Confluent vs Databricks 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.”

Databricks is utilized for advanced analytics, big data processing, machine learning models, ETL operations, data engineering, streaming analytics, and integrating multiple data sources.

Organizations leverage Databricks for predictive analysis, data pipelines, data science, and unifying data architectures. It is also used for consulting projects, financial reporting, and creating APIs. Industries like insurance, retail, manufacturing, and pharmaceuticals use Databricks for data management and analytics due to its user-friendly interface, built-in machine learning libraries, support for multiple programming languages, scalability, and fast processing.

What are the key features of Databricks?

• User-friendly interface: Simplifies operations and usability.
• Built-in machine learning libraries: Facilitates machine learning tasks.
• Support for multiple programming languages: Enhances flexibility.
• Scalability: Efficiently handles growing data needs.
• Fast processing: Improves performance and speed.
• Automated optimization: Reduces manual efforts.
• Data visualization: Provides insightful visuals.
• Collaborative features: Enhances teamwork.
• Delta Lake performance: Boosts data management.
• Seamless cluster management: Simplifies system operations.

What are the benefits or ROI to look for in Databricks reviews?

• Efficiency in handling large datasets: Ensures smooth processing.
• Interactive workspace environment: Improves user collaboration.
• Integration with platforms: Provides connectivity benefits.
• Performance optimization: Enhances overall system performance.
• Support for data governance and security: Ensures data integrity and protection.

Databricks is implemented in insurance for risk analysis and claims processing; in retail for customer analytics and inventory management; in manufacturing for predictive maintenance and supply chain optimization; and in pharmaceuticals for drug discovery and patient data analysis. Users value its scalability, machine learning support, collaboration tools, and Delta Lake performance but seek improvements in visualization, pricing, and integration with BI tools.