What is Confluent Kafka?
Confluent is a popular streaming technology based on Apache Kafka has launched Confluent platform version 4.1 that includes the general availability of KSQL and an open source SQL engine of Apache Kafka. Through K-SQL developers can develop any real-time stream-based applications in an easier and known way. The syntax of KSQL is much familiar to the developers. This release will keep the process of reading, writing, and processing data easier in a real-time manner. KSQL has been fully integrated with the confluent platform and the companies will experience the world-class support through the Company itself.
This article discusses the features of Confluent SQL and its technical specifications. The platform enhances the overall usability and scalability of the enterprises across other platforms and that is why Confluent is so much popular.
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Features of Confluent KSQL
KSQL is generally available with Confluent but apart from this there are many other features that are provided by the Company to make the platform enterprise suitable and following listed capabilities of Confluent makes it a popular and most used platform worldwide:
- Confluent Control Centre: Confluent control center expands the capabilities of management as it adds the search functionality, multi-cluster support, and the secure environment. Administrators can enable the read-only role support to restrict the access to topic management, they can also provide control center only monitoring access. In short, we can say that now administrators can control and manage the database access in a great manner.
- Extensive Client Support: Confluent can now support many popular platform clients. Newly added functionalities can support Schema Registry to the .NET client and it can also provide support to the C, Python, NET, and Go client for message headers.
- Kafka’s Stream API: Confluent SQL has new and improved resilience and operability features by adding a new unit testing kit that can verify the streaming application correctness.
- Kafka Connect API: It adds JMS connector that allows seamless data migration and it can be easily migrated from Active MQ and IBM MQ to Apache Kafka.
- Data Replication to multicentre: Businesses can replicate from on-premises to the cloud easily. It has also introduced streamlined configuration and deployment that can simplify disaster recovery operations efficiently.
Now developers can use a familiar and powerful SQL interface by this general KSQL. All kind of stream processing can be done by the KSQL that include frauds detection, real-time monitoring, and streaming ETL and anomaly detection.
As Per the Statement Of Confluent CTO
Neha Narkhede, CTO and the co-founder of Confluent said that KSQL offers potential use cases that include data processing or ETL, extraction, transformation, and loading of data to the organizations. Data can be easily transferred between various systems or data warehouses. She added that KSQL is an interactive distributed SQL engine that is suitable for Big Data Hadoop. All sorts of continuous stream processing can be done by it.
Traditionally developers have to write Java or Python code for data streaming, while KSQL has provided a stream processing fold to the SQL experts and data developers. It can be accessed through GitHub. She told that KSQL is similar to ANSI SQL but is not entirely compliant with it. KSQL is basically a customized version of SQL that can query data streams of the database and can even query the past transactions.
Narkhede said that now the database is turning and rather than querying past databases. it should be able to query the future database and more features will be added in the future. Like insert statements will be introduced in Kafka in coming days. She said that the ability of distributed processing makes KSQL different from other SQL tools like Striim, Kinetica and SQL Stream.
The query can be easily distributed over the partitioning model and clusters can process it. They can be processed and form even normal sized boxes and couples them. They can be integrated with basic building blocks of Kafka and can run several queries in parallel. Load balancing can also be handled through Kafka.
As KSQL of Confluent is an open source data streaming engine and can query infinite data streams. So, here are a few of its applications:
1). Real-Time monitoring
Business metrics that are computed in the real-time can be monitored through KSQL. Through this tool, you can also check the correctness of your application whether it is performing as per your requirements or not. So far counters and gauges have been used for measurement or performance statistics. It can inform you about CPU load but not about the performance of your application.
2). Anomaly Detection and Security Checks
Kafka Security use cases often seem just like analytics and monitoring and do not detect fraud patterns, or patterns of any spam, abuse, intrusion or any other bad behavior of the application. KSQL can define and query the patterns in the real-time to show the result.
3). A batch of Online Data Integration
So far from ages, ETL has been processed in batches that include the steps like the dumping of raw data in real-time and then transforming and querying it in real-time. In many cases, this complete process delays the expected results; KSQL and Kafka connectors can now complete this full cycle in the real-time.
Real-Time Clickstream Anomaly & Analytics by KSQL In Action
KSQL can be used for real-time monitoring, alerting, and anomaly detection. The real-time log analytics that can be recorded by clickstream can be in many forms. Here, we can flag even the user sessions that are consuming much bandwidth of the web servers. As sessions can help in analyzing user behavior, so many types of analysis can be done with such metrics or statistics. Right from the simple to complex metrics e.g. count of user visits to any site or customer conversion funnels or event flows can be easily identified.
Several new features will be added in KSQL in the nearby future so that is can support richer SQL grammar and help in developing quality, stable, and operable applications. Further aggregation functions like continuous tables, point-in-time SELECT operations can also be selected through KSQL.