flink-statefun
Apache Cassandra
flink-statefun | Apache Cassandra | |
---|---|---|
18 | 36 | |
495 | 8,535 | |
1.4% | 0.6% | |
3.2 | 9.9 | |
13 days ago | 5 days ago | |
Java | Java | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
flink-statefun
-
flink-statefun VS quix-streams - a user suggested alternative
2 projects | 7 Dec 2023
-
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.
-
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.
-
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 engagement records for easy access. Records are indexed on user ID and ad campaign ID to allow for efficient retrieval. The use of stream processing (Apache Flink) ensures this whole process happens within minutes, and keeps audiences up to date in real-time. The following high-level diagram summarizes the process:
-
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.
-
How to choose the right streaming database
Apache Flink.
-
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.
-
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.
-
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.
-
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 frameworks. You could use them to build streaming applications with stateful information, sliding windows, etc.
Apache Cassandra
-
System Design: Databases and DBMS
Apache Cassandra
-
How to Choose the Right MQTT Data Storage for Your Next Project
Apache Cassandra{:target="_blank"} is a highly scalable and fault-tolerant database that can handle large volumes of data across multiple nodes or clusters. It provides fast read and write operations, making it suitable for real-time analytics or applications with high throughput requirements.
- 10+ Open-Source Projects For Web Developers In 2023
-
Database 101: Data Consistency for Beginners
Wide Column: Apache Cassandra, ScyllaDB and DynamoDB
-
In One Minute : Hadoop
Cassandra, a replicated, fault-tolerant, decentralized and scalable database system.
-
Build Your First App with JavaScript, Node.js, and DataStax Astra DB
A popular database you might already be familiar with is Apache Cassandra®, which powers high-performing applications for thousands of companies including Hulu, Netflix, Spotify, and Apple. While this free, open-source database is known for its high availability, scalability, and resilience; the downside is that it’s also notoriously complex to set up and manage.
- Reducing logging cost by two orders of magnitude using CLP
-
Baeldung Series Part 2: Build a Dashboard With Cassandra, Astra and CQL – Mapping Event Data
In our previous article, we looked at augmenting our dashboard to store and display individual events from the Avengers using DataStax Astra, a serverless DBaaS powered by Apache Cassandra using Stargate to offer additional APIs for working with it.
-
System Design: CAP theorem
Example: Apache Cassandra, CouchDB.
-
Deploy a TikTok Clone with Node.js, Netlify, and DataStax Astra DB
For our TikTok database, we’re using DataStax Astra DB: a cloud-based database that fully manages Apache Cassandra®, one of the most robust and scalable NoSQL databases around.
What are some alternatives?
opensky-api - Python and Java bindings for the OpenSky Network REST API
Druid - Apache Druid: a high performance real-time analytics database.
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
LevelDB - LevelDB is a fast key-value storage library written at Google that provides an ordered mapping from string keys to string values.
debezium - Change data capture for a variety of databases. Please log issues at https://issues.redhat.com/browse/DBZ.
Scylla - NoSQL data store using the seastar framework, compatible with Apache Cassandra
redpanda - Redpanda is a streaming data platform for developers. Kafka API compatible. 10x faster. No ZooKeeper. No JVM!
delta - An open-source storage framework that enables building a Lakehouse architecture with compute engines including Spark, PrestoDB, Flink, Trino, and Hive and APIs
Apache Pulsar - Apache Pulsar - distributed pub-sub messaging system
Apache HBase - Apache HBase
faust - Python Stream Processing. A Faust fork
Event Store - EventStoreDB, the event-native database. Designed for Event Sourcing, Event-Driven, and Microservices architectures