Overview

Pentaho Data Integration

Stacks112

Followers79

Votes0

StreamSets

Stacks53

Followers133

Votes0

Pentaho Data Integration vs StreamSets: What are the differences?

Architecture: Pentaho Data Integration (PDI) utilizes a metadata-driven approach, where transformations and jobs are designed visually using a drag-and-drop interface. In contrast, StreamSets employs a pipeline-based architecture that allows for more granular control over data movement and transformation processes.
Connectors: Pentaho Data Integration provides a wide range of built-in connectors for various data sources and destinations, allowing users to easily integrate with different systems. On the other hand, StreamSets offers a smaller but growing number of connectors, with a focus on real-time streaming sources like Kafka and Hadoop.
Monitoring and Performance: Pentaho Data Integration offers detailed monitoring and logging capabilities, allowing users to track the execution of their ETL processes and troubleshoot any issues. StreamSets, on the other hand, provides real-time monitoring of data flows and performance metrics, enabling users to optimize their pipelines for efficiency.
Community and Support: Pentaho Data Integration has a large and active community of users and contributors, providing a wealth of resources and support options. StreamSets also has a growing community but may offer more personalized support options for enterprise customers.
Scalability and Deployment: Pentaho Data Integration supports traditional on-premises deployment as well as cloud deployment options, providing flexibility for organizations with different infrastructure needs. StreamSets is designed for cloud-native deployments, offering scalability and flexibility for cloud-based data integration projects.
Ease of Use: Pentaho Data Integration is known for its user-friendly interface and ease of use, making it accessible to users with varying levels of technical expertise. StreamSets, while powerful, may have a steeper learning curve due to its advanced features and pipeline-based architecture.

In Summary, Pentaho Data Integration and StreamSets differ in their architecture, connectors, monitoring capabilities, community support, scalability options, and ease of use.

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Detailed Comparison

Pentaho Data Integration	StreamSets
It enable users to ingest, blend, cleanse and prepare diverse data from any source. With visual tools to eliminate coding and complexity, It puts the best quality data at the fingertips of IT and the business.	An end-to-end data integration platform to build, run, monitor and manage smart data pipelines that deliver continuous data for DataOps.
-	Only StreamSets provides a single design experience for all design patterns (batch, streaming, CDC, ETL, ELT, and ML pipelines) for 10x greater developer productivity; smart data pipelines that are resilient to change for 80% less breakages; and a single pane of glass for managing and monitoring all pipelines across hybrid and cloud architectures to eliminate blind spots and control gaps.
Statistics
Stacks 112	Stacks 53
Followers 79	Followers 133
Votes 0	Votes 0
Pros & Cons
No community feedback yet	Cons 2 No user community 1 Crashes
Integrations
No integrations available	HBase Databricks Amazon Redshift MySQL gRPC Google BigQuery Amazon Kinesis Cassandra Hadoop Redis

What are some alternatives to Pentaho Data Integration, StreamSets?

Kafka

Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.

RabbitMQ

RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received.

Celery

Celery is an asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well.

Amazon SQS

Transmit any volume of data, at any level of throughput, without losing messages or requiring other services to be always available. With SQS, you can offload the administrative burden of operating and scaling a highly available messaging cluster, while paying a low price for only what you use.

NSQ

NSQ is a realtime distributed messaging platform designed to operate at scale, handling billions of messages per day. It promotes distributed and decentralized topologies without single points of failure, enabling fault tolerance and high availability coupled with a reliable message delivery guarantee. See features & guarantees.

Apache Spark

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

ActiveMQ

Apache ActiveMQ is fast, supports many Cross Language Clients and Protocols, comes with easy to use Enterprise Integration Patterns and many advanced features while fully supporting JMS 1.1 and J2EE 1.4. Apache ActiveMQ is released under the Apache 2.0 License.

ZeroMQ

The 0MQ lightweight messaging kernel is a library which extends the standard socket interfaces with features traditionally provided by specialised messaging middleware products. 0MQ sockets provide an abstraction of asynchronous message queues, multiple messaging patterns, message filtering (subscriptions), seamless access to multiple transport protocols and more.

Presto

Distributed SQL Query Engine for Big Data

Apache NiFi

An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.