IOblend and Streamsets are both advanced data integration platforms that cater to the growing needs of businesses, especially in real-time analytics use cases. While there are similarities, they also bring different features to the table. Here’s an overview of their capabilities:
Real-time Data Integration
IOblend:
- Supports real-time, production-grade data pipelines using Apache Spark with proprietary tech enhancements.
- Can integrate equally streaming (transactional event) and batch data due to its Kappa architecture with full CDC capabilities.
Streamsets:
- Designed to handle streaming data with native support for change data capture (CDC) and supports both real-time and batch processing.
Low-code/No-code Development
IOblend:
- Provides low-code/no-code development, facilitating quicker data migration and minimization of manual data wrangling.
Streamsets:
- Features a drag-and-drop interface for designing data pipelines and also supports scripting for more intricate requirements.
Data Architecture
IOblend:
- Enables delivery of both centralized and federated data architectures.
Streamsets:
- Offers a flexible architecture allowing for both centralized and decentralized data operations.
Performance & Scalability
IOblend:
- Boasts low-latency, massively parallelized data processing with speeds exceeding 10 million transactions per second.
Streamsets:
- Optimized for performance in large-scale environments and supports various scalability configurations to handle growing data loads.
Partnerships & Cloud Integration
IOblend:
- Has real-time integration capabilities with Snowflake, AWS, Google Cloud and Azure products and is an ISV technology partner with Snowflake and Microsoft.
Streamsets:
- Provides integration with major cloud platforms including AWS, Azure, Google Cloud, as well as other platforms and data stores.
User Interface & Design
IOblend:
- Consists of two main components: IOblend Designer and IOblend Engine, facilitating design and execution respectively.
Streamsets:
- Offers a singular, intuitive platform called Streamsets Data Collector, tailored for designing, deploying, and monitoring data pipelines.
Data Management & Governance
IOblend:
- Ensures data integrity with features like automatic record-level lineage, CDC, SCD, metadata management, de-duping, cataloguing, schema drifts, windowing, regressions, eventing, late-arriving data, etc. integrated in every data pipeline.
- Connects to any data source via ESB/API/JDBC/flat files, both batch and streaming (inc. JDBC) with CDC (supports all three log, trigger or query based).
Streamsets:
- Prioritizes data drift management, ensuring pipeline robustness against changes in data, infrastructure, and schemas. Also has strong monitoring capabilities.
- 100+ pre-built connectors to all major data sources
Cost & Licensing
IOblend:
- The Developer Edition is free, while the Enterprise Suite requires a paid annual license.
Streamsets:
- Offers a free community version and premium versions with added functionalities and support.
Deployment & Flexibility
IOblend:
- Operational on any cloud, on-premises, or in hybrid settings. Comes in Developer and Enterprise Editions.
Streamsets:
- Supports deployment in cloud, on-premises, and edge devices, ensuring flexibility in data operations.
Community & Support
IOblend:
- Being relatively new, its community is still burgeoning. Provides online support for Developer Edition and premium support for Enterprise Edition.
Streamsets:
- Sports a vibrant community providing resources, plugins, and assistance. Premium support is also available for enterprise-grade users.
To sum up, while IOblend places emphasis on real-time data integration and low-code solutions, Streamsets is tailored for handling streaming data with an emphasis on data drift management. Choosing between them would rest on the specific requirements, infrastructure, and objectives of an organization.
Better airport operations with real-time analytics
Good and bad Welcome to the next issue of our real-time analytics blog. Now that the summer holiday season is upon us, many of us will be using air travel to get to their destinations of choice. This means, we will be going through the airports. As passengers, we have love-hate relationships with airports. Some
The making of a commercial flight
What makes a flight Welcome to the next leg of our airline data blog journey. In this article, we will be looking at what happens behind the scenes to make a single commercial flight, well, take flight. We will again consider how processes and data come together in (somewhat of a) harmony to bring your
Enhance your airline’s analytics with a data mesh
Building a flying program In the last blog, I have covered how airlines plan their route networks using various strategies, data sources and analytical tools. Today, we will be covering how the network plan comes to life. Once the plans are developed, they are handed over to “production”. Putting a network plan into production is
Planning an airline’s route network with deep data insights
What makes an airline Commercial airlines are complex beasts. They comprise of multiple intertwined (and siloed!) functions that make the business work. As passengers, we see a “tip of the iceberg” when we fly. A lot of work goes into making that flight happen, which starts well in advance. Let’s distil the complexity into something
Flying smarter with real-time analytics
Dynamic decisioning We continue exploring the topics of operational analytics (OA) in the aviation industry. Data plays a crucial role in flight performance analytics, operational decisioning and risk management. Real-time data enhances them. The aviation industry uses real-time data for a multitude of operational analytics cases: monitor operational systems, measure wear and tear of equipment,
How Operational Analytics power Ground Handling
The Ground Handling journey – today and tomorrow In today’s blog we are discussing how Operational Analytics (OA) enables the aviation Ground Handling industry to deliver their services to airlines. Aviation is one of the most complex industries out there, so it offers a wealth of examples (plus it’s also close to our hearts). OA
