A go-to list of definitions for stream processing
The world of stream processing and analytics includes a mountain of concepts, tools and technologies. Components and processes may be unfamiliar, and the abundance of terms used with them adds to the chaos.
We’ve collected definitions to some of the most common concepts to clear up the confusion. Make your life a little easier and keep this glossary handy; you never know when you’ll need to look up the meaning of “real-time processing” or “time series data.”
Apache Flink is an open source, real-time processing library developed by the Apache Software Foundation. It can process both unbounded and bounded streams, which enables both data streaming and batch processing.
Flink is a good choice for Java-based applications of the following types:
- Event-driven: fraud detection and social networking are both types of apps that can use an event-driven model for better performance
- Data analytics: Uber has used streaming analytics to monitor its app and react to changes in traffic and weather
- Data pipelines: real-time search indexing is vital for a large ecommerce site like Alibaba
Apache Spark is an open source framework for large-scale data processing. Spark Streaming batches data into small groups with tight time windows. This micro-batching is a compromise between infrequent batch processing and genuine real-time stream processing. It’s a good approach for a corporate dashboard that refreshes every 15 minutes.
Read our Flink vs Spark vs Quix Streams comparison article.
Batch processing involves working with a fixed set of data that is analyzed at a certain point. It does not take any action on the data in real time. Batch processing often occurs at repeated intervals with a fixed frequency, on a regular schedule. But a batch process can usually be run on demand when required.
Updating analytics for a web application is a typical use of batch processing. Such a system might update user statistics once a day by reading through all log files from the previous day. In this context, batch processing is more suitable than real-time processing when:
- Visitors are more interested in daily analytics than real-time data.
- Analytics require a broader view of the data and consider it in the context of days or months of statistics.
See message broker.
A client library, sometimes called a helper library, is a set of code that application developers can add to their development projects. It typically interfaces with an API but is written in a specific language. This allows programmers to write code in their preferred language to access the API. It also tends to simplify common operations.
A client library is often distributed as part of an SDK, which also includes documentation and sample code. The Quix SDK provides client libraries for Python and C#.
In the Kafka context, a cluster refers to one or more servers running a Kafka Broker. Running a cluster is optional, but it conveys benefits from higher performance to data replication. A Kafka cluster is highly scalable and fault tolerant because if one server fails, another in the cluster can take its place.
A compute cluster is a set of computers operating to appear as one. In a cluster, each computer is referred to as a node. By parallelizing processing, clusters provide an excellent way of achieving fast performance for a lower price. Work involved in managing a cluster increases the overall complexity of a system.
Confluent is a real-time data streaming platform that uses Apache Kafka. It is available in a self-managed version or as a cloud-native service. Confluent simplifies the use of Kafka for small teams and for those without sufficient expertise in managing Kafka.
A connector is a software module that focuses entirely on transferring control and data between other components. It does so in an application-independent manner, which makes connectors highly portable and reusable.
Connectors simplify the work involved in integrating multiple systems quickly because they encapsulate the inner workings of those systems. They remove the need for each system to know about the inner workings of the others.
One of the Quix sample projects is a sentiment analysis applied to tweets. The analysis uses a connector to stream data from the Twitter API. The remaining components do not need to know the details of the Twitter API and can focus on the data this connector provides.
A consumer is a system component that reads events from a topic. A consumer aids concurrency since it can act alongside other consumers, but independently of them.
When a consumer is ready to carry out some work, it typically fetches a message from a broker. An individual consumer isn’t concerned with other consumers or how many producers are involved. It just deals with its own task: fetching messages and processing them.
For example, a producer may sense air temperature and write values to a topic. A consumer might read from that topic and present real-time values on a chart.
A consumer group is a collection of consumers assigned to a topic. Each consumer in the group can be assigned to one or more partitions. A group leader (one of the group’s consumers) assigns partitions to consumers. If a consumer needs to leave or join the group, the leader is responsible for assigning partitions.
The consumer group concept enables fault tolerance. If a consumer drops out, another is ready to take its place.
A data lake is a central repository that can store, process and secure large amounts of data. The data within a lake can be binary (e.g., an image), unstructured (e.g., an email) or structured (e.g., a JSON data file). Some of the data in a lake may be raw, straight from an input device. But it may also be transformed from raw data into other forms useful for reporting, machine learning, etc. This transformation can occur before it is stored in the lake or on demand.
A data stream is a continuous flow of data that is processed and analyzed as it moves from a source to a destination. Stock market prices and traffic behavior exemplify data that can be collected continuously and processed immediately.
Data streaming is the act of working with a stream of data. It’s often contrasted with batch processing, which analyzes a set of data at a fixed time or interval, usually hours or even days after it was produced.
We’ve answered frequently asked questions about data streaming.
A data warehouse is a centralized, canonical storage of data. Large applications usually use them to consolidate and integrate vast amounts of data from many sources. Data warehouses often improve the overall quality of data within an organization.
Tightly coupled components can be challenging to work with independently. Decoupling refers to the elimination of interdependences between pieces of software. When decoupled components communicate with each other, they can easily be swapped out, removed or modified without impacting the behavior of others.
A typical consumer-producer model uses a message broker to handle communication between components. The message brokers enable individual consumers and producers to carry out their tasks without worrying about the other.
Extract, load, transform (ELT)
ELT is a method for adding data to a data lake, data warehouse or database. It prioritizes loading over transforming. Data is added to the lake in its raw state, then later transformed on demand. This is in contrast to extract, transform, load (ETL), which transforms data before it is loaded.
ELT puts more responsibility on the data store and less on the source(s) of that data. It’s optimized for cases where a lot of diverse data is frequently produced or generated.
An event is an activity of interest. It’s a single data point in a stream that represents a value at a specific moment. An event includes a key, value, timestamp and optional metadata in the form of headers. An event might describe car model A (key) traveling at 80 MPH (value) at 12:35 PM on 1st Feb 2022 (timestamp).
A producer publishes events while a consumer reads and processes them. In an event-streaming model, the topics that events are written to and read from are referred to as streams.
A system is fault tolerant if it continues operating when one or more of its components fail. Fault tolerance is a scale rather than a binary property. A system’s standard “final resort” is reverting to a previous state, which is possible if backups exist; however, it is not the preferred first course of action since it is time consuming and will nevertheless result in some data loss. Decoupling components, graceful degradation and redundancy achieve better fault tolerance.
A header is part of an event that contains metadata in addition to the event’s primary data. In contrast to the actual value that the event represents, a header may include a name, timestamp and type. Headers vary by implementation. Some streaming platforms consider the timestamp part of the header, while others represent it as a fixed event field.
In-memory processing refers to the processing of data in RAM rather than reading it from disk and writing it back to disk again. Data stored in memory incurs far lower latency than on disk. As a result, in-memory processing is much more efficient. With decreased cost over time, even large quantities of data can be stored and processed in memory.
Apache Kafka is an open-source stream-processing platform written in Scala and Java. Engineers at LinkedIn originally developed Kafka before the Apache Software Foundation adopted it. Some of the biggest companies use Kafka in industries heavily involved in data streaming: banking, insurance and telecoms.
Kafka is either self-managed or hosted in the cloud.
Kafka is a complicated technology, one that requires dedicated engineers to configure and manage. There are various factors to consider, from how Kafka is distributed to ensure availability to how data is stored securely. Handling all this infrastructure is a considerable task on its own, regardless of the challenges associated with application development on top of that.
A hosted service mitigates this effort. Running Kafka in the cloud means a third party takes care of provisioning and building the Kafka part of your architecture. You concentrate on the application. Confluent’s Cloud product is a popular example of a cloud-based streaming platform with Apache Kafka at its core. Quix is another example.
An event’s key is an identifier used to access that event. The key usually stores data about the message itself rather than the values within it, so is typically used for partitioning, logging or related tasks. In cases where the key contains even less data relating to the message, a header may be used to store and transmit metadata instead.
Machine learning is a programming method that automates analytical model building. With machine learning (ML), algorithms improve through experience and data analysis. Machine learning is a branch of artificial intelligence.
Stream processing helps enable machine learning by making data analysis a real-time process. A system can then feed the insights it’s gained back into its processing of future data. For example, a chess computer can continuously analyze its opponent’s moves to improve its responses.
A message is a record of an event.
A message broker is a component responsible for message-based communication between systems. It routes messages between a sender and its recipients, transforms messages between forms for reliable consumption, composes complete messages from partial ones and ensures reliable storage.
In a data-streaming architecture, the message broker plays a similar role to that of a database in a batch-processing model. Adopting a message broker is one of the major paradigm shifts when moving to stream processing.
Apache Kafka is commonly used as a message broker. RabbitMQ is a popular alternative.
A Jupyter Notebook combines source code with other resources to provide an interactive document. For example, code can be embedded alongside Markdown-formatted text. The code can then be executed or modified by somebody reading the document.
Jupyter Notebooks can be written in various languages including Ruby, Go and PHP. The default option is Python. Jupyter Notebooks can even integrate with big data tools like Apache Spark and can be used with Python libraries like pandas.
The Quix platform is fully compatible with Jupyter Notebooks.
Pandas is an open-source data-science Python library for analysis and manipulation. The library is over a decade old and has a strong global community.
Pandas’s features include:
- A DataFrame structure that represents two-dimensional data. The Quix SDK supports pandas DataFrames for reading and writing data.
- Tools for converting data between file formats.
- Aggregation of data via grouping.
- Date tools for expressing offset or date ranges.
Pandas uses the NumPy package for numerical computing. It also uses Cython to help integrate C code, which supports processing-intensive features of pandas.
Parameter data is a means of extending the information sent as part of a message alongside its main value. It consists of further measurements that directly relate to event data. In particular, parameter data typically varies with time and has a direct relation with a message’s timestamp. Parameter data may be part of the message header.
Quix represents most time series data as parameters.
A partition is a subdivision of a topic. Messages belong to individual partitions that distribute the responsibility for handling them across a consumer group. Dividing a topic into smaller, manageable parts means that consumers can work more efficiently and can be assigned to individual partitions as required.
Messages within a partition are ordered, but that order is not guaranteed across parallel partitions. Producers and consumers can connect to specific partitions if necessary. But if they are written in a way that means record order is irrelevant, the partition can be overlooked. In that case, the broker can assign partitions.
Partitions increase the Fault Tolerance of the overall system. Partitions also provide for scalability since the system can distribute them amongst multiple brokers.
A message (or other forms of data) is persistent if it exists until it’s read. If a data stream is persistent, it will survive a shutdown of the data-streaming service, typically by the system writing it to disk. Persistent data allows a system to hold larger amounts of historical data than a stream alone. Persistent data can also be useful in a recovery from a catastrophic event or for correcting historical inaccuracies caused by bugs.
Quix offers persistence at the topic level. If you persist a topic, you’ll be able to explore data streamed to it via the data explorer.
A pipeline enables data to flow from one point to another. It’s software that manages the process of transporting that data. A pipeline is responsible for routing data as a domain-agnostic process and remains separate from other components of an application.
A streaming pipeline is one that continuously moves data from one point to another. A streaming pipeline typically handles many more events than a non-streaming data pipeline. For example, Twitter uses data streaming to process hundreds of billions of events every day.
A producer is a component that creates messages representing events and sends them to a topic.
A producer is not concerned with what happens to the data once it’s sent it. In the decoupled producer-consumer model, a producer’s responsibility ends when it releases data to the consumer.
Python is an object-oriented language with a preference for readability. Data scientists often use Python, and in 2018 two-thirds reported using Python, more so than any competing software.
Several high-profile Python libraries deal with data analysis, including NumPy and pandas.
In contrast to batch processing, real-time processing handles data continuously, ideally as soon as it is created. Data is processed in isolation, meaning that one data record is not influenced by another. This allows for parallelization of the process since the order of processing is irrelevant.
Sometimes called a message, a record includes details about an event. These include the time it happened (timestamp), the value it represents and additional metadata in headers.
When components in a system are tightly coupled, communication between them becomes more complicated the more components there are. Each new component adds a requirement for every other component to be able to interoperate with it effectively. Changes to one component may affect many more. The more components there are, the worse this problem gets.
The producer-consumer model alleviates this problem by loosening the coupling and allowing components to be independent while maintaining communication.
Quix’s architecture makes developing a scalable application much easier, both technically and in terms of managing your team and its resources.
An SDK — Software Development Kit — is a collection of client libraries, documentation, and sample code designed to ease the use of an API.
A data source produces data which it then passes to other components in a system. A data sink receives data and stores it for future use. Many devices or components act as both a source and a sink.
Examples of data sources range from those that do a lot of processing themselves — e.g., a mainframe writing system logs — to specialist devices that primarily generate data, such as a video recorder or medical instrument.
A data sink often stores data over a long period. An archival device, such as a tape backup or a cloud-storage solution such as Amazon S3, can act as a data sink.
SQL stands for Structured Query Language, a language defined by a standard and used to interact with a database, primarily for storing and retrieving data. SQL is traditionally paired with a relational database, although some relational data streaming systems also use SQL.
SQL support for streaming data is present in Kafka and Apache Spark via materialized views.
Typical streams are unbound data sets that continuously update; they carry a series of events. Streams are analogous with topics, although the term clarifies and emphasizes that the data involved is produced in real time.
In Quix, a stream represents a session of one data set that has a beginning and an (optional) ending, such as the journey of a single car during a race or one run-through of a game. Read more about Quix streams in our documentation.
Streaming analytics refers to the processing and analysis of real-time data records in a continuous process. Data may originate from a wide range of sources, including other computer systems and real-world sensors such as thermometers.
Time-sensitive data can be out of date if analyzed using a batch process. The streaming analytics process ensures such data is used at the earliest possible point to guarantee maximum accuracy.
Stream processing is the processing of data in real time, when it is produced, as opposed to batch processing, which strictly separates data creation from its analysis. Stream processing enables a form of parallel processing.
Real-time data processing also benefits from in-memory processing. A series of operations (kernel functions) is applied to each element in a data stream.
Related reading: What is stream processing?
Two components or systems are tightly coupled when they strongly depend on each other to operate. Changes to one may affect the other, even to the extent of breaking its operation. Tightly coupled components can be costly since changes to them are laborious. If you change one tightly coupled component, it can cause unwanted behavior in the other, so every change needs to take the dependent component into account.
Time series data
Time series data refers to a group of data points indexed in time order. Such data can be described by a series of values taken at fixed points in time. Time series data is discrete rather than continuous. Time series data is usually sampled at equally spaced points in time; this is often the easiest and results in the most meaningful data since it can be compared at like-for-like intervals.
A timestamp is a value representing a point in time. A typical example is Unix time, which is a count of the seconds since 1 January 1970. Other formats and standards exist with varying support for complex factors such as time zones and leap seconds. An event’s timestamp is normally the exact time at which the event took place.
A timestamp’s value can be as granular as required. It can represent a specific second or a year. Quix supports nanosecond precision, so you can use it to build the most demanding, data-intensive applications.
A topic is a durable store of events. In a streaming pipeline, messages are placed in a stream, which can be persisted as a topic. In such a case, the topic is essentially a stream at rest.
Topics collect related events; you can think of a topic as a file system directory that contains associated files. When producers and consumers interact with a stream, they specify a topic to write to and read from. Topics are typically split into separate partitions.
A value is anything that you might want to record and analyze. A value could be the speed of an object, the temperature, the number of arms something has, etc. In data streaming, values may fluctuate considerably in a very short period of time.
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