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m.blog.naver.com/sundooedu/221719965372 쿠버네티스(Kubernetes) 컨테이너 확장 자동화 기술, KEDA(케다) 마이크로소프트가 공개한 KEDA 1.0은 이벤트 기반 컨테이너 오토 스케일링을 지원하는 쿠버네티스용 오... blog.naver.com keda.sh/ KEDA Application autoscaling made simple keda.sh docs.microsoft.com/ko-kr/azure/azure-functions/functions-kubernetes-keda Kubernetes의 Azure Functions 및 KEDA Kubernetes-based event-driven autoscaling(KEDA, Kubernetes 기반 이벤트 기반 자동 크..

www.nifi.rocks/expression-language-cheat-sheet/ Apache Nifi Expression Language Cheat Sheet Apache Nifi Expression language allows dynmic values in functional fields. This is a short reference to find useful functions and examples. For a full reference see the offical documentation. Download a PDF version Reserved Charecters If these charecters a www.nifi.rocks

m.blog.naver.com/PostView.nhn?blogId=oper13357799&logNo=220939422191&targetKeyword=&targetRecommendationCode=1 NiFi로 일몰 일출 배치 작업 수행하기 Intro 오늘은 하는 일이 많아 잡담대신 Intro에서 전체적인 내용에 대해 말씀드려야 겠습니다. 본 포스트... blog.naver.com 1. 각 프로세서의 Concurrent Tasks : 동시 작업 수. 권장 = 3 ~ 5. 아웃풋 프로세스는 30 ~ 50 이어도 괜찮음. - ReplaceText 같은 트랜스포메이션 프로세스들은 인/아웃풋 보다 상당한 스펙을 요구함. 10 이 적당. - 노드당 최대 쓰레드수라고 봐도 무방. 2. Controllers 설정의 '..

- 들어온 데이터의 Json 을 Tree 구조로 Parsing 해준다. Json Schema https://bigquery-json-schema-generator.com/ BigQuery JSON Schema Generator bigquery-json-schema-generator.com

slacker.ro/2020/04/09/processing-one-billion-events-per-second-with-nifi/ Processing one billion events per second with NiFi Ever wonder how fast Apache NiFi is? Ever wonder how well NiFi scales? A single NiFi cluster can process trillions of events and petabytes of data per day with full data provenance and lineage. Her… slacker.ro

www.eksworkshop.com/advanced/410_batch/ EKSworkshop.com Amazon EKS Workshop In this workshop, we will explore multiple ways to configure VPC, ALB, and EC2 Kubernetes workers, and Amazon Elastic Kubernetes Service. www.eksworkshop.com argoproj.github.io/argo/quick-start/ Quick Start - Argo Workflows - The workflow engine for Kubernetes  Quick Start To see how Argo works, you can install it and r..

pyarrow import pyarrow.parquet as pq pfile = pq.read_table("000.parquet") print("Column names: {}".format(pfile.column_names)) print("Schema: {}".format(pfile.schema)) python3 test.py Column names: ['order_status_id', 'source_id', 'source_system_status', 'order_status', 'order_status_group'] Schema: order_status_id: int32 -- field metadata -- PARQUET:field_id: '1' source_id: int16 -- field metad..

https://gyrfalcon.tistory.com/entry/Flink-1-%EC%86%8C%EA%B0%9C-Basic-Concept Flink 시작하기 #1 소개 (Basic Concept) #Flink란? stream processing framework for distributed, high-performing, always-available, and accurate data streaming applications. 라고 소개되고 있다. 간단히 말해 최근 많이 사용되는 apache Storm,.. gyrfalcon.tistory.com https://medium.com/swlh/run-a-stateful-streaming-service-with-apache-flink-and-rocksdb-74..

https://towardsdatascience.com/airflow-vs-luigi-vs-argo-vs-mlflow-vs-kubeflow-b3785dd1ed0c Airflow vs. Luigi vs. Argo vs. MLFlow vs. KubeFlow Choosing a task orchestration tool towardsdatascience.com

http://schemaspy.org/ SchemaSpy • Database Documentation Built Easy. Documentation Here you will find more detials about SchemaSpy option and configuration How many times you are putting off for later preparation of the documentation database ? How many times have you had to do it but never enough time ? Building documentat schemaspy.org https://github.com/schemacrawler/SchemaCrawler schemacrawl..