IBM StreamSets Description
IBM® StreamSets allows users to create and maintain smart streaming data pipelines using an intuitive graphical user interface. This facilitates seamless data integration in hybrid and multicloud environments. IBM StreamSets is used by leading global companies to support millions data pipelines, for modern analytics and intelligent applications.
Reduce data staleness, and enable real-time information at scale. Handle millions of records across thousands of pipelines in seconds.
Drag-and-drop processors that automatically detect and adapt to data drift will protect your data pipelines against unexpected changes and shifts.
Create streaming pipelines for ingesting structured, semistructured, or unstructured data to deliver it to multiple destinations.
IBM StreamSets Alternatives
dbt Labs is redefining how data teams work with SQL. Instead of waiting on complex ETL processes, dbt lets data analysts and data engineers build production-ready transformations directly in the warehouse, using code, version control, and CI/CD. This community-driven approach puts power back in the hands of practitioners while maintaining governance and scalability for enterprise use.
With a rapidly growing open-source community and an enterprise-grade cloud platform, dbt is at the heart of the modern data stack. It’s the go-to solution for teams who want faster analytics, higher quality data, and the confidence that comes from transparent, testable transformations.
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Big Data Quality must always be verified to ensure that data is safe, accurate, and complete. Data is moved through multiple IT platforms or stored in Data Lakes. The Big Data Challenge: Data often loses its trustworthiness because of (i) Undiscovered errors in incoming data (iii). Multiple data sources that get out-of-synchrony over time (iii). Structural changes to data in downstream processes not expected downstream and (iv) multiple IT platforms (Hadoop DW, Cloud). Unexpected errors can occur when data moves between systems, such as from a Data Warehouse to a Hadoop environment, NoSQL database, or the Cloud. Data can change unexpectedly due to poor processes, ad-hoc data policies, poor data storage and control, and lack of control over certain data sources (e.g., external providers). DataBuck is an autonomous, self-learning, Big Data Quality validation tool and Data Matching tool.
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Rivery
Rivery’s ETL platform consolidates, transforms, and manages all of a company’s internal and external data sources in the cloud.
Key Features:
Pre-built Data Models: Rivery comes with an extensive library of pre-built data models that enable data teams to instantly create powerful data pipelines.
Fully managed: A no-code, auto-scalable, and hassle-free platform. Rivery takes care of the back end, allowing teams to spend time on mission-critical priorities rather than maintenance.
Multiple Environments: Rivery enables teams to construct and clone custom environments for specific teams or projects.
Reverse ETL: Allows companies to automatically send data from cloud warehouses to business applications, marketing clouds, CPD’s, and more.
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Apache Airflow
Airflow is a community-driven platform designed for the programmatic creation, scheduling, and monitoring of workflows. With its modular architecture, Airflow employs a message queue to manage an unlimited number of workers, making it highly scalable. The system is capable of handling complex operations through its ability to define pipelines using Python, facilitating dynamic pipeline generation. This flexibility enables developers to write code that can create pipelines on the fly. Users can easily create custom operators and expand existing libraries, tailoring the abstraction level to meet their specific needs. The pipelines in Airflow are both concise and clear, with built-in parametrization supported by the robust Jinja templating engine. Eliminate the need for complex command-line operations or obscure XML configurations! Instead, leverage standard Python functionalities to construct workflows, incorporating date-time formats for scheduling and utilizing loops for the dynamic generation of tasks. This approach ensures that you retain complete freedom and adaptability when designing your workflows, allowing you to efficiently respond to changing requirements. Additionally, Airflow's user-friendly interface empowers teams to collaboratively refine and optimize their workflow processes.
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Pricing
Pricing Starts At:
$1000 per month
Pricing Information:
5 active jobs
5 users
50 published pipelines
99.9% uptime
Email Support (Business hours M-F)
5 users
50 published pipelines
99.9% uptime
Email Support (Business hours M-F)
Free Version:
Yes
Free Trial:
Yes
Company Details
Company:
IBM
Year Founded:
1911
Headquarters:
United States
Website:
www.ibm.com/products/streamsets
Media
Use the wysiwyg editor to simply build and preview your smart data pipeline.
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Product Details
Platforms
Web-Based
Types of Training
Training Docs
Live Training (Online)
Webinars
In Person
Training Videos
Customer Support
Business Hours
Live Rep (24/7)
Online Support
IBM StreamSets Features and Options
DevOps Software
Approval Workflow
Dashboard
KPIs
Policy Management
Portfolio Management
Prioritization
Release Management
Timeline Management
Troubleshooting Reports
Streaming Analytics Platform
Data Enrichment
Data Wrangling / Data Prep
Multiple Data Source Support
Process Automation
Real-time Analysis / Reporting
Visualization Dashboards
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