Databricks and Amazon MSK serve different categories, with Databricks leading in data analytics and machine learning, and Amazon MSK leading in data streaming with Apache Kafka integration. Databricks might have an advantage in environments focusing on advanced analytics and machine learning capabilities, whereas Amazon MSK offers superior real-time data processing services.
Features: Databricks provides an integrated environment that supports multiple programming languages, collaborative notebooks, and machine learning integrations. It includes built-in optimization for data processing and interactive querying. Amazon MSK simplifies real-time stream management with a fully managed Kafka service, reducing administrative tasks and offering seamless integration with other AWS services.
Room for Improvement: For Databricks, improvements could be made in simplifying administrative setup, expanding multi-cloud compatibility, and enhancing cost transparency in data processing. For Amazon MSK, enhancements might include broadening support for custom integrations outside of AWS, improving user interfaces for complex setups, and optimizing latency for large-scale streaming environments.
Ease of Deployment and Customer Service: Databricks offers deployment across various cloud environments and is praised for effective customer support. In comparison, Amazon MSK offers tight integration within the AWS ecosystem but may face challenges in multi-cloud deployments despite benefiting AWS users through streamlined service integration.
Pricing and ROI: Databricks employs flexible pricing models catering to diverse business needs, which can result in high ROI from data insights. Amazon MSK's pay-as-you-go pricing appeals to existing AWS customers, with cost efficiency tied to data throughput, offering a budget-friendly option for streaming services. Both solutions offer competitive pricing, with Databricks potentially requiring higher initial investment but yielding substantial returns through efficient data strategies, whereas Amazon MSK provides an economical choice within the AWS ecosystem.
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.
Amazon's support is excellent.
Whenever we reach out, they respond promptly.
The functionality for scaling comes out of the box and is very effective.
The patches have sometimes caused issues leading to our jobs being paused for about six hours.
They release patches that sometimes break our code.
Cluster failure is one of the biggest weaknesses I notice in our Databricks.
The increase in cloud costs by 50% to 60% does not justify the savings.
It would be beneficial to have utilities where code snippets are readily available.
Adjusting features like worker nodes and node utilization during cluster creation could mitigate these failures.
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.
Once we started using Kafka, our cloud costs rose by 50% to 60%.
The scalability and usability are quite remarkable.
Databricks' capability to process data in parallel enhances data processing speed.
The notebooks and the ability to share them with collaborators are valuable, as multiple developers can use a single cluster.
Amazon Managed Streaming for Apache Kafka (Amazon MSK) is a fully managed service that enables you to build and run applications that use Apache Kafka to process streaming data. Amazon MSK provides the control-plane operations, such as those for creating, updating, and deleting clusters.
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.
