Samza Description
Samza lets you build stateful applications that can process data in real time from multiple sources, including Apache Kafka. It has been battle-tested at scale and supports flexible deployment options, including running on YARN or as a standalone program. Samza offers high throughput and low latency to instantly analyze your data. With features like host-affinity and incremental checkpoints, Samza can scale to many terabytes in state. Samza is easy-to-use with flexible deployment options YARN, Kubernetes, or standalone. The ability to run the same code to process streaming and batch data. Integrates with multiple sources, including Kafka and HDFS, AWS Kinesis Azure Eventhubs, Azure Eventhubs K-V stores, ElasticSearch, AWS Kinesis, Kafka and ElasticSearch.
Samza Alternatives
VeloDB
VeloDB, powered by Apache Doris is a modern database for real-time analytics at scale.
In seconds, micro-batch data can be ingested using a push-based system. Storage engine with upserts, appends and pre-aggregations in real-time. Unmatched performance in real-time data service and interactive ad hoc queries.
Not only structured data, but also semi-structured. Not only real-time analytics, but also batch processing. Not only run queries against internal data, but also work as an federated query engine to access external databases and data lakes.
Distributed design to support linear scalability. Resource usage can be adjusted flexibly to meet workload requirements, whether on-premise or cloud deployment, separation or integration.
Apache Doris is fully compatible and built on this open source software. Support MySQL functions, protocol, and SQL to allow easy integration with other tools.
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Baidu AI Cloud Stream Computing
Baidu Stream Computing provides real-time data processing with low delay, high throughput, and high accuracy. It is compatible with Spark SQL and can process complex business logic through SQL statements. It also provides users with a full life cycle management of streaming-oriented computing jobs. As the upstream and downstream of stream computing, integrate deeply with multiple storage solutions of Baidu AI Cloud, including Baidu Kafka and RDS. Provide a comprehensive monitoring indicator for the job. The user can view monitoring indicators and set alarm rules to protect the task.
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Apache Beam
This is the easiest way to perform batch and streaming data processing. For mission-critical production workloads, write once and run anywhere data processing. Beam can read your data from any supported source, whether it's on-prem and in the cloud. Beam executes your business logic in both batch and streaming scenarios. Beam converts the results of your data processing logic into the most popular data sinks. A single programming model that can be used for both streaming and batch use cases. This is a simplified version of the code for all members of your data and applications teams. Apache Beam is extensible. TensorFlow Extended, Apache Hop and other projects built on Apache Beam are examples of Apache Beam's extensibility. Execute pipelines in multiple execution environments (runners), allowing flexibility and avoiding lock-in. Open, community-based development and support are available to help you develop your application and meet your specific needs.
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Integrations
Company Details
Company:
Apache Software Foundation
Year Founded:
1999
Headquarters:
United States
Website:
samza.apache.org
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Product Details
Platforms
SaaS
Type of Training
Documentation
Customer Support
Online
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