Databricks and Apache Kafka are prominent players in the data processing and messaging domain. Databricks has an edge in data analytics and machine learning due to its integrated features, while Kafka leads in real-time messaging systems with its robust processing capabilities.
Features: Databricks stands out for its ease in handling large-scale analytics, built-in optimization that accelerates query speeds, and seamless integration with various programming languages and machine learning libraries. Apache Kafka is recognized for its powerful distributed messaging system, high throughput, and excellent real-time data processing capabilities, ensuring reliability and horizontal scalability for managing large data volumes.
Room for Improvement: Databricks could improve its visualization tools, error messaging, and integration with BI tools like PowerBI or Tableau. Enhancements in cost transparency and predictive analysis features are also desirable. Apache Kafka faces challenges with ZooKeeper dependency, setup complexity for secure environments, and GUI tool enhancements for management. Better monitoring tools and integrations with external systems are also needed.
Ease of Deployment and Customer Service: Databricks offers flexible deployment on public and hybrid clouds with generally helpful support, though there can be language hurdles. On the other hand, Kafka’s deployment is more suited to on-premises environments, requiring technical expertise for large-scale management. While both provide quality support, Databricks is noted for its proactive service, unlike Kafka, which requires specialized knowledge.
Pricing and ROI: Databricks is considered expensive but justifiable for its features that deliver good ROI when used effectively. Pricing depends on usage, offering flexibility but potentially increasing quickly. Apache Kafka, being open-source, does not involve direct licensing costs, making it cost-effective. However, setup and maintenance costs can accumulate, especially with cloud-based enhancements like Confluent, affecting its ROI.
When it comes to big data processing, I prefer Databricks over other solutions.
For a lot of different tasks, including machine learning, it is a nice solution.
The Apache community provides support for the open-source version.
Whenever we reach out, they respond promptly.
Customers have not faced issues with user growth or data streaming needs.
The patches have sometimes caused issues leading to our jobs being paused for about six hours.
Apache Kafka is stable.
They release patches that sometimes break our code.
Cluster failure is one of the biggest weaknesses I notice in our Databricks.
The performance angle is critical, and while it works in milliseconds, the goal is to move towards microseconds.
We are always trying to find the best configs, which is a challenge.
We prefer using a small to mid-sized cluster for many jobs to keep costs low, but this sometimes doesn't support our operations properly.
We use MLflow for managing MLOps, however, further improvement would be beneficial, especially for large language models and related tools.
If I could right-click to copy absolute paths or to read files directly into a data frame, it would standardize and simplify the process.
The open-source version of Apache Kafka results in minimal costs, mainly linked to accessing documentation and limited support.
It allows the use of data in motion, allowing data to propagate from one source to another while it is in motion.
Databricks' capability to process data in parallel enhances data processing speed.
Developers can share their notebooks. Git and Azure DevOps integration on the Databricks side is also very helpful.
Apache Kafka is an open-source distributed streaming platform that serves as a central hub for handling real-time data streams. It allows efficient publishing, subscribing, and processing of data from various sources like applications, servers, and sensors.
Kafka's core benefits include high scalability for big data pipelines, fault tolerance ensuring continuous operation despite node failures, low latency for real-time applications, and decoupling of data producers from consumers.
Key features include topics for organizing data streams, producers for publishing data, consumers for subscribing to data, brokers for managing clusters, and connectors for easy integration with various data sources.
Large organizations use Kafka for real-time analytics, log aggregation, fraud detection, IoT data processing, and facilitating communication between microservices.
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?
What are the benefits or ROI to look for in Databricks reviews?
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.
We monitor all Streaming Analytics reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
