL Server using Hevo:
- Configure Source: Connect Hevo Data with PostgreSQL providing a unique name for your Pipeline, along with details such as Database Host, Database Port, Database User, Database Password, Database Name and about your Data Source. You also can choose the Data Ingestion method while configuring PostgreSQL as a source
- Configure Destination: Establish a connection to MS SQL Server by providing information about its credentials such as Destination Name, Database Host, Database Port, Database User, Database Password, Database Name and Schema Name.
Here are more reasons to try Hevo for oracle to mysql replication:
- Data Transformation: Hevo provides a simple interface to perfect, modify, and enrich the data you want to transfer.
- Schema Management: Hevo can automatically detect the schema of the incoming data and map it to the destination schema.
- Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
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Method 2: Manually Connect PostgreSQL to SQL Server using JDBC Driver
Step 1: Accessing Data
The first step in migrating data from your PostgreSQL to SQL Server is to gain access to it and begin extracting it.
There are a variety of ways to accomplish this, including the Logical Replication Log. In this instance, you must monitor the log for database changes and reflect them on the target system. When extracting data from a database, you must be able to filter tables and columns, discover a means to detect updates and replicate the right database structure, all while keeping in mind that the data will be stored in a columnar database for analytics.
Using a JDBC Importer is another option. In this instance, any input configuration will include all of the required database authentication and connection values. You may manage each table’s behavior during import and, if required, change its schema by properly setting the JDBC importer. Furthermore, by querying tables in batching mode, paging of data importation can be emulated. Below are the steps to carry out for JDBC connection in order to migrate data from your Postgres to SQL Server:
- Step 1: Connect to Database
- Step 2: Create a Table
- Step 3: INSERT Operation in PostgreSQL JDBC Driver Integration
- Step 4: SELECT Operation in PostgreSQL JDBC Driver Integration
For getting a deep understanding of JDBC Driver, you can check out the steps here.
Step 2: Preparing Data
After you’ve accessed your data into PostgreSQL, you’ll need to alter it based on two key factors:
- The Database’s Constraints on which the data will be loaded
- The type of analysis you intend to do
The Data Types and Data Structures that each system offers are limited in some way. You must also select the appropriate Data Types. Again, depending on the system to which you’ll send the data, you’ll need to make the appropriate decisions.
While the mapping choices for the most common data types may appear obvious, each Database System will almost certainly support a set of more “sophisticated” and Database-Specific types whose mapping choices must be carefully considered because they can limit the expressivity of your queries and limit what your analysts can do directly out of the Database.
Before importing scripts into the destination database, the most crucial step in database conversion is to manage all differences between PostgreSQL and SQL Server appropriately:
- Be aware of the correct type of mapping.
- Swap off the double quotes with square brackets when enclosing database object names.
- Swap out the “public” default PostgreSQL schema with “dbo” from SQL Server.
- Add the “GO” statement terminator from SQL Server after each statement.
- Convert triggers from PostgreSQL to SQL Server format.
Step 3: Loading Data
So, once you’ve figured out how to access your data in PostgreSQL and what structure the data will have in your database, you’ll need to load the data into the destination database, in this instance, from PostgreSQL to SQL Server.
- SQL Server offers a vast and diverse collection of techniques for putting data into a database as a feature-rich and mature solution. The SQL Server Import and Export Wizard is one method for importing data into your SQL Server database. You will be able to bulk load data from a variety of supported data sources using it and a visual interface.
- The bcp utility can also be used to import bulk data from PostgreSQl to SQL Server, both on Azure and on-premises. This is a command-line utility that is designed to load and unload data from SQL Server Database in bulk. The bcp command can execute following tasks:
- Data from a SQL Server table is bulk exported to a data file.
- A query’s data is exported in bulk.
- Bulk imports data into a SQL Server table from a data file.
- Produces format files.
- Finally, you can use BULK INSERT SQL commands for compatibility concerns, especially if you’re managing databases from various suppliers.
- You can also use the usual INSERT statements, which add data row-by-row directly to a table, in the same manner that you can with the rest of the databases. It’s the most basic and straightforward method of populating a table with data, but it doesn’t scale well with larger datasets.
Step 4: Updating Data
- You’ll need to update your previous data on an MS SQL Server database because you’ll be generating more data on PostgreSQL. This covers new records as well as revisions to previous records that have been modified on PostgreSQL for whatever reason.
- You’ll need to check PostgreSQL for new data on a regular basis and repeat the process mentioned previously, while updating your existing accessible data if necessary. UPDATE statements are used to update an already existing row in a SQL Server table.
- Another problem you’ll have to deal with is identifying and removing duplicate records from your database. Duplicate records may be added to your database due to PostgreSQL’s lack of a mechanism to identify new and updated records, or due to faults in your Data Pipelines.
- In general, assuring the quality of data stored into your database is a significant and challenging task, and MS SQL Server capabilities like TRANSACTIONS can help a lot, even if they don’t address the problem entirely.
Limitations of Manually Connecting PostgreSQL to SQL Server
- The Data flow is only unidirectional, from PostgreSQL to SQL Server. To keep both tools up to date, two-way sync is required.
- The manual procedure is time-consuming because records must be updated regularly. This consumes resources and time that could be better spent on more critical business operations.
- Updating the existing data and managing workflows in both platforms requires immense engineering bandwidth, and hence can be a pain point for many users.
When Should you use PostgreSQL: Key Use Cases
- Finance Industry: The finance business is a great fit for PostgreSQL. PostgreSQL is fully ACID compliant, making it appropriate for OLTP workloads (Online Transaction Processing). PostgreSQL, on the other hand, isn’t just an excellent pick for its superior OLTP capabilities; it’s also a capable analytical database that works well with mathematical tools like Matlab and R.
- GIS Data: PostgreSQL isn’t just a database for the financial industry; it also has a GIS extension called “PostGIS” that has hundreds of functions for processing geometric data in multiple forms. PostGIS is one of the de-facto standards in the Open Source GIS field, as it is extremely standard-compliant.
- Scientific Data: Terabytes of data can be generated by research and scientific projects, which must be managed in the most advantageous and effective way feasible. PostgreSQL has excellent analytical capabilities and a robust SQL engine that makes handling massive amounts of data a breeze.
Key Use Cases of SQL Server and Reasons to migrate from PostgreSQL to MS SQL Server?
The use cases are,
- SQL Server is largely used as a relational database engine for managing and storing data for ERP, CRM, and automation applications.
- It is also used by a big number of teams from various project functions who want a database management solution.
- SQL Server can also be used as a backend for data staging, web applications, and SSIS/SSRS.
Reasons to migrate from PostgreSQL to MS SQL Server?
- System compatibility
Converting your PostgreSQL database to SQL Server can facilitate the integration of your PostgreSQL database with other systems or applications that require SQL Server for operation. Integrating SQL Server databases with other systems or applications can be simplified by synchronizing the databases.
When it comes to complex queries or enormous datasets, PostgreSQL may not always perform as well as SQL Server. By dividing the burden between the two systems, synchronizing the databases can assist enhance performance.
PostgreSQL does not provide certain features that SQL Server does, including automated tuning, in-memory OLTP capability, and sophisticated security capabilities. Replicating data between the two systems using database synchronization is advantageous for disaster recovery, backup, and data warehouse creation.
SQL Server could be a better option for some projects if people find it more user-friendly or used to it than PostgreSQL. You may simplify the management and analysis of data by combining information from several sources by synchronizing the databases.
- Cross-platform compatibility
Working with data across platforms is made possible by synchronizing the databases, which might be helpful if you use a combination of Windows and Linux computers.
Let’s cover some basics as well before wrapping up.
What is PostgreSQL?
Postgres is another name for PostgreSQL. It’s an Open-source, free-to-use RDBMS platform with SQL support and a lot of extensibility. It was designed in a lab at the University of California, Berkeley, and was released on July 8, 1996. It was created to replace the old Ignes database. PostgreSQL employs structured objects to store data.
What is SQL Server?
Microsoft SQL Server is a Relational Database Management System. It has 100s of simultaneous running cores and can store petabytes of data. It includes a robust querying layer for business analysis and tightly connects with reporting solutions like Power BI. Spark support has been built-in to recent versions, allowing users to analyze massive volumes of data using Spark SQL without requiring much additional setup.
In this article, you got a glimpse of how to connect PostgreSQL to SQL Server after a brief introduction to the salient features, and use cases. The method for Oracle to MySQL migration step by-step talked in this article is using JDBC Drivers. The process can be a bit difficult for beginners. Moreover, you will have to update the data each and every time it is updated and this is where Hevo, an Oracle to MySQL migration tool saves the day!
Visit our Website to Explore Hevo
Hevo Data provides its users with a simpler platform for integrating data from 150+ data sources for Analysis. It is a No-code Data Pipeline that can help you combine data from multiple sources like PostgreSQL. You can use it to transfer data from multiple data sources into your Data Warehouses, databases, or a destination of your choice such as MS SQL Server. It provides you with a consistent and reliable solution to managing data in real-time, ensuring that you always have Analysis-ready data in your desired destination.
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