In my digital garden, a “Seedling” is a tag assigned to notes that have just begun to take shape. These notes are the starting points for ideas, topics, or concepts that I find intriguing but have not yet fully developed. Just like young plants, these seedlings have the potential to grow and evolve into more detailed and comprehensive notes. As I delve deeper into these topics and gather more insights, I will nurture these seedlings, eventually maturing them into “Budding” notes that are more fully fleshed out.
Page page is a Seedbox of all the Seedlings you will find that I have sown in my digital garden. They are the nascent stages of ideas, waiting to be explored and cultivated. Feel free to browse through these seedlings to see the beginnings of thoughts and concepts that may soon blossom into detailed entries.
LSP standardizes the communication between code editors and language servers, enhancing the development experience by providing consistent features like auto-completion, go-to-definition, and error checking.
A branch of artificial intelligence that focuses on the interaction between computers and human language, enabling machines to understand, interpret, and generate human language.
A deep learning architecture that revolutionized natural language processing (NLP) by utilizing self-attention mechanisms to process and generate sequences of data more efficiently than traditional models.
A Database Management System (DBMS) is software that allows users to define, create, maintain, and control access to databases, ensuring efficient data management and retrieval.
A category of software technology that enables analysts, managers, and executives to gain insight into data through fast and interactive analysis of multidimensional data.
The capability to measure and understand the internal states of a system through external outputs, allowing for effective monitoring, troubleshooting, and performance optimization.
A disciplined approach to identifying and mitigating potential failures in a system by intentionally injecting faults and observing how the system responds.
A set of practices that combines machine learning, DevOps, and data engineering to automate and streamline the deployment, monitoring, and management of machine learning models in production.
Advanced AI systems trained on vast amounts of text data to understand and generate human-like language, enabling them to perform tasks such as translation, summarization, and conversation.
Prompt engineering involves designing and refining prompts to effectively guide AI models in generating accurate, relevant, and high-quality responses for various tasks and applications.
A testing framework for Python that simplifies the process of writing and running test cases, promoting the use of fixtures and plugins for enhanced functionality.
A software testing method that focuses on validating the smallest testable parts of an application, called units, to ensure they function correctly in isolation.
Specific conditions or inputs used to validate the functionality, performance, and reliability of a software application to ensure it behaves as expected.
A mythical programmer who is ten times more productive than their peers and this person has been a topic of both fascination and controversy in the tech industry for quite some time.
A personalized development environment tailors the tools, configurations, and settings to suit an individual developer's preferences and workflow, enhancing productivity and comfort.
A lightweight, partly open-source code editor developed by Microsoft that offers features like debugging, Git integration, and an extensive library of extensions
Cloud computing enables on-demand access to shared computing resources and services over the internet, providing scalability, flexibility, and cost-efficiency.
Infrastructure as a Service (IaaS) provides virtualized computing resources over the internet, including servers, storage, and networking, allowing businesses to scale and manage IT infrastructure without physical hardware.
Platform as a Service (PaaS) offers a cloud-based environment with tools and services for developers to build, deploy, and manage applications, enabling them to focus on coding without worrying about underlying infrastructure.
Software as a Service (SaaS) delivers software applications over the internet, allowing users to access and use them via a web browser without needing to install or maintain the software on local devices.
A software emulation of a physical computer that runs an operating system and applications as if it were a separate physical machine, enabling resource isolation and efficient hardware utilization.
A powerful, open-source operating system known for its flexibility, security, and robust performance, widely used in servers, desktops, and embedded systems.
A dimension table is a type of table in a data warehouse that stores descriptive attributes related to dimensions, providing context for data in fact tables.
A galaxy schema, also known as a fact constellation schema, is a data warehousing design that includes multiple fact tables sharing dimension tables, providing a flexible and scalable way to model complex data relationships.
A subset of artificial intelligence that enables systems to learn from data, identify patterns, and make predictions or decisions without explicit programming.
A Spark session is the entry point to programming with Apache Spark, allowing users to create DataFrame and Dataset objects, manage Spark configurations, and access Spark's capabilities for distributed data processing.
Data validation ensures the accuracy and quality of data by checking its compliance with defined rules and constraints before processing or storing it.
Deep learning is a subset of machine learning that uses artificial neural networks with many layers (deep architectures) to learn representations of data at multiple levels of abstraction, enabling computers to perform tasks such as image recognition, natural language processing, and speech recognition with high accuracy.
A web-based interface that provides insights into the performance and execution of Apache Spark applications, allowing users to monitor jobs, stages, and tasks in real-time.
Distributed computing is a computing paradigm in which tasks are divided among multiple computers or nodes within a network, enabling parallel processing and scalability, and facilitating the execution of complex computations and data processing tasks across distributed systems.
An open-source cluster manager that abstracts resources across a cluster of machines, enabling efficient resource allocation and management for distributed applications
A hybrid programming language that combines object-oriented and functional programming paradigms, designed for high-performance applications and interoperability with Java.
A Spark DataFrame is a distributed collection of data organized into named columns, similar to a table in a relational database or a data frame in R or Python's pandas library.
Yet Another Resource Negotiator (YARN) is a resource management and job scheduling framework used in Apache Hadoop for managing resources and running distributed applications on a cluster of machines.
A data serialization system that provides compact, fast binary data format and rich data structures for serializing, transporting, and storing data in a language-neutral way.
Change Data Capture (CDC) is a method used to automatically track and capture changes in data in a database, enabling real-time data integration and analysis.
A data catalog is a centralized repository that stores metadata and information about the data assets within an organization, facilitating data discovery, governance, and collaboration among data users.
Data contracts define the rules, formats, and expectations for exchanging data between different systems or parties, ensuring consistency, compatibility, and reliability in data communication and integration.
Data governance encompasses the processes, policies, and practices organizations implement to ensure the proper management, quality, integrity, and security of their data throughout its lifecycle, aiming to maximize its value while mitigating risks and ensuring compliance with regulations.
A data lake is a centralized repository that stores large volumes of raw and unstructured data in its native format, enabling organizations to store diverse data types at scale and perform advanced analytics, machine learning, and other data processing tasks for insights and decision-making.
A data mart is a specialized subset of a data warehouse that focuses on specific business functions or departments, containing structured data optimized for analysis and reporting to support decision-making within those areas.
Data mesh is an architectural paradigm that advocates for a decentralized approach to data management, where data ownership, access, and governance are distributed across different domain-oriented teams, enabling scalability, flexibility, and agility in managing and leveraging data assets within organizations.
DevOps is a set of practices that integrates software development and IT operations to improve collaboration, automation, and delivery speed in software development.
Distributed computing is a computing paradigm in which tasks are divided among multiple computers or nodes within a network, enabling parallel processing and scalability, and facilitating the execution of complex computations and data processing tasks across distributed systems.
An Entity-Relationship Diagram (ERD) is a visual representation of the relationships between entities (such as objects, concepts, or people) in a database, typically used in database design to illustrate the structure of the data model and the relationships between different entities.
Extract, Transform, Load (ETL) is a data integration process where data is first extracted from various sources, then transformed or manipulated to meet specific business requirements, and finally loaded into a target destination such as a data warehouse or database for analysis and reporting purposes. This process enables organizations to consolidate and standardize data from multiple sources, ensuring consistency and reliability in data analysis.
Iceberg tables are a high-performance, open table format for large analytic datasets that support complex data management and enable ACID transactions.
An Integrated Development Environment (IDE) is a software application that provides comprehensive facilities to programmers for software development, including code editing, debugging, and testing.
A data warehousing technique that consolidates miscellaneous, low-cardinality attributes into a single dimension table to streamline the database schema.
A data processing architecture designed for real-time streaming data, where all data is treated as a stream and processed through a single real-time layer.
Master Data Management (MDM) is the process of managing and maintaining a single, authoritative source of critical business data entities across an organization.
A data management framework that organizes data into three layers — bronze, silver, and gold — to streamline data ingestion, transformation, and analytics in a scalable manner.
A data management approach that prioritizes the design and management of metadata to enhance data integration, governance, and usability across systems.
A columnar storage file format designed for efficient data processing, optimized for use with big data processing frameworks like Apache Spark and Apache Hadoop.
A high-level, versatile language known for its readability and simplicity, widely used in web development, data analysis, artificial intelligence, and automation.
The process of extracting data from a data warehouse and loading it into operational systems, enabling organizations to leverage analytical insights in day-to-day operations.
A snowflake schema is a type of database schema used in data warehousing where a centralized fact table is connected to multiple dimension tables in a hierarchical manner.
A star schema is a type of database schema used in data warehousing where a centralized fact table is connected to multiple dimension tables in a denormalized manner.
A powerful open-source unified analytics engine for large-scale data processing and machine learning, designed to handle both batch and streaming data efficiently.
Data science is an interdisciplinary field that uses scientific methods, algorithms, and systems to extract insights and knowledge from structured and unstructured data, employing techniques from statistics, machine learning, data mining, and visualization to solve complex problems and make data-driven decisions.
Business Intelligence Systems are technologies, strategies, and practices used by organizations to analyze and interpret their data in order to make informed business decisions.
Data engineering involves designing, building, and maintaining the infrastructure and systems that enable the acquisition, storage, processing, and analysis of data at scale, ensuring data quality, reliability, and accessibility for downstream analytics and applications.
A data lakehouse combines the benefits of a data lake (scalability, flexibility, and cost-effectiveness for storing raw and unstructured data) with those of a data warehouse (structured querying, transactional integrity, and performance optimizations), providing a unified platform for both operational and analytical workloads in modern data architectures.
A data warehouse is a centralized repository that stores structured and organized data from multiple sources, providing a single source of truth for reporting, analysis, and decision-making within an organization. It is optimized for querying and analysis, often using techniques like indexing and data partitioning to improve performance.
