Apache Flink Reviews and Details

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Social recommendations and mentions

• Why Apache IoTDB Is Written in Java: A Decade of Engineering Trade-offs

  When IoTDB was initiated in 2011, almost all influential distributed systems and databases were built in Java or on the JVM—such as Hadoop, HBase, Spark (Scala on JVM), Cassandra, Kafka, and Flink. To integrate deeply with the big data ecosystem, choosing Java was a natural decision. - Source: dev.to / 4 months ago

• Gravitino - the unified metadata lake

  In the meantime, other query engine support is on the roadmap, including Apache Spark, Apache Flink, and others. - Source: dev.to / 11 months ago

• Towards Sub-100ms Latency Stream Processing with an S3-Based Architecture

  Many stream processing systems today still rely on local disks and RocksDB to manage state. This model has been around for a while and works fine in simple, single-tenant setups. Apache Flink, for example, uses RocksDB as its default state backend - state is kept on local disks, and periodic checkpoints are written to external storage for recovery. - Source: dev.to / about 1 year ago

• Introducing RisingWave's Hosted Iceberg Catalog-No External Setup Needed

  Because the hosted catalog is a standard JDBC catalog, tools like Spark, Trino, and Flink can still access your tables. For example:. - Source: dev.to / about 1 year ago

• When plans change at 500 feet: Complex event processing of ADS-B aviation data with Apache Flink

  I wrote a python based aircraft monitor which polls the adsb.fi feed for aircraft transponder messages, and publishes each location update as a new event into an Apache Kafka topic. I used Apache Flink — and more specially Flink SQL, to transform and analyse my flight data. The TL;DR summary is I can write SQL for my real-time data processing queries — and get the scalability, fault tolerance, and low latency... - Source: dev.to / about 1 year ago

• What is Apache Flink? Exploring Its Open Source Business Model, Funding, and Community

  Continuous Learning: Leverage online tutorials from the official Flink website and attend webinars for deeper insights. - Source: dev.to / about 1 year ago

• Is RisingWave the Next Apache Flink?

  Apache Flink, known initially as Stratosphere, is a distributed stream processing engine initiated by a group of researchers at TU Berlin. Since its initial release in May 2011, Flink has gained immense popularity in both academia and industry. And it is currently the most well-known streaming system globally (challenge me if you think I got it wrong!). - Source: dev.to / about 1 year ago

• Every Database Will Support Iceberg — Here's Why

  Apache Iceberg defines a table format that separates how data is stored from how data is queried. Any engine that implements the Iceberg integration — Spark, Flink, Trino, DuckDB, Snowflake, RisingWave — can read and/or write Iceberg data directly. - Source: dev.to / about 1 year ago

• RisingWave Turns Four: Our Journey Beyond Democratizing Stream Processing

  The last decade saw the rise of open-source frameworks like Apache Flink, Spark Streaming, and Apache Samza. These offered more flexibility but still demanded significant engineering muscle to run effectively at scale. Companies using them often needed specialized stream processing engineers just to manage internal state, tune performance, and handle the day-to-day operational challenges. The barrier to entry... - Source: dev.to / over 1 year ago

• Twitter's 600-Tweet Daily Limit Crisis: Soaring GCP Costs and the Open Source Fix Elon Musk Ignored

  Apache Flink: Flink is a unified streaming and batching platform developed under the Apache Foundation. It provides support for Java API and a SQL interface. Flink boasts a large ecosystem and can seamlessly integrate with various services, including Kafka, Pulsar, HDFS, Iceberg, Hudi, and other systems. - Source: dev.to / over 1 year ago

• Exploring the Power and Community Behind Apache Flink

  In conclusion, Apache Flink is more than a big data processing tool—it is a thriving ecosystem that exemplifies the power of open source collaboration. From its impressive technical capabilities to its innovative funding model, Apache Flink shows that sustainable software development is possible when community, corporate support, and transparency converge. As industries continue to demand efficient real-time data... - Source: dev.to / over 1 year ago

• Automating Enhanced Due Diligence in Regulated Applications

  For real-time data streaming and analysis, tools like Apache Kafka and Apache Flink are popular choices. - Source: dev.to / over 1 year ago

• Major Technologies Worth Learning in 2025 for Data Professionals

  With the explosion of IoT devices and demand for instant insights, real-time analytics is no longer optional. Technologies like Apache Kafka, Apache Flink, and Redpanda are at the forefront of this movement. Learning these platforms will help you design systems that process streaming data efficiently. - Source: dev.to / over 1 year ago

• Serverless Data Processing on AWS : AWS Project

  To do so, we will use Kinesis Data Analytics to run an Apache Flink application. To enhance our development experience, we will use Studio notebooks for Kinesis Data Analytics that are powered by Apache Zeppelin. - Source: dev.to / over 1 year ago

• Data Engineering with Scala: Mastering Real-Time Data Processing with Apache Flink and Google Pub/Sub

  Apache Flink is a distributed data processing framework for both batch and streaming processing. It can be used to develop event-driven applications; perform batch and streaming data analysis; and can be used to develop ETL data pipelines. - Source: dev.to / almost 2 years ago

• Streaming Data Alchemy: Apache Kafka Streams Meet Spring Boot

  Apache Flink: A more general-purpose stream processing framework known for its low latency and advanced windowing capabilities. https://flink.apache.org/. - Source: dev.to / almost 2 years ago

• Show HN: Restate, low-latency durable workflows for JavaScript/Java, in Rust

  Restate is built as a sharded replicated state machine similar to how TiKV (https://tikv.org/), Kudu (https://kudu.apache.org/kudu.pdf) or CockroachDB (https://github.com/cockroachdb/cockroach) since it makes it possible to tune the system more easily for different deployment scenarios (on-prem, cloud, cost-effective blob storage). Moreover, it allows for some other cool things like seamlessly moving from one log... - Source: Hacker News / about 2 years ago

• Array Expansion in Flink SQL

  I’ve recently started my journey with Apache Flink. As I learn certain concepts, I’d like to share them. One such "learning" is the expansion of array type columns in Flink SQL. Having used ksqlDB in a previous life, I was looking for functionality similar to the EXPLODE function to "flatten" a collection type column into a row per element of the collection. Because Flink SQL is ANSI compliant, it’s no surprise... - Source: dev.to / about 2 years ago

• Show HN: An SQS Alternative on Postgres

  You should let the Apache Flink team know, they mention exactly-once processing on their home page (under "correctness guarantees") and in their list of features. [0] https://flink.apache.org/ [1] https://flink.apache.org/what-is-flink/flink-applications/#building-blocks-for-streaming-applications. - Source: Hacker News / about 2 years ago

• 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 / over 2 years 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 / over 2 years ago