A streaming database is broadly defined as a data store designed to collect, process, and/or enrich an incoming series of data points (i.e., a data stream) in real time, typically immediately after the data is created. This term does not refer to a discrete class of database management systems, but rather, applies to several types of databases that handle streaming data in real time, including in-memory data grids, in-memory databases, NewSQL databases, NoSQL databases, and time-series databases.
A streaming database is in contrast to traditional relational database management systems (RDBMSs), in which a database administrator would typically load data via an ETL tool/process at regular intervals such as nightly or weekly. A streaming database may sit alongside RDBMSs for modern use cases in larger enterprises. As the volume of data continues to grow and the velocity of data continues to accelerate, some technologies that once relied primarily on batch-oriented databases now rely more heavily on streaming database technologies (e.g., recommendation engines).
Stream processing is the practice of taking action on a series of data at the time the data is created. Historically, data practitioners used “real-time processing” to talk generally about data that was processed as frequently as necessary for a particular use case. But with the advent and adoption of stream processing technologies and frameworks, coupled with decreasing prices for RAM, “stream processing” is used in a more specific manner.
Different ways to analyze streaming data
1. Imply.io
Imply is the druid manufacturer based on Apache druids and is a powerful tool for visualizing event and data streams.
Imply provides real-time analysis and a data platform supported by Apache Druid and Apache Kafka. Druid can record and store data streams in a shared file system, and users can also interact with Druid from the console. It has a large number of data sources such as Kinesis and Kafka, as well as a variety of analysis tools.
Druid allows you to store real-time and historical data, i.e., time series, in nature and stream data in a variety of formats such as CSV, XML, JSON, or JSONB. Although there is no user interface for Apache Druid, you do not need to write code to make it work.
Benefits
1. Management & Security
NIX-based environment, including public and private cloud, click here for more information. This implies the use of NIX as the primary operating system in a NIX-centric environment (e.g. Linux, Windows).
2. Visualization
Imply Pivot is a simple drag-and-drop interface that allows you to perform real-time analysis and visualization of your data with a single click. You can drop, organize, and share important visualizations. Visualizations are fully interactive and support the analysis of data from various sources such as web, mobile, and desktop applications.
3. Analyze
Apache Druid is a powerful analytics database designed for use in a wide range of applications, from web applications to news buses and mobile applications.
2. Materialize.io
Materialize is incrementally updated materialized for processing streaming data. It is built upon Timely Dataflow and Differential Dataflow.
It supports PostgreSQL and a variety of integration points. It is written in Rust, which is well suited for various types of development-intensive computing and built to be friendly for developers.
Materialize also allows you to ask questions about your data and then get low latency answers, even if the underlying data changes.
Benefits
1. Real-Time Data Visualizations
Materialize connects you to business intelligence tools, creates your own application dashboards, and creates real-time visualizations. ANSI queries in standard SQL Materialize allows you to easily connect data sources from any data source and connect them to your application.
2. SQL Development
Materialize is compatible with PostgreSQL.
3. Fast Results
Materialize provides a stream of real-time information about real-world events, such as weather, traffic, and traffic patterns.
3. Rockset
Rockset is a real-time indexing database service that serves low – latent, high – analytical queries on a large scale. Converted indexes ™ created in real-time and exposed via a RESTful SQL interface, with support for a wide range of data types such as SQL, JSON and CSV.
Benefits
1. Serverless Auto-Scaling in the Cloud
You can use Rockset to automatically scale in the cloud and automate cluster deployment and index management. You’re able to minimize operational overheads through serverless auto-scaling and minimize costs thanks to automating scaling and automating cluster deployment, indexes, management, etc.
2. Query Lambdas
A Lambdas query calls parameterized SQL queries stored in Rockset and can be executed via special sleep endpoints, but they are also useful for other applications.
With Query Lambdas, you can enforce version control and integrate it into your CI / CD workflow. You can also use it on-the-go, with a single command-line interface (CLI), or even as a standalone application.
3. Full SQL
Rockset enables you to always retrieve data, change data and execute standard SQL queries, including standard queries for SQL Server, SQLite, and other SQL databases, directly on semi-structured data.
4. ksqlDB
The new version of ksqlDB is designed to give developers a comfortable RDBMS feel, with the ability to extract data at any time. It’s a bit like SQL Server, but with a bit more performance and a lot more flexibility. The new release of KsqlDB is built in a new way, giving developers a cozy RDBMS feel without having to incur any overhead costs in terms of data at any given time and at any given time.
ksqlDB currently supports push queries and pull queries. Pull queries are most common. It is a form of query issued by a client that retrieves a result as of “now”, similar to a query against a traditional RDBMS. You may find a list of pull queries limitations here.
Benefits
1. Streaming ETL pipeline
Streams can be accessed and viewed in a coherent and powerful SQL language using a range of powerful tools.
2. Materialized cache
This gives you the ability to manage the materialized End-to-End views, and you do not need to query the ksqlDB state table to store data from the data store. ksqlDB does not manage schemas automatically.
5. NoSQL
NoSQL uses a document that is more of a relational model, although they offer many of the same capabilities as traditional databases. Traditional databases store their data in tabular relationships, which means that a NoSQL database does not have a fixed table structure as found in relational databases, but is stored in a variety of ways, such as key value pairs or JSON objects. Users often find NoSQL databases to store very wide columns and sparsely populated data.
Although NoSQL databases are designed to remain light and efficient in scale, normalization can increase the capacity of traditional relational RDBMS to expand to terabytes. Therefore, SQL databases are not designed to overcome the limitations of the Relational Databases found in other databases.
Benefits
1. Handle Large Volumes of Data
SQL databases are usually implemented with a scale – an architecture based on performance improvement, rather than the traditional database-as-a-service approach.
2. Store different structures of data
If you use a relational database, you need to design a data model and then load and transform that data into the database. Structured tables have a predefined schema, and the data is then transformed and stored in a structured table.
How Data Streaming Works
Streaming data allows pieces of data to be processed in real or near real-time. The two most common use cases for data streaming:
Streaming media, especially video
Real-time analytics
Data streaming used to be reserved for very select businesses, like media streaming and stock exchange financial values. Today, it’s being adopted in every company. Data streams allow an organization to process data in real-time, giving companies the ability to monitor all aspects of its business.
The real-time nature of the monitoring allows management to react and respond to crisis events much quicker than any other data processing methods. Data streams offer a continuous communication channel between all the moving parts of a company and the people who can make decisions.
Streaming media
Media streaming is one example. It allows a person to begin watching a video without having to download the whole video first.
This allows users to begin viewing the data (video) sooner, and, in the case of media streaming, prevents the user’s device from having to store large files all at once. Data can come and go from the device as it is processed and watched.
Real-time analytics
Data streams enable companies to use real-time analytics to monitor their activities. The generated data can be processed through time-series data analytics techniques to report what is happening.
The Internet of Things (IoT) has fueled the boom in the variety and volume of data that can be streamed. Increasing network speeds contribute to the velocity of the data.
Thus we get the widely accepted three V’s of data analytics and data streams:
Variety
Volume
Velocity
Paired with IoT, a company can have data streams from many different sensors and monitors, increasing its ability to micro-manage many dynamic variables in real-time.
From a chaos engineering perspective, real-time analytics is great because it increases the company’s ability to monitor the company’s activities. So, if equipment were to fail, or readings were to send back information that needed quick action, the company has the information to act.
Data streams directly increase a company’s resilience.
Business Use Cases for a Streaming Database
There are many reasons why business teams are encouraging their IT partners to adopt streaming databases. At a high level, business teams see that streaming databases can enable them to:
Respond to events faster than competitors
Enable real-time alerting for market changes
Support preventive maintenance use cases
Analyze data in real time as it is generated
Deploy real-time machine learning inference
Technical Use Cases for a Streaming Database
Technologists are adopting streaming databases for a variety of use cases. These include:
Stream data enrichment. One important use case for streaming databases is storing data that can enrich streaming data. Since streaming data, especially from Internet of Things sources, is almost always minimalistic, joining that data with reference data from a streaming database can provide more context for analysis.
Real-time event capture and processing. From the C-level down, many companies want to become event-driven, and streaming databases can help IT teams get there while often providing some of the same benefits as traditional databases, such as the ability to interact with SQL-like languages.
Microservices architectures. Streaming databases can move data from purpose-built app to purpose-built app in real time, so they can serve as the backbone for sharing data and messaging in microservices architectures, which are becoming more common.
Stream processing. Much of the data that people, applications, and machines create today is generated as a series of ongoing events. Streaming databases can execute continuous queries to process these events as they occur rather than as idle batches of stale data.
Challenges with data streaming
Data streams offer continuous streams of data that can be queried for information.
Generally, the data will need to be in order, which is sometimes the point of having a stream. (After all, any messaging app needs to have all the messages in order.)
Because data may come from different sources, or even the same source, but it moves through a distributed system, it means the stream faces the challenge of ordering its data and delivering to its consumer.
So data streams directly encounter the CAP theorem problem in its build. When choosing a database or a particular streaming option, the data architect needs to determine the value between:
Having consistent data, where all the reads received are the most recent write, and, if not return an error.
Having highly available data, where all the reads contain the data, but they might not be the most recent.
CAP Theorem
C - Consistency : All reads and receive the most recent write or an error
A - Availability : All reads contain data, but it might not be the most recent
P - Partition Tolerance : The System continues to operate despite network failure.
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