In today’s digitized world, everyone expects their data the second it is updated. Moreover, large-scale companies depend on this data to predict the supply and demand of their product in the market and anticipate user behavior for a better experience. Real-time data streaming has become an integral part of modern business strategy.
To stay ahead of the competition, businesses need to leverage the opportunities provided by data streaming platforms to make insightful decisions.
But, what do we mean by data streaming, and what are these tools that help with data streaming? What are the benefits and limitations of data streaming platforms?
To help you out, this blog will cover all that you need to know about data streaming tools. Without further ado, let’s get started.
An Overview of Data Streaming
Streaming data refers to the continuously generated data by numerous data sources. By using data streaming platforms, data can be stored, processed, analyzed, and used to derive valuable insights.
A data stream includes data like log files produced by users through eCommerce purchases, social media information, telemetry from connected devices, financial trading, in-game activity, or data centers.
This data needs to be processed consecutively and incrementally on either a sliding time window or over a record-by-record basis to be used for sampling, correlations, filtering, and aggregations. The analytics retrieved from the data give businesses visibility into different aspects such as server activity, users’ and products’ geo-location, service usage for billing, etc. It enables businesses to respond to upcoming situations quickly.
How Does Data Streaming Work?
As we have discussed before, businesses have multiple data sources that get channeled to different destinations. However, to process the data you can use data stream processing techniques like event-stream processing which includes small data chunks.
Streaming data enables data chunks to be processed in near real-time or real-time. Moreover, to be able to provide real-time data, streaming platforms should offer low latency, high scalability, and high availability. Real-time analytics and streaming media are the most common use cases of data streaming.
Although initially data stream was introduced for stock exchange values and media streaming, it is now being adopted by different businesses to process data in near real-time while allowing them to monitor and analyze different aspects of the business.
Benefits of Data Streaming Platform
The core aim and benefit of a data streaming platform is the ‘real-time insights’. But there’s much more that adds to the advantages a data streaming platform offers to its users. Some of them are:
Increased Customer Satisfaction
With data streaming, it becomes easier for a business to get insights into user behavior, and their likes as well as dislikes. Using these insights, an organization can make their online web application more engaging that converts the user into their customer with a custom-made user experience.
Better Return on Investment (ROI)
The capability to gather, analyze, and act on the data using data streaming gives a business a competitive advantage in the market. Real-time analytics makes a business more adaptive and responsive to customer needs, market trends, and opportunities, leading to an increase in ROI.
Reduced Infrastructure Cost
Large volumes of data are often stored in data warehouses in the traditional data processing. Organizations often have a hard time affording these storage systems and hardware. Since data stream processing doesn't store large amounts of data, processing systems have lower hardware costs.
Minimized Losses
With real-time data intelligence that comes along with data streaming, businesses can get warnings on upcoming problems in the application system including but not limited to financial downturns, system outages, and data breaches that can create a negative impact on the business. Using this insight, businesses can prevent the effect of such events.
Data Streaming Challenges
Data streaming systems have to deal with real-time, highly volatile, and continuous data which is often incomplete and heterogeneous that can pose several potential problems. Some of the challenges that data streaming platforms might face are as follows:
Due to huge data volumes, data streaming platforms may have to deal with damaged data packets.
If the data streaming system isn’t fast enough, the relevance of streaming data will reduce over time.
It is important for data streaming systems to provide high elasticity otherwise they will not be able to allocate resources according to the data volume leading to system failure or high latency.
Some data streaming platforms are not fault-tolerant, which can lead to downtime in data transmission from source to destination or analyzing real-time data that’s not good for a business.
Data in a streaming system may either come from the same or different sources. But, the data has to move through a distributed system leading to problems in the system like calling its data and transmitting it to the user.
Top 5 Data Streaming Tools
Now that we know about the data streaming platforms, their advantages, and their challenges, it is time to look at the top 5 data streaming tools that you should know about before working with a tremendous volume of data.
1. Google Cloud Dataflow
Google Cloud Dataflow is a fully-managed cloud-based streaming platform that is equipped with Python 3. It allows one to extract data from edge sources, define data pipelines, and process streams.
With Google Cloud Dataflow, developers can automate provisioning, and processing resource management. It also provides horizontal autoscaling of worker resources to maximize utilization. Besides, the processing of Dataflow is both consistent and reliable.
In simple terms, Dataflow allows a fast, simplified, low-latency data streaming pipeline, and removes operational overhead from the workload so that developers can focus on programming rather than managing server clusters.
Some of the key features of Google Cloud Dataflow include:
- Flexible pricing and scheduling for batch processing
- Resources’ autoscaling
- Dynamic rebalancing of work
- Smart diagnostics
- Streaming engine
- Flexible resource scheduling
- Real-time change data capture
Besides, the billing in Google Dataflow is based on batch or streaming use per second.
2. Azure Stream Analytics
Azure stream analytics is an easy-to-use, real-time analytics platform designed for workloads that are mission-critical. It is an end-to-end pipeline that can be ready for production within a few minutes with extensible C# and JavaScript code and SQL syntax.
Being hybrid architecture, it helps businesses to run queries in both edge and cloud. The stream analytics of Azure comes with elastic capacity and scalability making robust streaming data pipelines that analyze millions of events in a fraction of the time.
3. Amazon Kinesis
With Amazon Kinesis, it becomes easier to gather, process, and analyze streaming data like audio, video, IoT telemetry, and application logs in real-time for analytics or machine learning purposes. It helps you get quick insights and the ability to act accordingly. Kinesis is cost-effective regardless of the scale of your business and provides you with the flexibility to choose the ideal tools for your project.
Kinesis is a fully managed tool that runs the streaming app without external infrastructure management. Moreover, it can handle large volumes of data from a plethora of sources with minimal latencies.
4. Apache Kafka
Apache Kafka is an open-source, distributed event streaming platform that is written in Scala and Java by Apache Software Foundation. Kafka aims to provide a high-throughput, unified, low-latency platform to manage data feeds in real-time.
Apache Kafka is used to develop real-time streaming applications and data pipelines that process and transfer data from source to destination while consuming data streams.
What makes Kafka different from other streaming platforms is its ability to store data streams in a fault-tolerant, distributed, and durable cluster that can be stretched or scaled easily over availability zones to provide higher availability.
5. IBM Stream Analytics
IBM Stream analytics assesses a wide range of streaming data to help businesses find the risks and opportunities to make real-time and impactful decisions.
With IBM Stream Analytics, developers can easily connect with any data sources and integrate with Spark or Hadoop. It also comes with an in-built for natural language, machine learning, text, acoustics, etc to help you create adaptive applications.
Using existing Python code for stream apps, Stream Analytics by IBM offers faster delivery of streaming applications. Besides, its speech-to-text toolkit allows you to build a web application that can store voice, convert it into text, and perform natural language processing on it to unlock voice insights with ease.
It has a rich visual IDE that allows you to develop applications using popular programming languages like Python, Java, and Scala easily.
Conclusion
So that was it for data streaming platforms. We hope you have found the blog insightful to help you understand the basic concepts as well as the data streaming tools that you can go for.
To conclude, data streaming refers to the communication between a sender and a receiver through either single or multiple streams of data. They also store, process, and analyze data to be used by businesses. And yes, data streaming also has its pros and cons like any other technology in the marketplace.
Although there are a multitude of data streaming tools that you might have come in touch with. The top data streaming tools that you can choose from are Google Cloud Dataflow, Azure Stream Analytics, Amazon Kinesis, Apache Kafka, and IBM Stream Analytics.
Source: Decipher Zone
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