shardingsphere-elasticjob-ui
flink-kubernetes-operator
shardingsphere-elasticjob-ui | flink-kubernetes-operator | |
---|---|---|
8 | 7 | |
156 | 718 | |
0.6% | 3.3% | |
0.0 | 9.2 | |
10 months ago | 6 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.
shardingsphere-elasticjob-ui
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Five Apache projects you probably didn't know about
ShardingSphere claims to offer an ecosystem able to transform any database into a distributed database system. It acts as a proxy between your code and your database(s). It comes in two flavors:
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Managing Data Residency - the demo
The application uses Apache Shardingsphre to route again depending on the data. If the value computed by the API Gateway is correct, the flow stays "in its lane"; if not, it's routed to the correct database, but with a performance penalty as it's outside its lane.
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Managing Data Residency - concepts and theory
Apache ShardingSphere
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Fuzzy query for CipherColumn | ShardingSphere 5.3.0 Deep Dive
Apache ShardingSphere supports data encryption. By parsing users’ SQL input and rewriting the SQL according to the users’ encryption rules, the original data is encrypted and stored with ciphertext data in the underlying database at the same time.
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Use AWS CloudFormation to create ShardingSphere HA clusters
Apache ShardingSphere is a distributed database ecosystem that can transform any database into a distributed database system, and enhance it with sharding, elastic scaling, encryption features & more.
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ShardingSphere 5.3.0 is released: new features and improvements
ShardingSphere supports a database gateway, but its heterogeneous capability is limited to the logical database in previous versions. This means that all the data sources under a logical database must be of the same database type.
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ShardingSphere-on-Cloud & Pisanix replace Sidecar for a true cloud-native experience
ShardingSphere Official Website
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ElasticJob UI now supports Auth 2.0, OIDC and SAML single sign-on thanks to Casdoor
ElasticJob UI is the visual admin console of ElasticJob, whose target users are developers and DevOps teams rather than users. Generally, it is deployed only in the internal environment and thus its R&D focus more on its features.
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
What are some alternatives?
opentelemetry-tracing - Demo for end-to-end tracing via OpenTelemetry
hugging-chat-api - HuggingChat Python API🤗
shardingsphere-on-cloud - A collection of tools and best practices to take ShardingSphere into the cloud
anomalib - An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
pisanix - A Database Mesh Project Sponsored by SphereEx
ToolBench - [ICLR'24 spotlight] An open platform for training, serving, and evaluating large language model for tool learning.
shardingsphere - Distributed SQL transaction & query engine for data sharding, scaling, encryption, and more - on any database.
CallCMLModel - An example on calling models deployed in CML
FirebaseUI-Android - Optimized UI components for Firebase
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]
shardingsphere-elasticjob - Distributed scheduled job
cdf-workshop