Kafka Streams
Apache Flink
Apache Kafka
Apache Spark
Apache NiFi
Confluent
KSQL
RisingWave
Apache Storm
Apache Spark
Apache Flink
Google BigQuery
Qubole
Hadoop
Apache Beam
Snowflake
Kafka Streams Apache StormKafka Streams might be a bit more popular than Apache Storm. We know about 14 links to it since March 2021 and only 11 links to Apache Storm. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Data scientists often prefer Python for its simplicity and powerful libraries like Pandas or SciPy. However, many real-time data processing tools are Java-based. Take the example of Kafka, Flink, or Spark streaming. While these tools have their Python API/wrapper libraries, they introduce increased latency, and data scientists need to manage dependencies for both Python and JVM environments. For example,... - Source: dev.to / about 1 year ago
We’re not discussing the technical details behind the deduplication process. It could be Apache Flink, Apache Spark, or Kafka Streams. Anyway, it’s out of the scope of this article. - Source: dev.to / over 2 years ago
In pub-sub systems, you cannot have multiple services to consume the same data because the messages are deleted after being consumed by one consumer. Whereas in Kafka, you can have multiple services to consume. This opens the door to a lot of opportunities such as Kafka streams, Kafka connect. We’ll discuss these at the end of the series. - Source: dev.to / over 2 years ago
Internally, Streamiz use the .Net client for Apache Kafka released by Confluent and try to provide the same features than Kafka Streams. There is gap between these two library, but the trend is decreasing after each release. - Source: dev.to / over 2 years ago
Both Kafka and Pulsar provide some kind of stream processing capability, but Kafka is much further along in that regard. Pulsar stream processing relies on the Pulsar Functions interface which is only suited for simple callbacks. On the other hand, Kafka Streams and ksqlDB are more complete solutions that could be considered replacements for Apache Spark or Apache Flink, state-of-the-art stream-processing... - Source: dev.to / over 2 years ago
There are several frameworks available for batch processing, such as Hadoop, Apache Storm, and DataTorrent RTS. - Source: dev.to / over 2 years ago
Although this article lists a lot of targets for technical selection, there are definitely others that I haven't listed, which may be either outdated, less-used options such as Apache Storm or out of my radar from the beginning, like JAVA ecosystem. - Source: dev.to / over 2 years ago
Storm, a system for real-time and stream processing. - Source: dev.to / over 2 years ago
Google has scaled well and has helped others scale, Twitter has always been behind by years. I think the only thing they did well was Twitter Storm, now taken up by Apache Foundation. Source: over 2 years ago
Streaming: Sparks Streamings's latency is at least 500ms, since it operates on micro-batches of records, instead of processing one record at a time. Native streaming tools like Storm, Apex or Flink might be better for low-latency applications. - Source: dev.to / over 3 years ago
Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.
Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
Apache Kafka - Apache Kafka is an open-source message broker project developed by the Apache Software Foundation written in Scala.
Google BigQuery - A fully managed data warehouse for large-scale data analytics.
Apache NiFi - An easy to use, powerful, and reliable system to process and distribute data.
Qubole - Qubole delivers a self-service platform for big aata analytics built on Amazon, Microsoft and Google Clouds.