Apache Spark Streaming vs. StreamSets DataOps Platform

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Spark Streaming
Score 8.7 out of 10
N/A
Apache Spark Streaming is a scalable fault-tolerant streaming processing system that natively supports both batch and streaming workloads.N/A
StreamSets
Score 8.1 out of 10
N/A
StreamSets in San Francisco offers their DataOps Platform, a subscription based streaming analytics platform including StreamSets Data Collector data source management, Control Hub for data movement architecture management, StreamSets Data Collector Edge IoT manager, DataFlow Performance Manager (DPM), and StreamSets Data Protector compliance (e.g. GDPR) compliance module.N/A
Pricing
Apache Spark StreamingStreamSets DataOps Platform
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Apache Spark StreamingStreamSets
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Apache Spark StreamingStreamSets DataOps Platform
Top Pros
Top Cons
Features
Apache Spark StreamingStreamSets DataOps Platform
Streaming Analytics
Comparison of Streaming Analytics features of Product A and Product B
Apache Spark Streaming
8.4
1 Ratings
4% above category average
StreamSets DataOps Platform
9.0
1 Ratings
11% above category average
Real-Time Data Analysis8.01 Ratings00 Ratings
Visualization Dashboards9.01 Ratings7.01 Ratings
Data Ingestion from Multiple Data Sources9.01 Ratings00 Ratings
Low Latency8.01 Ratings8.01 Ratings
Integrated Development Tools8.01 Ratings10.01 Ratings
Data wrangling and preparation8.01 Ratings10.01 Ratings
Linear Scale-Out8.01 Ratings00 Ratings
Machine Learning Automation9.01 Ratings00 Ratings
Data Enrichment9.01 Ratings10.01 Ratings
Best Alternatives
Apache Spark StreamingStreamSets DataOps Platform
Small Businesses
Amazon Kinesis
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Score 8.2 out of 10
Amazon Kinesis
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Score 8.2 out of 10
Medium-sized Companies
Confluent Platform
Confluent Platform
Score 7.5 out of 10
Confluent Platform
Confluent Platform
Score 7.5 out of 10
Enterprises
Spotfire Streaming
Spotfire Streaming
Score 7.8 out of 10
Spotfire Streaming
Spotfire Streaming
Score 7.8 out of 10
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User Ratings
Apache Spark StreamingStreamSets DataOps Platform
Likelihood to Recommend
9.0
(1 ratings)
9.0
(1 ratings)
User Testimonials
Apache Spark StreamingStreamSets DataOps Platform
Likelihood to Recommend
Apache
Apache Spark Streaming is a tool that we are using for almost a year and is excellent in managing batch processing. It is user-friendly. Using it, we can even process our massive data in fractions of seconds. Its pricing is its other plus point. Only its In-memory processing is its demerit as it occupies a large memory.
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StreamSets
Majorly for all Batch and Streaming Scenarios we are designing StreamSets pipelines, few best suited and tried out use cases below : 1. JDBC to ADLS data transfer based on source refresh frequency. 2. Kafka to GCS. 3. Kafka to Azure Event. 4. Hub HDFS to ADLS data transfer. 5. Schema generation to generate Avro. The easy to design Canvas, Scheduling Jobs, Fragment creation and utilization, an inbuilt wide range of Stage availability makes it an even more favorable tool for me to design data engineering pipelines.
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Pros
Apache
  • It is amazing in solving complicated transformative logic.
  • It is straightforward to program.
  • It is a very quick tool.
  • It processes large data within a fraction of seconds.
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StreamSets
  • A easy to use canvas to create Data Engineering Pipeline.
  • A wide range of available Stages ie. Sources, Processors, Executors, and Destinations.
  • Supports both Batch and Streaming Pipelines.
  • Scheduling is way easier than cron.
  • Integration with Key-Vaults for Secrets Fetching.
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Cons
Apache
  • There must be more documentation.
  • It is a profoundly complex tool.
  • Its in-memory processing consumes massive memory.
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StreamSets
  • Monitoring/Visualization can be improvised and enhanced a lot (e.g. to monitor a Job to see what happened 7 days back with data transfer).
  • The logging mechanism can be simplified (Logs can be filtered with "ERROR", "DEBUG", "ALL" etc but still takes some time to get familiar for understanding).
  • Auto Scalability for heavy load transfer (Taking much time for >5 million record transfer from JDBC to ADLS destination in Avro file transfer).
  • There should be a concept of creating Global variables which is missing.
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Alternatives Considered
Apache
Apache Spark Streaming stands above all the huge data transformative tools because of its speed of processing which was quite slow in Presto as it takes a lot of our time in the data processing. Spark, comfortably provides integration with Jupyter like notebook environment. and Spark's combination with Jupyter and Python results in enhancing the speed .
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StreamSets
StreamSets is a one-stop solution to design Data engineering Pipelines and doesn't require deep Programming knowledge, It's so user-friendly that anyone in Team can contribute to the Idea of pipeline design. In Hadoop One has to be programming proficient to use its various components like Hive, HDFS, Kafka, etc but in StreamSets all these stages are built-in and ready to use with minor configuration.
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Return on Investment
Apache
  • Cost and time-effective tool for our business.
  • We can integrate with Jupyter with many conveniences.
  • Its high-speed data processing has proved beneficial for us.
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StreamSets
  • Simplified Improvised Overall data ingestion and Integration Process.
  • Support to various Hetrogenous Source systems like RDBMS< Kafka, Salesforce, Key Vault.
  • Secure, easy to launch Integration tool.
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