Leveraging the amazing power of the awesome PostgreSQL Community

This article is part of a series on Cloud journey specialized on Financial Institutions.

Feedback is very welcome.?

Articles in this Series:

1. Cloud vs. on Premise for Data Warehouse
2. Road to the Cloud
3. Free yourself from Oracle
4. Leveraging the amazing power of the awesome PostgreSQL Community

Part 4 reveals the hidden benefits of migrating to PostgreSQL.?

Postgres is not just another RDBMS or relational database. Instead, it's an entire community of active, enthusiastic users, developers, and contributors passionate about developing and improving the PostgreSQL database system you could benefit from immediately.?

By using PostgreSQL, you can gain access to a wealth of resources, including free extensions, open-source solutions seamlessly integrated with the ecosystem, documentation, tutorials, mailing lists, forums, and user groups, that can help you to learn more about PostgreSQL and become more proficient in using it.

This article is a concise introduction to the Garden of Eden of PostgreSQL co.

Table of Contents

1. Accessing and database development
2. Data modelling
3. Application development
4. Data-Intensive Application development
5. Job scheduler and dependency management tools?
6. Clustering, High Availability, Scalability
7. Monitoring

1. Accessing and database development

Accessing a database means retrieving, modifying, or storing data in a structured way. In order to access the data stored in a database, users or applications need to interact with it using specific software, including connecting to the database, querying and modifying data, managing database objects (creating, modifying, or deleting tables, views, indexes, triggers, and stored procedures) as well as optimizing database performance (configuring the database settings, optimizing the query execution plan, etc.).

The most popular open-source tools:

1. pgAdmin: pgAdmin is a popular open-source management that provides a graphical interface for managing databases, tables, users, and more.
2. SQL Shell (psql): psql is a command-line interface that comes with PostgreSQL and is used to interact with the database. It allows you to execute SQL queries, manage databases and users, and more.
3. DBeaver: DBeaver is a universal database client. It provides a graphical user interface for managing databases, executing queries, and more.
4. The?"Database tools and SQL"?plugin for IntelliJ-based IDEs allows you to query, create, and manage databases and provides full SQL language support.
5. PostgREST?is an open-source web application framework that automatically generates a RESTful API (Application Programming Interface) for a PostgreSQL database. It provides a simple and efficient way to expose database resources over HTTP/HTTPS, enabling easy access to data stored in a PostgreSQL database by web and mobile applications.
6. PL/Python?allows you to write stored procedures, triggers, and functions in Python.

2. Data modelling

Data modelling is the process of creating a conceptual representation of data objects and the relationships between them. It involves identifying and defining the data requirements for a specific business or organizational domain and creating a graphical representation.

3. Application development

In the context of PostgreSQL, application development tasks typically involve working with the database to create, read, update, and delete data, including database design and data modelling.?

More specifically, using an ORM (Object-Relational Mapping) tool in application development regarding database integration became a defacto best practice in the last 20 years.?

The following shortlist is the most popular ORM tools working well with PostgreSQL:

4. Data-Intensive Application development

Data-intensive applications are software applications designed to handle and process large volumes of data.?

Some examples of data-intensive applications include:

1. Big data processing: Applications that process and analyze large volumes of data, such as log data, social media data, or machine data.
2. Data warehousing: Applications that store and manage large amounts of data in a centralized repository, such as a data warehouse.
3. Business intelligence and analytics: Applications that provide insights and analysis on business performance using data from various sources.
4. Machine learning and artificial intelligence: Applications that use data to train and improve machine learning models, such as image recognition, natural language processing, or predictive analytics.

4.1. ETL and Business Intelligence frameworks working well with PostgreSQL

The most relevant 2 types of frameworks in data-intensive applications are Data integration and Business Intelligence tools. According to my knowledge, all of them natively support PostgreSQL; however, if you are looking for open-source technologies, here are a few examples:?

1. Talend:?A powerful open-source ETL tool that allows you to extract, transform, and load data from various sources.
2. Apache Nifi:?A data integration tool that automates moving, transforming, and integrating data.
3. Kettle?(Pentaho Data Integration): An open-source ETL tool that extracts, transforms, and loads data from various sources.
4. Apache Airflow: A platform to programmatically author, schedule, and monitor workflows.
5. Metabase: An open-source BI tool that allows you to create interactive dashboards, visualizations, and reports with your data.
6. Superset: An open-source data exploration and visualization platform that allows you to create interactive dashboards and reports.
7. Redash?is an open-source data analytics platform that allows you to connect, query, visualize and share data.
8. Looker: An open-source data exploration platform allowing you to create, manage and share interactive data analytics.
9. OpenBI: An open-source business intelligence platform allowing you to create, manage and share data analytics.

4.2. Artificial Intelligence frameworks working well with PostgreSQL

5. Job scheduler and dependency management tools?

A job scheduler is a tool that helps automate and manage the scheduling of jobs or tasks on a computer system. A job scheduler allows you to specify when and how often a job should be executed and can help manage dependencies between jobs, ensuring that all required components are present and correctly configured before a job is started. In data processing and analytics, job scheduling and dependency management are critical components of the overall data pipeline. The following list contains the most popular and free job schedulers for PostgreSQL:

6. Clustering, High Availability, Scalability

In the context of databases, clustering generally refers to using multiple servers (nodes) to provide high availability and/or scalability for a database system.?

Clustering typically involves replicating the data across multiple nodes to provide redundancy and ensure the database remains available even during hardware failures or other issues. In addition to redundancy, clustering can also provide load balancing and improved performance by distributing query processing across multiple nodes. There are different types of clustering, such as shared-disk clustering, shared-nothing clustering, and hybrid clustering, each with its own advantages and trade-offs. These are the most popular clustering tools for PostgreSQL:

7. Monitoring

Database monitoring refers to the process of tracking, analyzing, and optimizing the performance of a database system. This involves regularly checking the database's health and performance metrics, such as response times, resource usage, and query throughput, to ensure that it is functioning correctly and efficiently.

The goal of database monitoring is to identify and resolve issues before they become critical problems that can affect the performance and availability of the database. This may involve setting up alerts to notify administrators of issues such as slow queries, high resource usage, or errors, and taking proactive measures to address these issues.

In conclusion, PostgreSQL is a powerful and flexible database system that can support a wide range of applications and use cases. Furthermore, thanks to its open-source nature and active community of developers, many tools and software packages are available that integrate seamlessly with PostgreSQL and help streamline database development, management, and optimization.

Whether you are looking to improve data modeling, streamline application development, build data-intensive applications, or ensure high availability and scalability, many tools and software packages can help. From job schedulers and dependency management tools to clustering and monitoring solutions, there is no shortage of options for PostgreSQL users looking to optimize their database environment.

By leveraging the power of these tools and software packages, PostgreSQL users can ensure that their database environment is optimized for performance, reliability, and security.?

In addition, with the right tools and well-designed database architecture, organizations can take advantage of the full potential of PostgreSQL and build robust, scalable, and data-driven applications that meet the needs of their users and customers.