Replicating PostgreSQL to SQL Server : 2 Easy Methods

PostgreSQL is an Open-source Database Management System that supports Relational (single-valued) and NoSQL (non-relational, multi-valued, hierarchical) data types equally well. MS SQL Server is one of the most popular Database Management Systems available. It has shown to be an easy-to-use, dependable, and efficient solution. It also comes with a wide range of features. It is based on SQL, and like any other Database Management System, it does not deliver the amount of efficiency you require in a variety of scenarios.

This article talks about the different methods you can use to connect PostgreSQL to SQL Server seamlessly. It also gives a brief introduction to PostgreSQL and MS SQL Server before diving into the PostgreSQL to SQL Server data transfer methods.

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.

It is programmed in C and has a monolithic architecture in which all of the components are connected and work in symmetry and in a methodical manner. It has a large community and offers a lot of community support as well as additional support to its paying community. It’s used in a variety of industries, including healthcare, banking, and manufacturing. It also allows for the implementation of novel backup systems.

It’s written in C and has a monolithic architecture, which means all of the parts are connected and work in a logical order. It provides community assistance as well as further assistance to some of its paid customers. Due to its novel backup systems, it is frequently employed in the healthcare, finance, and industrial industries.

Key Features of PostgreSQL

PostgreSQL has some distinguishing characteristics that make it a viable alternative to other standard RDBMSs. The following are some of these characteristics:

• PostgreSQL supports a wide range of data and document types.
• The components work together in an automated manner because the architecture is monolithic.
• It’s suitable for Risk Management, Business Intelligence, and other Transactional Workflows in Bank Systems.
• It ensures Storage Reliability by providing numerous fail-safes and redundancies.
• It’s Open-source, free, and includes a lot of features.
• Due to its processing capacity being dependent on the system it runs on, it has limited Scalability.
• It is ACID (Atomicity, Consistency, Isolation, and Durability) compliant and runs on major operating systems.

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.

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.

The standard SQL (structured query language) language, ANSI SQL, is supported. T-SQL is a proprietary language that SQL Server includes as part of its installation (transact SQL). The main interface tool, which consists of a client and a server, is SQL Server Management Studio (SSMS). A client is an application that sends requests to a SQL Server installed on a system, whereas a server can handle incoming data as needed.

SQL Server offers enterprise-level security and support, making it a popular choice among businesses with stringent compliance requirements. This article explains how to import data from Google Analytics into SQL Server.

Key Features of SQL Server

• Database Engine: It is in charge of data storage, data security, and quick exchange processing.
• SQL Server Full-Text Search: This feature allows the client to do a full-text search on character data in SQL tables.
• SQL Server: This help starts, pauses, stops, and resumes the dataset processing.
• Server Agent: It is responsible for carrying out the task scheduler’s duties. It is usually triggered by a special occasion or a request.
• SQL Server VSS Writer: When the server isn’t running, this allows data backup and rebuilding of the data file.
• SQL Server Browser: It receives incoming queries and connects them to the most appropriate SQL server instance.
• SQL Server Analysis Services (SSAS): Data analytics, data mining, and machine learning are all available through this service. For cutting-edge data analytics, the SQL server is integrated with the R and Python languages.
• SQL Server Reporting Services (SSRS): Uses Hadoop integration to provide data reporting features and dynamic capacities.
• SQL Server Integration Services (SSIS): This provides extract-transform-load capabilities for a variety of data types, starting with one source and progressing to the next. It might be interpreted as transforming unhelpful data into useful data.

When Should you use SQL Server: Key Use Cases

• 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.

Methods to Connect PostgreSQL to SQL Server

Method 1: Connect PostgreSQL to SQL Server using Hevo

Hevo helps you 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 fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss.

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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:

• Fully Managed: Hevo requires no management and maintenance as it is a fully automated platform.
• Data Transformation: Hevo provides a simple interface to perfect, modify, and enrich the data you want to transfer.
• Faster Insight Generation: Hevo offers near real-time data replication so you have access to real-time insight generation and faster decision making. 
• Schema Management: Hevo can automatically detect the schema of the incoming data and map it to the destination schema.
• Scalable Infrastructure: Hevo has in-built integrations for 100+ sources (with 40+ free sources) that can help you scale your data infrastructure as required.
• Live Support: Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.

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 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. To get a deep understanding of how to carry out Logical Replication, check out the process here.

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 need to carry out for JDBC connection:

• Step 1: Connecting 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.

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 the platforms requires immense engineering bandwidth, and hence can be a pain point for many users.

Conclusion

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 talked about 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 saves the day!

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Hevo Data provides its users with a simpler platform for integrating data from 100+ 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, Database, 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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