The Data Glossary

This in-depth Confluent Cloud and Amazon MSK comparison will show you how each stacks up on scalability, resilience, and platform capabilities.

Apache Iceberg is a high-performance open table format for large analytic datasets.

Kafka is a distributed data streaming or event streaming engine, used to power event-driven architectures, real-time data pipelines, and much more. Learn what Kafka is, how it works, its benefits and challenges, and popular use cases.

Application security refers to the different sets of processes, practices, and tools maintaining the security of the software application against any external threat or vulnerability.

Application integration ensures interdependent components of software applications can share data and workflows in real time. Explore the key benefits of different app integration approaches for business efficiency, how it differs from data integration, and the common challenges involved.

ASPICE is a framework designed to assess and enhance the software development processes within the automotive industry.

Learn the fundamental definition of big data, including key characteristics known as the "3 V's" (volume, velocity, and variety). You'll discover why the overall concept is so important in enterprise analytics and review its common challenges, benefits, and use cases.

Learn how data flows through real-time applications and best practices for building them with event-driven architectures that process data as it's created. You'll also explore common use cases, like real-time fraud detection, and understand why Kafka and Flink are essential technologies for powering these systems.

In today’s fast-paced environment, success in software development depends significantly on development speed, reliability, and security.

A comprehensive guide to cloud computing, explaining what it is, how it works, and its various pros and cons. Learn about the different types of cloud services, compare the top cloud providers, and get tips on how to choose the right service for your needs.

Discover six effective cloud migration strategies to transform your business. Learn how to optimize costs, boost scalability, and ensure a smooth transition to the cloud.

CQRS is an architectural design pattern that helps handle commands to read and write data in a scalable way. Learn how it works, its benefits, use cases, and how to get started.

Data fabric architectures enable consistent data access and capabilities across distributed systems. Learn how it’s used, examples, benefits, and common solutions.

Also known as dataflow or data movement, data flow refers to how information moves through a system. Learn how it works, its benefits, and modern dataflow solutions.

Learn how data products power data streaming, their importance for real-time analytics, and best practices for building effective data integration solutions.

If computer networks were cities, routing would be the interstates and freeways connecting them all, and vehicles would be the data packets traveling along those routes.

Data serialization can be defined as the process of converting data objects to a sequence of bytes or characters to preserve their structure in an easily storable and transmittable format.

You data strategy should unify your operational and analytical systems, effectively breaking down the "slow, brittle ETL wall." Discover how to create a practical roadmap that aligns with your existing investments and focuses on high-impact value streams to get started.

Learn how a data streaming platform (DSP) enables organizations to capture, store, and process data as a continuous flow of real time events.

A database is a collection of structured data (or information) stored electronically, which allows for easier access, data management, and retrieval. Learn the different types of databases, how they're used, and how to use a database management system to simplify data management.

A Distributed Control System (DCS) is a control system used in industrial processes to manage and automate complex operations.

Dynamic content creation is the key to creating personalized experiences that resonate with your audience

Data asset management is an approach that treat an organization's data as a valuable business asset instead of just bits of information to store and secure. Learn the key pillars for transform raw data into insightful, reliable data assets, including governance, discovery, lineage, and quality.

Event sourcing tracks the current state of the system, and how it evolves over time. Learn how event sourcing works, its benefits and use cases, and how to get started.

FIPS or Federal Information Processing Standards is a set of publicly announced standards developed by the National Institute of Standards and Technology (NIST).

Apache Flume is an open-source distributed system designed for efficient data extraction, aggregation, and movement from various sources to a centralized storage or processing system.

Building a streaming data pipeline might seem complex, but it can be broken down into manageable steps.

IaC is a transformative approach to managing IT infrastructure by allowing organizations to define and provision their resources through code rather than manual processes.

Legacy systems can often be dusty relics of a bygone era, leaving you with outdated technology.

Apache Kafka's backup mechanisms are essential components of a robust data infrastructure strategy.

Apache Kafka is the most commonly used stream processing / data streaming system. Learn how Kafka benefits companies big and small, why it's so popular, and common use cases.

This Kafka cheat sheet cover essential terminology and core architectural concepts you need to know to succeed with data streaming. Learn the key Kafka CLI commands needed to manage topics, producers, and consumers in this quick-reference guide.

Learn the difference between Kafka's command-line interance (CLI) tools and web UI's like Confluent Cloud Console and see which fits better for your job role and needs, your organizational data challenges, and your business requirements.

A Kafka Dead Letter Queue (DLQ) is a special type of Kafka topic where messages that fail to be processed by downstream consumers are routed.

Learn the key differences between horizontal scaling (adding more servers to your cluster), and vertical scaling (increasing the compute resources) in Kafka. You'll understand why Kafka is especially designed to excel at horizontal scaling and how you can use this strategy to handle massive data loads, ensure high availability, and lower your Kafka costs.

In production, Kafka developers often run into issues like consumer lag, under-provisioned partitions, and broker disk exhaustion. Learn how to diagnose these common Kafka issues and preventative best practices to help you troubleshoot, monitor, and configure your cluster for high performance and reliability.

A Kafka message key is an attribute that you can assign to a message in a Kafka topic. Each Kafka message consists of two primary components: a key and a value.

The Kafka message size limit is the maximum size a message can be to be successfully produced to a Kafka topic.

Learn how Kafka MirrorMaker enables cross-cluster replication. Explore its architecture, setup, use cases, best practices, and troubleshooting tips for seamless Kafka data mirroring.

A partition key in Apache Kafka is a fundamental concept that plays a critical role in Kafka's partitioning mechanism.

Apache Kafka partition strategy revolves around how Kafka divides data across multiple partitions within a topic to optimize throughput, reliability, and scalability.

Learn how to conduct effective Kafka performance testing with this guide. Explore best practices, essential tools, and key metrics to optimize Kafka's throughput, latency, and scalability.

Kafka rebalancing is a necessary process where topic partitions are automatically redistributed among consumers in a group to ensure fault tolerance and balance the workload. Learn what triggers a rebalance, its significant impact on performance and latency, and discover strategies to manage and minimize its disruptions.

Kafka's retention capabilities allow you to configure topics to automatically delete data after a certain time period or once a specific storage size is reached. Learn about how to configure Kafka retention and the log compaction policy, an alternative mechanism for retaining only the most recent value for each message key.

Kafka strategically uses partitions to parallelize processing and achieve elastic horizontal scalability. Discover key best practices for scaling Kafka workloads, including how to select the right number of partitions, monitor consumer lag, and manage both stateful and stateless applications effectively.

Learn about the common security vulnerabilities and misconfigurations that can impact your Kafka environment. You'll read about essential Kafka security best practices, including proper encryption, authentication, and authorization, to help you mitigate these risks and secure your data.

Kafka Topic Naming convention keeps your data organized and makes it easier to understand, scale, and maintain.

There are critical design tradeoffs (e.g., cost, complexity, feature velocity) that you need to consider when configuring and operating Kafka environments. You'll understand how to balance competing factors like performance and throughput against durability, latency, and overall cost to fit your specific use case.

Learn the differences between Kafka, an open source streaming engine, and Confluent, an enterprise-grade data streaming platforms, built by the original co-creators of Kafka and complete with pre-built integrations, governance, stream processing, and analytics & AI capabilities.

Learn the fundamental differences between Kafka Streams and Apache Spark, including how their architectures and processing models compare. You'll get get a clear breakdown of their different approaches to state management and see which specific real-time or large-scale analytical use cases are ideal for each technology.

Message brokers facilitate communication between applications, systems, and services. Learn how they work, their use cases, and how to get started.

While message brokers and message queues are often used interchangeably, understanding their distinct characteristics and use cases can help you make better architectural decisions.

Microservices refers to an architectural approach where software applications are composed of small, independently deployable services that communicate with each other over a network.

Apache NiFi is an integrated data logistics platform for automating the movement of data between disparate systems. It can handle various data types and support various protocols. Learn how it's used, and how it works.

The National Institute of Standards and Technology's Secure Software Development Framework NIST SSDF is a set of guidelines that are intended to assist organizations in developing their software securely.

Observability is the ability to measure the current state or condition of your system based on the data it generates. With the adoption of distributed systems, cloud computing, and microservices, observability has become more critical, yet complex.

There's a massive divide between operational data, which powers real-time daily business activities, and analytical data, which often relies on historical information used for busienss insights and decision-making? Learn the challenges of bridging the gap between these two data types and discover modern solutions for integrating them effectively.

OWASP is a non-profit organization dedicated to improving the security of software.

Learn the differences between prompts, workflows, and agents. See a breakdown of each, with example use cases and how to select an approach.

Pub/sub is a messaging framework commonly used for inter-service communication and data integration pipelines. Learn how it works, with examples, benefits, and use cases.

RabbitMQ is a message broker that routes messages between two applications. Learn how RabbitMQ works, common use cases, pros, cons, and best alternatives.

RabbitMQ and Apache Kafka are both open-source distributed messaging systems, but they have different strengths and weaknesses.

RAG leverages real-time, domain-specific data to improve the accuracy of LLM-generated responses and prevent hallucinations. Learn how RAG works with use case examples.

Real-time data (RTD) refers to data that is processed, consumed, and/or acted upon immediately after it's generated. While data processing is not new, real-time data streaming is a newer paradigm that changes how businesses run.

A complete comparison of Kafka vs Redpanda and two cloud Kafka services - Confluent vs Redpanda. Learn how each works, the pros and cons, and how their features stack up.

Refactoring is an important part of software development that optimizes the code's internal structure without changing how the application works on the outside.

REST API stands for Representational State Transfer. Learn more about REST API, how it simplifies server communication, and how it leverages large-scale data.

Learn core concepts of real-time streaming architectures as you explore real-world examples from industries like finance, e-commerce, and healthcare to understand how Apache Kafka is used to build these powerful, event-driven systems.

SIEM involves aggregating and analyzing log data to detect threats based on security event data. You'll learn the key benefits of implementing a SIEM solution, such as preventing security breaches, enabling rapid incident response, and ensuring regulatory compliance.

Shift Left in data integration is derived from the software engineering principles of Shift Left Testing where tests are performed earlier in the software development lifecycle to improve quality of software, accelerate time to market, and identify issues earlier.

Learn how to integrate Apache Kafka with Spring Boot. Explore setup, producer-consumer architecture, configuration, security, and best practices for scalable event-driven applications.

SAST is a method that checks for security flaws in code before it reaches production.

Stream processing allows for data to be ingested, processed, and managed in real-time, as it's generated. Learn how stream processing works, its benefits, common use cases, and the best technologies to get started.

Streaming analytics is an approach to business analytics and business intelligence where data is analyzed in real-time. Learn how streaming analytics works, common use cases, and technologies.

Streaming data pipelines move data from multiple sources to multiple target destinations in real time. Learn how they work, with examples and demos.

Technical debt is a concept that originated in software engineering and refers to the future costs due to shortcuts taken in system development.

Learn how to tackle data integration challenges in microservices with proven strategies, event-driven architectures, and tools like Apache Kafka for seamless scalability.