Confluent vs Elastic Search comparison

Confluent is an enterprise-ready, full-scale streaming platform that enhances Apache Kafka. 

Confluent has integrated cutting-edge features that are designed to enhance these tasks: 

• Speed up application development and connectivity
• Enable transformations through stream processing
• Streamline business operations at scale
• Adhere to strict architectural standards

Confluent is a more complete distribution of Kafka in that it enhances the integration possibilities of Kafka by introducing tools for managing and optimizing Kafka clusters while providing methods for making sure the streams are secure. Confluent supports publish-and-subscribe as well as the storing and processing of data within the streams. Kafka is easier to operate and build thanks to Confluent.

Confluent's software is available in three different varieties: 

1. A free, open-source streaming platform that makes it simple to start using real-time data streams
2. An enterprise-grade version of the product with more administrative, ops, and monitoring tools
3. A premium cloud-based version.

Confluent Advantage Features

Confluent has many valuable key features. Some of the most useful ones include:

• Multi-language
  
  
  • Clients: C++, Python, Go, and .NET
  • REST proxy: Can connect to Kafka from any connected network device
  • Admin REST APIs: RESTful interface for performing administrator operations

• Pre-built ecosystem
  
  
  • Connectors: More than 100 supported connectors, including S3, Elastic, HDFS, JDBC
  • MQTT proxy: Gain access to Kafka from MQTT gateways and devices
  • Schema registry: Centralized database to guarantee data compatibility

• Streaming database
  
  
  • ksqlDB: Materialized views and real-time stream processing

• GUI management 
  
  
  • Control panel: GUI for scalable Kafka management and monitoring
  • Health+: Smart alerts and cloud-based control centers

• DevOps automation that is flexible
  
  
  • Confluent for Kubernetes: Complete API to deploy on Kubernetes
  • Automated Ansible deployment on non-containerized environments

• Dynamic performance 
  
  
  • Self-balancing clusters: Automated partition re-balancing across brokers in the cluster
  • Tiered storage: Older Kafka data offloading to object storage with transparent access

• Security that is enterprise-grade 
  
  
  • Role-based access control: Granular user/group access authorization
  • Audit logs that are structured: Logs of user actions kept in dedicated Kafka topics
  • Secret protection: Sensitive information is encrypted

• Global resilience
  
  
  • Linking clusters: A real-time, highly reliable, and consistent bridge across on-premises and cloud environments
  • Multiple-region clusters: Single Kafka cluster with automated client failover distributed across multiple data centers
  • Replicator: Asynchronous replication that is based on the Kafka Connect framework

• Support
  
  
  • Round the clock enterprise support from Kafka experts

Reviews from Real Users

Confluent stands out among its competitors for a number of reasons. Two major ones are its robust enterprise support and its open source option. PeerSpot users take note of the advantages of these features in their reviews: 

Ravi B., a solutions architect at a tech services company, writes of the solution, “KSQL is a valuable feature, as is the Kafka Connect framework for connecting to the various source systems where you need not write the code. We get great support from Confluent because we're using the enterprise version and whenever there's a problem, they support us with fine-tuning and finding the root cause.”

Amit S., an IT consultant, notes, “The biggest benefit is that it is open source. You have the flexibility of opting or not opting for enterprise support, even though the tool itself is open source.” He adds, “The second benefit is it's very modern and built on Java and Scala. You can extend the features very well, and it doesn't take a lot of effort to do so.”

Elasticsearch is a prominent open-source search and analytics engine known for its scalability, reliability, and straightforward management. It's a favored choice among enterprises for real-time data search, analysis, and visualization. Open-source Elasticsearch is free, offering a comprehensive feature set and scalability. It allows full control over deployments but requires managing and maintaining the infrastructure. On the other hand, Elastic Cloud provides a managed service with features like automated provisioning, high availability, security, and global reach.

Elasticsearch excels in handling time-sensitive data and complex search requirements across large datasets. Its scalability allows it to handle growing data volumes efficiently, maintaining high performance and fast response times. Integrated with Kibana, Elasticsearch enables powerful data visualization, providing real-time insights crucial for data-driven decision-making.

Elastic Cloud reduces operational overhead and improves scalability and performance, though it comes with associated costs. It is available on your preferred cloud provider — AWS, Azure, or Google Cloud. Customers who want to manage the software themselves, whether on public, private, or hybrid cloud, can download the Elastic Stack.

At its core, Elasticsearch is renowned for its full-text search capabilities, capable of performing complex queries and supporting features like fuzzy matching and auto-complete.

Peer reviews from various professionals highlight its strengths and weaknesses. Pros include its detection and correlation features, flexibility, cloud-readiness, extensibility, and efficient search capabilities. However, users have noted challenges like steep learning curves, data analysis limitations, and integration complexities. The platform is generally viewed as stable and scalable, with varying degrees of satisfaction regarding its usability and feature set.

In summary, Elasticsearch stands out for its high-speed search, scalability, and versatile analytics, making it a go-to solution for organizations managing large datasets. Its adaptability to different enterprise needs, robust community support, and continuous development keep it at the forefront of enterprise search and analytics solutions. However, potential users should be aware of its learning curve and the need for skilled personnel for optimization.