Real-time data streaming has become one of the organizations’ most popular requirements for data analytics. It refers to publishing data at a target as soon as it is collected from the source.
It eliminates the slow processing of data in batches and churns it continuously to invoke an image stream.
Why is Real-Time Data Streaming Useful?
The ongoing streams of business cannot wait for batch-processed data. They need real-time data to assist services such as fraud detection, stock market platforms, rideshare apps, and e-commerce websites.
When data streaming is integrated, it becomes easy to gather, process, filter, analyze and react to data on the spot.
Real-time data streaming brings about better use cases. For example, improved Netflix recommendations or a better online shopping process.
Benefits of Using Real-Time Data Streaming Tools
Now, let’s proceed to check the top benefits of real-time data streaming tools in 2022.
1. KPI Visualization
Executives and other managerial heads are required to make efficient decisions that boost the growth of businesses.
Creating sales networks and the derived data makes decision-making more analytical and sophisticated. Organizations use KPIs to monitor performance and customer reactions.
These are quantifiable indicators that need to be observed in real-time so businesses can understand how things are going at a given time.
Visualizing KPIs in real-time makes it easier to identify focus areas that need immediate changes or improvements for better efficiency and growth.
2. Improved Market Competency
KPI visualizations lead to more effective decision-making from executives and other managerial leads. Real-time data acquired allows higher-level staff to monitor ground-level progress and productivity.
It also lets organizations view industry-standard benchmarks and make internal changes in costs, operations, and deliverables. Real-time data improves the competitiveness of an organization and its performance in the market.
3. Customer Profiling
Customers have a dynamic nature that makes them respond differently depending on market conditions.
Real-time data helps businesses to analyze what drives a customer to make a purchase, understand their spending habits, and even their preferences.
With customer profiling, organizations can tailor their products and services with trigger points that will increase the likelihood of higher customer engagement.
4. Improve Business Processes
In a competitive market, all businesses must keep up quickly and efficiently to attract high traction. Real-time data streaming can allow organizations to identify areas where they may fall behind, need new changes, etc.
It allows businesses to optimize their processes in a way that will bring about maximum efficiency and growth.
Examples of Real-Time Data Processing
- Bank ATM systems
- Traffic control systems
- Online shopping networks
Top 8 Real-Time Data Streaming Tools
Real-time data streaming tools allow enterprises to improve and scale their operations. They enable businesses to identify their shortfalls and take care of distinct needs.
Here is a look at the eight best data streaming tools you can check out and incorporate as a part of business operations.
1. Google Cloud DataFlow
Cloud DataFlow allows you to filter ineffectual data, which may cause the streaming analytics process to slow down. It is a tool that uses Python SDK and Python 3 for supporting real-time data streams.
The data streaming tool also uses Apache Beam with Python to clearly define data pipelines that ensure data from different IoT services and other sources is extracted, transformed, and analyzed.
- Resource auto-scaling and rebalancing of work.
- Flexible pricing and scheduling plans for batch data processing.
- Real-time and ready-to-use AI patterns.
2. Apache Kafka
Apache Kafta is one of the leading and highly-demanded data streaming tools which is developed in the year 2011. It can handle high data volumes, thus allowing enterprises to use Kafka to manage peak data ingestion loads and big data messages.
The general Apache Kafka application works in the back end for integrating other third-party services. Its infrastructure allows easy integration of other real-time streaming platforms and conveniently provides stream processing and analytics.
Key benefits of Apache Kafka:
- Highly reliable functioning.
- Reduces the requirement of multiple integrations.
- Provides high throughput.
3. Amazon Kinesis
This distinguished real-time data streaming tool allows enterprises to use open-source Java libraries and SQL editor integrations. Kinesis conveniently takes after areas like scaling, running applications, and other crucial responsibilities.
It helps reduce the complexities and costs enterprises must undertake for real-time data processing. At the same time, the flexible nature of Kinesis allows enterprises to support anything from basic report analytics to machine learning algorithms.
- Entirely managed
- Doesn’t require any infrastructure management.
- Real-time processing.
4. Azure Stream Analytics
Azure Stream Analytics is one of the most prominent real-time data streaming environment names. It focuses on critical end-to-end analytics and can provide faster results in a limited time with the help of C#, JavaScrip, and SQL.
Moreover, it holds machine learning abilities that allow easier identification of spikes, adverse trends, dips, etc. This means that users can interpret data visualizations conveniently.
- End-to-end analytics.
- Rapid scalability.
- Hybrid architectures for stream processing.
- Enterprise level reliability.
5. Stream SQL
Stream SQL is one of the most simplified real-time data streaming tools suitable for non-developers.
It allows you to conveniently develop applications for data manipulation, surveillance, and monitoring real-time compliances.
Its advantages include:
- High speeds, ease of use
- Faster responses
- Conveniences in data science jobs.
6. Apache NIFI
Another prominent tool for data streaming, Apache NIFI, is known for its integrated data logistic features. It can also track real-time data and ensure the movement of data.
These features make NIFI an ideal tool for automating data movement between a source and its destination. It also can support distributed sources like videos, social feeds, files, and more.
- Web-based interface.
- High configuration abilities.
- Highly secure.
- Data tracking at all sources.
7. IBM Stream Analytics
IBM Stream Analytics is a real-time data streaming tool that provides an Integrated Development Environment (IDE) and also supports Java, Scala, and Python languages.
Its development abilities make it easier for developers to monitor and manage data and make informed decisions.
- Faster delivery of streams.
- Voice data insights.
- IoT analytics.
8. Spark Streaming
Spark Streaming is an entry-level data streaming tool that allows you to integrate it with Apache Spark directly.
It has become a popular tool for real-time analytics and provides exceptional support for historical and merging stream data.
Moreover, it is open-source, allowing the community to contribute towards reimplementation with improved support for Java, Python, and Scala languages.
- Easy to use.
- Cost-effective and low latency.
- Unified Batch and streaming processes.
Real-time data streaming has gained popularity for its convenience. It is a cost-effective and reliable process that can help enterprises analyze operations deeply and influence decisions.
Data streaming tools are a convenient way to process and analyze data in real-time. It allows enterprises to unlock new potentials that weren’t available otherwise. Each open platform has unique capabilities that are advantageous to enterprises.
At the same time, they can optimize their operations to integrate these tools and achieve informed decision-making and higher competence.
Hence, it is necessary to understand the different tools available in the market and pick the best one as per your needs.