Apache Beam is a unified model that defines and executes batch and stream data processing pipelines. Learn Beam architecture, its benefits, examples, and how it works.
Apache Flink is an open-source framework that unifies real-time distributed streaming and batch processing. Learn about Flink architecture, how it works, and how it's used.
Apache Kafka is an open-source distributed streaming platform that's incredibly popular due to being reliable, durable, and scalable. Created at LinkedIn in 2011 to handle real-time data feeds, today, it's used by over 80% of the Fortune 100 today to build streaming data pipelines, integrate data, enable event-driven architecture, and more.
Apache NiFi is an integrated data logistics platform for automating the movement of data between disparate systems.
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.
Batch processing is when the processing and analysis happens on a set of data that have already been stored over a period of time. An example is payroll and billing systems that have to be processed weekly or monthly. Learn how batch processing differs from stream processing, and the best toosl to get started.
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.
Data ingestion is the extraction of data from multiple sources into a data store for further processing and analysis. Learn about ingestion architectures, processes, and the best tools.
Data integration works by unifying data across disparate sources for a complete view of your business. Learn how data integration works with benefits, examples, and use cases.
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.
Data mesh is a decentralized approach for data management, data federation, governance designed to enhance data sharing and scalability within organizations.
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.
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.
An ESB is an architectural pattern that centralizes integrations between applications.
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.
Event-driven architecture is a software design pattern that can detect, process, and react to real-time events as they happen. Learn how it works, benefits, use cases, and examples.
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.
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.
Microservices refers to an architectural approach where software applications are composed of small, independently deployable services that communicate with each other over a network.
Middleware is a type of messaging that simplifies integration between applications and systems. Learn how middleware works, its benefits, use cases, and common solutions.
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.
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 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.
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.