IBM StreamSets Features
What are the features of IBM StreamSets?
Data
- Data Processing
Functionality
- Diverse Extraction Points
- Data Structuring
- Consolidation
- Data Cleaning
- Cloud Extraction
- Visualization
- Extraction
- Transformation
- Loading
- Automation
- Scalability
Management
- Reporting
- Auditing
Diverse Extraction Points
- Diverse Extraction Points
IBM StreamSets Categories on G2
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Data
Data Processing | The ability to process large amounts of data. This feature was mentioned in 15 IBM StreamSets reviews. | 82% (Based on 15 reviews) | |
Data Sources | Based on 14 IBM StreamSets reviews. The ability to process data from a wide variety of sources and formats. | 80% (Based on 14 reviews) | |
Integration | The ability to work seamlessly with another software platform. 15 reviewers of IBM StreamSets have provided feedback on this feature. | 77% (Based on 15 reviews) | |
Real-Time Processing | Processing data from a variety of sources in real time as it arrives. 14 reviewers of IBM StreamSets have provided feedback on this feature. | 90% (Based on 14 reviews) |
Analytics
Reporting & Analytics | As reported in 14 IBM StreamSets reviews. Tools to visualize and analyze data. | 76% (Based on 14 reviews) | |
Analytics capabilities | As reported in 14 IBM StreamSets reviews. Provides a high performance, flexibile analytics platform to support data management and embrace data driven decision making. | 68% (Based on 14 reviews) | |
Dasboard visualizations | Collect and displays metrics across the data integration via a dashboard. 14 reviewers of IBM StreamSets have provided feedback on this feature. | 68% (Based on 14 reviews) |
Functionality
Diverse Extraction Points | Pull any required data from a variety of sources, including email, web pages, PDFs, and other documents. This feature was mentioned in 33 IBM StreamSets reviews. | 77% (Based on 33 reviews) | |
Data Structuring | Based on 33 IBM StreamSets reviews. Organize extracted data into a more easily digestible structure. | 76% (Based on 33 reviews) | |
Consolidation | Amass extracted data in a variety of data formats like spreadsheets and .csv. 32 reviewers of IBM StreamSets have provided feedback on this feature. | 78% (Based on 32 reviews) | |
Data Cleaning | As reported in 32 IBM StreamSets reviews. Clean extracted data by removing duplicates, clearing excess characters, grouping by characteristic, and more. | 79% (Based on 32 reviews) | |
Cloud Extraction | As reported in 32 IBM StreamSets reviews. Stores data in cloud storage for access at any point. | 78% (Based on 32 reviews) | |
Visualization | Generate visual data representations from extracted data. 32 reviewers of IBM StreamSets have provided feedback on this feature. | 77% (Based on 32 reviews) | |
Extraction | As reported in 32 IBM StreamSets reviews. Extract data from the designated source(s) like relational databases, JSON files, and XML files. | 77% (Based on 32 reviews) | |
Transformation | Based on 32 IBM StreamSets reviews. Cleanse and re-format extracted data to the needed target format. | 78% (Based on 32 reviews) | |
Loading | As reported in 32 IBM StreamSets reviews. Load reformatted data into target database, data warehouse, or other storage location. | 75% (Based on 32 reviews) | |
Automation | Based on 33 IBM StreamSets reviews. Arrange ETL processes to occur automatically on needed time schedule (e.g., daily, weekly, monthly). | 75% (Based on 33 reviews) | |
Scalability | Based on 33 IBM StreamSets reviews. Capable of scaling processing power up or down based on ETL volume. | 73% (Based on 33 reviews) |
Management
Reporting | As reported in 33 IBM StreamSets reviews. View ETL process data via reports and visualizations like charts and graphs. | 67% (Based on 33 reviews) | |
Auditing | As reported in 31 IBM StreamSets reviews. Record ETL historical data for auditing and potential data correction needs. | 69% (Based on 31 reviews) |
Data Management
Data Integration | Integrates data and data-related technologies into a single environment. 13 reviewers of IBM StreamSets have provided feedback on this feature. | 73% (Based on 13 reviews) | |
Metadata | Provides metadata management capabilities. 11 reviewers of IBM StreamSets have provided feedback on this feature. | 64% (Based on 11 reviews) | |
Self-service | Based on 12 IBM StreamSets reviews. Empowers the user via a self-service capability to manage data workflows. | 65% (Based on 12 reviews) | |
Automated workflows | As reported in 11 IBM StreamSets reviews. Completely automates end-to-end data workflows across the data integration lifecycle. | 71% (Based on 11 reviews) |
Monitoring and Management
Data Observability | As reported in 13 IBM StreamSets reviews. Involved solely in monitoring data pipelines, sending alerts and troubleshooting data. | 67% (Based on 13 reviews) | |
Testing capabilities | As reported in 13 IBM StreamSets reviews. Deploys testing capabilities such as report testing, big data testing, cloud data migration testing, ETL and data warehouse testing. | 71% (Based on 13 reviews) |
Cloud Deployment
Hybrid cloud support | Supports analytical platforms and data pipelines across complex hybrid environments. 13 reviewers of IBM StreamSets have provided feedback on this feature. | 71% (Based on 13 reviews) | |
Cloud migration capabilities | Supports migration of component or pipeline to different cloud environments. This feature was mentioned in 13 IBM StreamSets reviews. | 69% (Based on 13 reviews) |
Diverse Extraction Points
Diverse Extraction Points | As reported in 33 IBM StreamSets reviews. Pull any required data from a variety of sources, including email, web pages, PDFs, and other documents. | 77% (Based on 33 reviews) |
Generative AI
AI Text Generation | Allows users to generate text based on a text prompt. | Not enough data | |
AI Text Summarization | Condenses long documents or text into a brief summary. | Not enough data |
Agentic AI - DataOps Platforms
Autonomous Task Execution | Capability to perform complex tasks without constant human input | Not enough data | |
Multi-step Planning | Ability to break down and plan multi-step processes | Not enough data | |
Cross-system Integration | Works across multiple software systems or databases | Not enough data | |
Adaptive Learning | Improves performance based on feedback and experience | Not enough data | |
Decision Making | Makes informed choices based on available data and objectives | Not enough data |
