Deploying and managing computing resources through cloud platforms for scalability and reliability.
Infrastructure as Code (IaC): Method of managing and provisioning computing infrastructure through machine-readable definition files.
Data Checkpoints are a technique used in computing to capture and save the state of a process, allowing for recovery from failures.
Downstream Data is data that flows from upstream sources to downstream consumers, crucial for data-driven decision making and analytics.
Auto Recovery mechanisms in systems enable the automatic restoration of data and processes following a failure or crash.
Data Contracts define the structure, format, and constraints of data shared between systems, ensuring consistency and reliability.
Data Lag refers to the delay between data creation and its availability for processing or analysis, impacting decision-making.
Data Skew is an uneven distribution of data across different nodes in a distributed system, leading to bottlenecks and performance issues.
Data Iceberg is concept in data management where the bulk of data, often unseen, requires significant resources to manage.
Fault tolerance ensures systems continue to operate even when components fail, critical for maintaining service and data integrity in technology.
Streaming Pipelines: Architectures that allow for continuous data flow, enabling real-time data processing and analytics.
Stateful Storage is a type of storage solution that retains state information across sessions, Fault Tolerance is the ability of a system to continue operating without interruption when one or more of its components fail.
Throughput measures of how much data can be processed in a given time frame, crucial for system performance evaluation.
Real Time Data Processing processes data as it arrives, enabling immediate analysis and action for timely insights.
Modern data stack: Discover the essential components and benefits of a cutting-edge data infrastructure in today's digital landscape.
Scalable infrastructure: Discover how to build a flexible and efficient system that grows with your business needs.
Data infrastructure optimization: Optimize your data infrastructure for peak performance and cost-effectiveness.
Understand warehousing solutions, systems that store and manage large volumes of data for query and analysis.
Explore lakehouse architecture, a hybrid data management approach combining data lake flexibility with data warehouse performance.
Understand NoSQL databases, flexible data storage solutions designed for large-scale data management, supporting varied data models.
Explore Cloud Computing, the delivery of computing services over the internet, including storage, processing, and software on demand.
Understand Data Orchestration, the automated arrangement, coordination, and management of complex data workflows and services.
Explore Data Streaming, the technology that allows for the continuous transfer of data at high speed for real-time processing and analysis.
Understand Data Infrastructure, the foundational systems and services that support the collection, storage, management, and analysis of data.
Get insights into Data Management Platforms (DMP), systems that collect and manage data, allowing businesses to target specific audiences.
Cloud Native Data Management refers to systems and practices specifically designed to handle data within cloud environments.
MQTT, or Message Queuing Telemetry Transport, is a lightweight messaging protocol specifically designed for machine-to-machine (M2M) communication.
A Software Development Kit (SDK) is essential in data management as it provides developers with a set of tools to create applications specific to data management platforms.
A Content Delivery Network (CDN) improves data management by optimizing the delivery of data-heavy applications.
The ELK Stack is a powerful trio of tools that work in unison to facilitate the searching, analyzing, and visualization of data. It encompasses Elasticsearch, Logstash, and Kibana
The Systems Development Life Cycle (SDLC) plays a crucial role in data management by providing a structured approach to the development and maintenance of data systems. This methodology ensures that data management solutions are designed, implemented, and updated in a systematic and efficient manner.
IoT, or the Internet of Things, significantly augments data management systems by providing a continuous stream of real-time data from a myriad of connected devices.
An API defines a set of rules and protocols for building and interacting with software applications, making it possible for developers to access and use functionalities provided by an external service or software component.
Apache Airflow is a platform to programmatically author, schedule and monitor workflows. Learn about the history of Apache Airflow and more here.
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