The Data Glossary

An application programming interface (API) is a set of protocols that help computer programs interact with one another. Learn how APIs work, with examples, an introduction to each API type, and the best tools to use.

Change Data Capture (CDC) is a software process that identifies, processes, and tracks changes in a database. Ultimately, CDC allows for low-latency, reliable, and scalable data movement and replication between all your data sources.

There are plenty of benefits for moving to the cloud, however cloud migrations are not a simple, one-time project. Learn how cloud migrations work, and the best way to undergo this complex process.

Similar to event stream processing, complex event processing (CEP) is a technology for aggregating, processing, and analyzing massive streams of data in order to gain real-time insights from events as they occur.

Data governance is a process to ensure data access, usability, integrity, and security for all the data enterprise systems, based on internal data standards and policies that also control data usage. Effective data governance ensures that data is consistent and trustworthy and doesn't get misused. It's increasingly critical as organizations face new data privacy regulations and rely more and more on data analytics to help optimize operations and drive business decision-making.

Also known as data in transit or data in flight, data in motion is a process in which digital information is transported between locations either within or between computer systems. The term can also be used to describe data within a computer's RAM that is ready to be read, accessed, updated or processed. Data in motion is one of the three different states of data; the others are data at rest and data in use.

Learn the most common types of data stores: the database, data lake, relational database, and data warehouse. You'll also learn the difference, commonalities, and which to choose.

A data pipeline is a set of data processing actions to move data from source to destination. From ingestion and ETL, to streaming data pipelines, learn how it works with examples.

Streaming Data is the continuous, simultaneous flow of data generated by various sources, which are typically fed into a data streaming platform for real-time processing, event-driven applications, and analytics.

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.

Also known as distributed computing, a distributed system is a collection of independent components on different machines that aim to operate as a single system.

Event streaming (similar to event sourcing, stream processing, and data streaming) allows for events to be processed, stored, and acted upon as they happen in real-time.

Extract, Transform, Load (ETL) is a three-step process used to consolidate data from multiple sources. Learn how it works, and how it differs from ELT and Streaming ETL.

Learn how Kafka benefits companies big and small, why it's so popular, and common use cases.

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.

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.

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.

Stream processing allows for data to be ingested, processed, and managed in real-time, as it's generated. Learn how streaming differs from batch processing, how it works, and the best technologies to get started.

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

What is ETL vs ELT streaming, and how are they different from streaming ETL pipelines? Learn the differences between data pipeline and integration tools, their processes, and which to choose.