Why is PostgreSQL voted as the most loved database by developers? The diagram shows the many use cases by PostgreSQL - one database that includes almost all the use cases developers need. OLTP (Online Transaction Processing) We can use PostgreSQL for CRUD (Create-Read-Update-Delete) operations. OLAP (Online Analytical Processing) We can use PostgreSQL for analytical processing. PostgreSQL is based on HTAP (Hybrid transactional/analytical processing) architecture, so it can handle both OLTP and OLAP well. FDW (Foreign Data Wrapper) A FDW is an extension available in PostgreSQL that allows us to access a table or schema in one database from another. Streaming PipelineDB is a PostgreSQL extension for high-performance time-series aggregation, designed to power real-time reporting and analytics applications. Geospatial PostGIS is a spatial database extender for PostgreSQL object-relational database. It adds support for geographic objects, allowing location queries to be run in SQL. Time Series Timescale extends PostgreSQL for time series and analytics. For example, developers can combine relentless streams of financial and tick data with other business data to build new apps and uncover unique insights. Distributed Tables CitusData scales Postgres by distributing data & queries. -- Subscribe to our weekly newsletter to get a Free System Design PDF (158 pages): bit.ly/42Ex9oZ

Visualizing a SQL query SQL statements are executed by the database system in several steps, including: - Parsing the SQL statement and checking its validity - Transforming the SQL into an internal representation, such as relational algebra - Optimizing the internal representation and creating an execution plan that utilizes index information - Executing the plan and returning the results – Subscribe to our weekly newsletter to get a Free System Design PDF (158 pages): bit.ly/42Ex9oZ

DevOps vs. SRE vs. Platform Engineering. What is the difference? DevOps as a concept was introduced in 2009 by Patrick Debois and Andrew Shafer at the Agile conference. They sought to bridge the gap between software development and operations by promoting a collaborative culture and shared responsibility for the entire software development lifecycle. SRE, or Site Reliability Engineering, was pioneered by Google in the early 2000s to address operational challenges in managing large-scale, complex systems. Google developed SRE practices and tools, such as the Borg cluster management system and the Monarch monitoring system, to improve the reliability and efficiency of their services. Platform Engineering is a more recent concept, building on the foundation of SRE engineering. The precise origins of Platform Engineering are less clear, but it is generally understood to be an extension of the DevOps and SRE practices, with a focus on delivering a comprehensive platform for product development that supports the entire business perspective. It's worth noting that while these concepts emerged at different times. They are all related to the broader trend of improving collaboration, automation, and efficiency in software development and operations. -- Subscribe to our weekly newsletter to get a Free System Design PDF (158 pages): bit.ly/42Ex9oZ