Some use cases in data analysis require near-instantaneous processing of data. To make it happen, you need tools for real-time data analysis that support the use of continuous queries. This technology is called "Streaming analytics" or "Real-Time analytics." Through this article, we’ll help you choose a streaming database, a first step when starting out with real-time analytics.
Streaming analytics prepares and continually measures data as soon as it enters the database. You can immediately draw conclusions or insights as the data enters the system. A streaming analytics database is a datastore that collects, processes, and enriches incoming data streams. It also connects to external data sources, enabling applications to integrate specific data and update external databases with the processed information. The streaming analytics databases are mainly used for:
- Fraud Detection
- Real-time credit scoring that helps financial institutions to decide whether to extend credit
- Targeting individual customers with promotions and incentives in retail outlets
- Customer relationship management that maximizes business results and satisfaction
- Real-time stock trades
How To Pick a Streaming Database?
A streaming data source continuously generates data in high volumes and at high velocity. Some common examples of streaming data include:
- Real-time advertising
- Server and security logs
- IoT sensors
We have end devices that continuously generate thousands or millions of records in the above cases. They form a data stream that can be unstructured or semi-structured. In short, a single streaming source generates massive amounts of data every minute. So, you need to select the best streaming database that can analyze and process large amounts of data.
You must consider these factors while choosing a streaming database.
Type of Data
It is challenging to handle the streaming data as various devices and sources continuously generate it. There are different types of streaming data. For instance, a complicated continuous data stream comes from the Internet of Things (IoT). The data always keeps flowing means it is always on; there is no start and no stop. The traditional batch processing approach can not work with IoT data as it encompasses a variety of data types. For instance, a single turbine can have hundreds of devices and sensors. Each device has a purpose like measuring temperature, oil level, blade pressure, etc. The data type can be pretty different as different manufacturers produce these devices. You need to choose a streaming database that can handle a wide variety of data efficiently and effectively. It must be able to manage and monitor live streaming data.
Apart from IoT, the data can originate from mobile phones and mobile devices such as IPads, Web clickstream, sensors, market data, and transactions. Suppose you do not choose a streaming database that can handle various forms of data. In that case, you will lose the value of your data, resulting in additional costs such as administrative, operational, business risks, reduction in productivity, and inability to make informed decisions.
Amount of Data
Streaming data is flooding into companies from social networks, the web, Cloud, sensors, machines, and devices. It isn't easy to precisely describe the amount of data involved in real-time analytics as different data types exist. The traditional data repositories such as operational databases and data warehouses can not handle voluminous data. Therefore, you need to select a streaming database that can extract, transform and load millions of records in seconds.
Budget
A streaming database works with the data that changes continually. Retailers like eBay and Amazon measure hot products and remove slow-moving products from online storefronts by analyzing customer transaction data streams. Streaming databases can reduce the budget or cost of developing applications by replacing complex microservices.
Speed
Development time and speed of analysis are the main features of streaming databases. They help you analyze the data speedily to handle large volumes of complicated instructions.
Best Streaming Analytics Databases
Below are some of the best streaming analytics databases you can use to process and manage your data.
Amazon Kinesis
Organizations can build streaming applications and open-source Java libraries through Amazon Kinesis. You can easily use it to collect, process, and analyze real-time data for getting timely insights and reacting quickly to new information. Amazon Kinesis allows you to process the streaming data at any scale cost-effectively. It also provides you the flexibility to choose the tools that best suit the requirements of your application. Using this streaming analytics tool, you can ingest real-time data such as audio, video, website clickstreams, application logs, etc. Thus, Amazon Kinesis has the following benefits.
- You can run your streaming applications without managing any infrastructure.
- Instead of hours or days, you can derive insights from data in seconds or minutes.
- You can handle any amount of streaming data with low latencies.
Memgraph
Memgraph is a real-time graph application platform that allows you to do the following.
- Wrangling the streaming data fast with in-memory storage
- Building sophisticated models that you can query in real-time
Memgraph helps you in building and scaling your real-time graph apps faster. You can use your favorite programming language and integrated tools for building your application. Thus, Memgraph offers broad language support built for developer usability. You can also use it to visualize and interact with your graph data in real-time and make smarter decisions about application development. You can easily extract insights and understand your results by executing ad hoc queries in seconds and observing their impact. Further, Memgraph allows you to understand complex relationships and interdependencies between different data points.
An important feature of Memgraph that makes it the fastest among all streaming analytics tools is that it uses random access memory (RAM) as the primary storage for data. Thus, it crunches data in memory and backs it up to disk with transaction logs and periodic snapshots. It does not use a hard drive to store the data.
Apache Storm
Apache Storm is a free and open-source real-time computation system that reliably processes unbounded data streams. It is simple to use and works with all programming languages. Some important features of Apache Storm are:
- Fault-tolerant
- Scalable
- Easy to set up and operate
Apache Storm is specifically built to transform streams of data, unlike Hadoop, which carries out batch processing. Some benefits of using this streaming analytics tool are given below.
- Apache Storm allows real-time stream processing.
- It is fast, robust, flexible, and reliable because it has enormous power to process the data in a short interval.
- It possesses operational intelligence and maintains its performance under increasing load by linearly adding resources.
Apache Kafka
Apache Kafka is an open-source event streaming platform. Thousands of companies use it for streaming analytics, high-performance data pipelines, mission-critical applications, and data integration. You can think of event streaming as the digital version of a human's central nervous system. It is the practice to capture data in real-time in a stream of events from sources like sensors, mobile devices, databases, and software applications.
Apache Kafka combines the following capabilities for event streaming in a highly scalable, fault-tolerant, flexible, and secure manner.
- Reading (subscribe) and writing (publish) to a stream of events
- Storing streams of events durably and reliably for as long as you want
- Processing streams of events when they occur
StreamSQL
StreamSQL allows you to create applications for monitoring networks, manipulating data streams, and real-time compliance. It also accelerates machine learning development by:
- Monitoring and managing features
- Allowing features to be reused and shared
- Generating model features and training sets
StreamSQL offers good feature management that simplifies and accelerates the machine learning process. Features are defined in a central repository with a consistent interface. Further, StreamSQL allows machine learning engineers to think at a high abstraction level. You can combine streams and tables to StreamSQL. Later, they can be transformed and joined using SQL before turning into features. When the data is prepared, StreamSQL handles generating the features for training and serving.
Conclusion
You can pick the best streaming analytics database considering factors like the type of data, amount of data, cost, and speed. Unlike traditional tools, numerous streaming analytics tools monitor and manage the data efficiently. Some of them are:
- Amazon Kinesis - It allows you to get timely insights and handle any amount of real-time data with low latencies.
- Memgraph - It develops applications in days and crunches data in memory. Thus, it is one of the fastest streaming analytics tools. It uses a hard disk to back up data with log files and periodic snapshots.
- Apache Storm - It is scalable, fault-tolerant, and allows real-time stream processing.
- Apache Kafka - It captures data in real-time in the form of a stream of events in a secure manner.
- Stream SQL - It accelerates machine learning development and simplifies the training process.
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