flink-kubernetes-operator
hudi
flink-kubernetes-operator | hudi | |
---|---|---|
8 | 20 | |
729 | 5,114 | |
4.3% | 2.0% | |
9.2 | 9.9 | |
about 9 hours ago | 4 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-kubernetes-operator
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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, implementing a real-time anomaly detection model in Kafka Streams would require translating Python code into Java, slowing down pipeline performance, and requiring a complex initial setup.
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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.
- FLaNK Stack Weekly 22 January 2024
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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.
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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.
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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.
- FLaNK Stack Weekly for 07August2023
hudi
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Getting Started with Flink SQL, Apache Iceberg and DynamoDB Catalog
Apache Iceberg is one of the three types of lakehouse, the other two are Apache Hudi and Delta Lake.
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The "Big Three's" Data Storage Offerings
Structured, Semi-structured and Unstructured can be stored in one single format, a lakehouse storage format like Delta, Iceberg or Hudi (assuming those don't require low-latency SLAs like subsecond).
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Data-eng related highlights from the latest Thoughtworks Tech Radar
Apache Hudi
- For those of you with Lakehouse Architectures, how do you handle duplicate records?
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AWS ACID data lakehouse
Try Apache Hudi, it is fully integrated with AWS and offers almost everything that you requested.
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Data n00b looking for guidance on how to setup data lake/warehouse
the corresponding kafka topics have 30d retention and I intend on having s3 sink connector for long term storage (open to other ideas here too, I noticed theres a hudi connector also)
- apache/hudi: Upserts, Deletes And Incremental Processing on Big Data.
- Big Data file formats
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How-to-Guide: Contributing to Open Source
Apache Hudi
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What do you use for Data versioning?
You could have a look at Apache Hudi - especially if you're running your Data Pipelines using Spark or Flink.
What are some alternatives?
hugging-chat-api - HuggingChat Python API🤗
iceberg - Apache Iceberg
anomalib - An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
kudu - Mirror of Apache Kudu
ToolBench - [ICLR'24 spotlight] An open platform for training, serving, and evaluating large language model for tool learning.
Trino - Official repository of Trino, the distributed SQL query engine for big data, former
CallCMLModel - An example on calling models deployed in CML
debezium - Change data capture for a variety of databases. Please log issues at https://issues.redhat.com/browse/DBZ.
Qwen-7B - The official repo of Qwen (通义千问) chat & pretrained large language model proposed by Alibaba Cloud. [Moved to: https://github.com/QwenLM/Qwen]
pinot - Apache Pinot - A realtime distributed OLAP datastore
cdf-workshop
delta - An open-source storage framework that enables building a Lakehouse architecture with compute engines including Spark, PrestoDB, Flink, Trino, and Hive and APIs