What Is Real-Time Analytics?
Real-time analytics turns data into insights immediately after it’s collected. These kinds of insights are used when time is of the essence. Otherwise known as operational intelligence, real-time analytics can predict when a device is about to fail, warning your operations team before it happens. Prompt retailers to send mobile promotions to customers when they’re in a store vicinity. Or detect credit card fraud before a transaction is completed.
To better understand how real-time analytics works, let’s compare it to traditional analytics, or batch processing. With the traditional approach, limited sets of historical data are stored and indexed. When business users need insights, they query the system. Batch processing is typically used for routine tasks like generating monthly sales reports or running payroll.
Here are the top 10 real-time analytics tools, in no particular order:
- Apache Kafka: A distributed streaming platform that allows for real-time data processing and analysis.
- Apache Flink: An open-source stream processing framework for big data analytics.
- Apache NiFi: A data integration platform that supports real-time data streaming and processing.
- StreamSets: A data operations platform that provides real-time data integration and analytics.
- Google Cloud Dataflow: A fully-managed service for stream and batch data processing and analysis.
- Spark Streaming: A scalable and fault-tolerant stream processing framework that integrates with Apache Spark.
- IBM Streams: A real-time stream processing platform for analyzing and acting on streaming data.
- Microsoft Azure Stream Analytics: A cloud-based service for real-time analytics on streaming data.
- Apache Storm: A distributed real-time computation system for processing streaming data.
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1. Apache Kafka
Apache Kafka is also a leading mention among real-time data streaming tools. Enterprises can use Apache Kafka for the management of peak data ingestion loads and also as a big data message bus. The capability of Apache Kafka to manage peak data ingestion loads is a unique and formidable advantage over common storage engines.
The general application of Kafka is in the back end for the integration of microservices. In addition, it can also support other real-time data streaming portals such as Flink or Spark. Interestingly, the majority of real-time data streaming platforms can integrate effectively with Kafka to provide stream analytics and stream processing.
Kafka can also send data to other platforms for streaming analytics for the purpose of analysis. Since Kafka is a comparatively new technology than others, users can find it a little bit difficult to operate. However, the features of data redundancy and fault tolerance offered a credible boost to the reputation of Kafka, among other tools used for data streaming.
2. Apache Flink
The final entrant among the top real-time data streaming tools is Apache Flink. You can think of it as a hybrid of Storm and Spark. Even if Spark provides a batch framework, it does not have any support for real-time streaming like Spark Streaming. Flink overcomes such issues by providing frameworks for batch processing.
As a result, Flink can depict lower latency while also exhibiting the data fault tolerance of Spark. The different windows and redundant settings support user configuration. In addition, Flink also implements Apache Beam, which is the contribution of Google to enable real-time processing.
3. Apache NIFI
In simple words, we can say that Apache NiFi is a data ingestion tool. It is an integrated data logistics platform for automating the movement of data between disparate systems. It provides real-time control that makes it easy to manage data movement between any source and any destination. Apache NiFi supports disparate and distributed sources of differing formats, schemas, protocols, speeds, and sizes, such as machines, geolocation devices, clickstreams, files, social feeds, log files and videos, and more. It is configurable plumbing for moving data around, similar to how FedEx, UPS, or other courier delivery services move parcels around. Apache NiFi also allows us to trace our data in real-time, just like we could trace a delivery.
StreamSets is also a data ingestion tool similar to NIFI. Basically, StreamSets is a data operations platform where we can efficiently develop batch and streaming dataflows, further, operate them with full visibility and control, and easily evolve our architecture over time.
5. Google Cloud DataFlow
The first entry among real-time analytics tools is Google Cloud DataFlow. Google recently excluded Python 2 and powered Cloud DataFlow with Python SDK and Python 3 for supporting data streaming. The use of streaming analytics in Google Cloud DataFlow helps in filtering ineffectual data that can slow down the speed of analytics. In addition, users can also utilize Apache Beam with Python for defining data pipelines to ensure extraction, transformation, and analysis of data from different IoT devices and additional data sources.
6. Spark Streaming
Spark Streaming is the next important entry among tools for open-source real-time analytics. The stream analytics service has direct integration with the Apache Spark platform and is one of the most popular open-source real-time data streaming tools. One of the previous versions of Spark Streaming executed streaming processing with the help of a micro-batch process.
However, the streaming framework faced certain performance challenges despite the faster execution of batch jobs. So, the Spark open-source community, which is constantly expanding, contributed to a reimplementation of Spark Streaming. The reimplementation has been helpful in improving performance and lowering latency. Spark Streaming supports Java, Python, and Scala programming languages and provides access to different components of Apache Spark. Furthermore, Spark Streaming also provides exceptional support for merging streaming data and historical data.
7. IBM Streams
IBM Stream Analytics also deserves better recognition than tools for open-source real-time analytics. It provides Eclipse-based IDE (Integrated Development Environment) alongside support for Java, Python, and Scala programming languages. As a result, it makes it easier to develop real-time data analytics applications.
The capabilities of IBM stream analytics are different than other common real-time data streaming tools. It helps in developing notebooks so that Python users can ensure effortless monitoring, management, and informed decision-making. You can use the streaming services of IBM Streaming Analytics on the IBM BlueMix platform for processing the information in data streams.
8. Azure Stream Analytics
The in-built machine learning capabilities of Azure Stream Analytics also provide adequate support for intuitive data processing. The machine learning capabilities also help in the easier identification of spikes and dips, slow positive and negative trends, and outliers pertaining to streamed data. As a result, users could easily interpret the output visualizations. Based on these advantages, Azure Stream Analytics establishes its reputation among various popular data streaming tools.
9. Apache Storm
Apache Storm is the next popular mention among the top open-source data streaming tools. Storm is an ideal tool for real-time data analytics. Built by Twitter, Apache Storm specifically aims at the transformation of data streams. This is a considerable difference from Hadoop which is one of the top Big Data tools, which relies on batch processing.
On the other hand, the applications of Apache Storm are also useful for ETL, online machine learning, and many others. The foremost capability of Apache Stream is faster data processing. Apache Stream can carry out processes at the nodes with faster data processing than its competitors. Most important of all, you can integrate Apache Storm with Hadoop to improve its capability for larger throughputs.
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