Apache Flink Reviews and details

Social recommendations and mentions

• Top 10 Common Data Engineers and Scientists Pain Points in 2024

  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 / 23 days ago

• Choosing Between a Streaming Database and a Stream Processing Framework in Python

  Other stream processing engines (such as Flink and Spark Streaming) provide SQL interfaces too, but the key difference is a streaming database has its storage. Stream processing engines require a dedicated database to store input and output data. On the other hand, streaming databases utilize cloud-native storage to maintain materialized views and states, allowing data replication and independent storage scaling. - Source: dev.to / 3 months ago

• Go concurrency simplified. Part 4: Post office as a data pipeline

  Also, this knowledge applies to learning more about data engineering, as this field of software engineering relies heavily on the event-driven approach via tools like Spark, Flink, Kafka, etc. - Source: dev.to / 4 months ago

• Five Apache projects you probably didn't know about

  Apache SeaTunnel is a data integration platform that offers the three pillars of data pipelines: sources, transforms, and sinks. It offers an abstract API over three possible engines: the Zeta engine from SeaTunnel or a wrapper around Apache Spark or Apache Flink. Be careful, as each engine comes with its own set of features. - Source: dev.to / 5 months ago

• Getting Started with Flink SQL, Apache Iceberg and DynamoDB Catalog

  Due to the technology transformation we want to do recently, we started to investigate Apache Iceberg. In addition, the data processing engine we use in house is Apache Flink, so it's only fair to look for an experimental environment that integrates Flink and Iceberg. - Source: dev.to / 5 months ago

• Snowflake - what are the streaming capabilities it provides?

  When low latency matters you should always consider an ETL approach rather than ELT, e.g. Collect data in Kafka and process using Kafka Streams/Flink in Java or Quix Streams/Bytewax in Python, then sink it to Snowflake where you can handle non-critical workloads (as is the case for 99% of BI/analytics). This way you can choose the right path for your data depending on how quickly it needs to be served. Source: 12 months ago

• JR, quality Random Data from the Command line, part I

  Sometimes we may need to generate random data of type 2 in different streams, so the "coherency" must also spread across different entities, think for example to referential integrity in databases. If I am generating users, products and orders to three different Kafka topics and I want to create a streaming application with Apache Flink, I definitely need data to be coherent across topics. - Source: dev.to / about 1 year ago

• Brand Lift Studies on Reddit

  The Treatment and Control audiences need to be stored for future low-latency, high-reliability retrieval. Retrieval happens when we are delivering the survey, and informs the system which users to send surveys to. How is this achieved at Reddit’s scale? Users interact with ads, which generate events that are sent to our downstream systems for processing. At the output, these interactions are stored in DynamoDB as... Source: about 1 year ago

• Query Real Time Data in Kafka Using SQL

  Most streaming database technologies use SQL for these reasons: RisingWave, Materialize, KsqlDB, Apache Flink, and so on offering SQL interfaces. This post explains how to choose the right streaming database. - Source: dev.to / about 1 year ago

• 5 Best Practices For Data Integration To Boost ROI And Efficiency

  There are different ways to implement parallel dataflows, such as using parallel data processing frameworks like Apache Hadoop, Apache Spark, and Apache Flink, or using cloud-based services like Amazon EMR and Google Cloud Dataflow. It is also possible to use parallel dataflow frameworks to handle big data and distributed computing, like Apache Nifi and Apache Kafka. Source: about 1 year ago

• Forward Compatible Enum Values in API with Java Jackson

  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 / about 1 year ago

• Which MQTT (or similar protocol) broker for a few 10k IoT devices with quite a lot of traffic?

  One can also consider https://flink.apache.org/ instead of Kafka for connecting a large number of devices. Source: over 1 year ago

• Apache Pulsar vs Apache Kafka - How to choose a data streaming platform

  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 1 year ago

• Real Time Data Infra Stack

  The Apache Flink, which is often mentioned, is one of these options, and there are many others. - Source: dev.to / over 1 year ago

• In One Minute : Hadoop

  Flink, a fast and reliable large-scale data processing engine. - Source: dev.to / over 1 year ago

• A peek into Location Data Science at Ola

  This requires the use of distributed computation tools such as Spark and Hadoop, Flink and Kafka are used. But for occasional experimentation, Pandas, Geopandas and Dask are some of the commonly used tools. - Source: dev.to / over 1 year ago

• Evolutionary Data Infrastructure

  Therefore, I still recommend using a streaming framework such as Apache Flink or Apache Kafka Streams. - Source: dev.to / over 1 year ago

• Headless BI with streaming data

  In the last few years, streaming SQL technologies such as ksqlDB, Materialize, and Apache Flink have significantly progressed. These technologies enable us to process streaming data and run analysis with SQL—without needing to learn a new language or build specific language-unique integrations. - Source: dev.to / over 1 year ago

• Spark vs Flink vs ksqlDB for stream processing

  Apache FlinkⓇ is a stream and batch processing framework designed for data analytics, data pipelines, ETL, and event-driven applications. Like Spark, Flink helps process large-scale data streams and delivers real-time analytical insights. - Source: dev.to / over 1 year ago

• Talend, Apache Kafka, MongoDB And Docker-Compose - Real-Time Streaming – Have You Ever Tried to Integrate Them Together?

  At the forefront we can distinguish: Apache Kafka and Apache Flink. Often in the same “bag” you can still meet Spark Structured Streaming or Spark Streaming, but this is a mistake, because Spark represents an approach that we call “micro-batch” – that is, processing data in small packages. Source: about 2 years ago

• Spark for beginners - and you

  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 2 years ago