Bitbucket from Australian-headquartered Atlassian offers source code management and version control.
$0
per month
StreamSets
Score 8.4 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.
As a team we need to push code into the repo on daily basis, Bitbucket has proven that is a reliable and secure server to save and get the code available in no time. The administration part is really easy and there's an extra tool for every developer profile either if you want to use the console or a GUI like Sourcetree.
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.
Very easy to integrate with other DevOps tools like Jenkins and with project/workflow management tools like JIRA.
Very efficient in managing security and compliance standards for code, especially during pull requests, merge requests, branching, etc.
Very robust in performance, especially the cloud and datacenter versions hardly hit any performance issues and supports more than 2000+ developers in my company.
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.
All products have room for improvement. The system improves over time with better and better integrations and I look forward to even more features without paying extra! The system has increased transparency across my organization and with this transparency comes increased throughput on projects. I don't think I can go back to any other system and we are definitely married to this product.
The architecture of Bitbucket makes it more easily scalable than other source code management repositories. Also, administration and maintaining the instance is very easy. It integrates with JIRA and other CI/CD applications which makes it more useful to reduce the efforts. It supports multiple plugins and those bring a lot of extra functionality. It increases the overall efficiency and usefulness of Bitbucket.
The customer support provided by Atlassian (Bitbucket's parent company that also makes Jira, Confluence, etc.) is very helpful. They seem to be very concerned about any issues reported with their products and even just questions about functionality. They are constantly improving the products with new features in nearly every release. Plus they have a plethora of online documentation to reference.
For the features we were looking at, Bitbucket, GitHub and GitLab were all at par and were in a similar price range. We found that GitHub was the most full featured should we need to scale very quickly. GitLab was at par with GitHub for our future needs, but GitHub was a more familiar tool compared to GitLab. Bitbucket won out because of its close integration with Jira and being in the Atlassian family. It was also cheaper than GitHub. As we started with Jira, Bitbucket addition became a natural next step for us. We really liked Bitbucket and stayed with it but we do know we have great options in the form of GitHub and GitLab should we need to scale fast.
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.
