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Databricks Data Intelligence Platform vs. IBM Confluent

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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    Databricks Data Intelligence Platform

    Score9 out of 10
    N/ADatabricks offers the Databricks Lakehouse Platform (formerly the Unified Analytics Platform), a data science platform and Apache Spark cluster manager. The Databricks Unified Data Service provides a platform for data pipelines, data lakes, and data platforms.

    $0.07

    Per DBU

    IBM Confluent

    Score9.1 out of 10
    N/AIBM Confluent helps enterprises stream, connect, process, govern and serve real-time data across hybrid environments.

    $385

    per month

    Pricing
    Databricks Data Intelligence PlatformIBM Confluent
    Editions & Modules
    Standard
    $0.07
    Per DBU
    Premium
    $0.10
    Per DBU
    Enterprise
    $0.13
    Per DBU
    Basic
    $0
    Standard
    Starting at ~$385
    per month
    Enterprise
    Starting at ~$1,150
    per month
    Offerings
    Pricing Offerings
    Databricks Data Intelligence PlatformIBM Confluent
    Free Trial
    NoNo
    Free/Freemium Version
    NoYes
    Premium Consulting/Integration Services
    NoNo
    Entry-level Setup FeeNo setup feeNo setup fee
    Additional Details—Confluent monthly bills are based upon resource consumption, i.e., you are only charged for the resources you use when you actually use them: Stream: Kafka clusters are billed for eCKUs/CKUs ($/hour), networking ($/GB), and storage ($/GB-hour). Connect: Use of connectors is billed based on throughput ($/GB) and a task base price ($/task/hour). Process: Use of stream processing with Confluent Cloud for Apache Flink is calculated based on CFUs ($/minute). Govern: Use of Stream Governance is billed based on environment ($/hour). Confluent storage and throughput is calculated in binary gigabytes (GB), where 1 GB is 2^30 bytes. This unit of measurement is also known as a gibibyte (GiB). Please also note that all prices are stated in United States Dollars unless specifically stated otherwise. All billing computations are conducted in Coordinated Universal Time (UTC).
    More Pricing Information
    Community Pulse
    Databricks Data Intelligence PlatformIBM Confluent
    Considered Both Products
    Databricks
    No answer on this topic
    IBM
    Chose IBM Confluent
    For our use case it was very important that the technology we were working with fit into our Azure architecture, and met our data processing size requirements to stream data within certain SLAs. Confluent more than met our performance requirements and compared to the others …
    Incentivized
    Key User Insights
    Would buy again
    100%
    Would buy again
    16 Answers
    No answers on this topic
    Delivers good value for the price
    100%
    Delivers good value for the price
    16 Answers
    No answers on this topic
    Happy with the feature set
    94%
    Happy with the feature set
    15 Answers
    No answers on this topic
    Lived up to sales and marketing promises
    100%
    Lived up to sales and marketing promises
    10 Answers
    No answers on this topic
    Implementation went as expected
    92%
    Implementation went as expected
    12 Answers
    No answers on this topic
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    User Ratings
    Databricks Data Intelligence PlatformIBM Confluent
    Likelihood to Recommend
    9.4
    (21 ratings)
    10.0
    (2 ratings)
    Usability
    9.7
    (7 ratings)
    -
    (0 ratings)
    Support Rating
    8.7
    (2 ratings)
    10.0
    (1 ratings)
    Contract Terms and Pricing Model
    8.0
    (1 ratings)
    -
    (0 ratings)
    Professional Services
    10.0
    (1 ratings)
    -
    (0 ratings)
    User Testimonials
    Databricks Data Intelligence PlatformIBM Confluent
    Likelihood to Recommend
    Databricks
    Medium to Large data throughput shops will benefit the most from Databricks Spark processing. Smaller use cases may find the barrier to entry a bit too high for casual use cases. Some of the overhead to kicking off a Spark compute job can actually lead to your workloads taking longer, but past a certain point the performance returns cannot be beat.
    Incentivized
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    IBM
    If you have a need to stream data, real time or segmented structured data then Confluent is a great platform to do so with. You won't run into packet transfer size limitations that other platforms have. Flexibility in on-prem, cloud, and managed cloud offerings makes it very flexible no matter how you choose to implement.
    Incentivized
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    Pros
    Databricks
    • Process raw data in One Lake (S3) env to relational tables and views
    • Share notebooks with our business analysts so that they can use the queries and generate value out of the data
    • Try out PySpark and Spark SQL queries on raw data before using them in our Spark jobs
    • Modern day ETL operations made easy using Databricks. Provide access mechanism for different set of customers
    Incentivized
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    IBM
    • Products work great.
    • Training is available.
    • Customer support is good.
    Incentivized
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    Cons
    Databricks
    • Sometimes, when multiple jobs depend on each other in different environments, it is not always easy to see the full workflow in one place.
    • It is sometimes difficult to determine which job or cluster contributes more to the overall cost.
    • For beginners, cluster configuration may be a little difficult. So more recommendation in the platform can help.
    Incentivized
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    IBM
    • Cloud based Azure platform features for Confluent lacks behind AWS And GCP
    Incentivized
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    Usability
    Databricks
    Because it is an amazing platform for designing experiments and delivering a deep dive analysis that requires execution of highly complex queries, as well as it allows to share the information and insights across the company with their shared workspaces, while keeping it secured.

    in terms of graph generation and interaction it could improve their UI and UX
    Incentivized
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    IBM
    No answers on this topic
    Support Rating
    Databricks
    One of the best customer and technology support that I have ever experienced in my career. You pay for what you get and you get the Rolls Royce. It reminds me of the customer support of SAS in the 2000s when the tools were reaching some limits and their engineer wanted to know more about what we were doing, long before "data science" was even a name. Databricks truly embraces the partnership with their customer and help them on any given challenge.
    Read full review
    IBM
    The support from the Confluent platform is great and satisfying. We have been working with Confluent for more than a year now. They sent out resident architects to help us set up Confluent cluster on our cloud and help us troubleshoot problems we have encountered. Overall, it has been a great experience working with the Confluent Platform.
    Incentivized
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    Alternatives Considered
    Databricks
    The most important differentiating factor for Databricks Lakehouse Platform from these other platforms is support for ACID transactions and the time travel feature. Also, native integration with managed MLflow is a plus. EMR, Cloudera, and Hortonworks are not as optimized when it comes to Spark Job Execution. Other platforms need to be self-managed, which is another huge hassle.
    Incentivized
    Read full review
    IBM
    For our use case it was very important that the technology we were working with fit into our Azure architecture, and met our data processing size requirements to stream data within certain SLAs. Confluent more than met our performance requirements and compared to the others scale options and cost to run it was more than financially viable as a platform solution to our global operations.
    Incentivized
    Read full review
    Return on Investment
    Databricks
    • The ability to spin up a BIG Data platform with little infrastructure overhead allows us to focus on business value not admin
    • DB has the ability to terminate/time out instances which helps manage cost.
    • The ability to quickly access typical hard to build data scenarios easily is a strength.
    Incentivized
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    IBM
    • It enables us to develop event driven application.
    • It increases our ability to handle streaming data.
    • It reduces latency of communication.
    Incentivized
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