Data Engineering: Elastic, Low-Cost Data Processing in the CloudCloudera, Inc.
3 Things to Learn About:
*On-premises versus the cloud: What’s the same and what’s different?
*Benefits of data processing in the cloud
*Best practices and architectural considerations
Hive, Impala, and Spark, Oh My: SQL-on-Hadoop in Cloudera 5.5Cloudera, Inc.
Inefficient data workloads are all too common across enterprises - causing costly delays, breakages, hard-to-maintain complexity, and ultimately lost productivity. For a typical enterprise with multiple data warehouses, thousands of reports, and hundreds of thousands of ETL jobs being executed every day, this loss of productivity is a real problem. Add to all of this the complex handwritten SQL queries, and there can be nearly a million queries executed every month that desperately need to be optimized, especially to take advantage of the benefits of Apache Hadoop. How can enterprises dig through their workloads and inefficiencies to easily see which are the best fit for Hadoop and what’s the fastest path to get there?
Cloudera Navigator Optimizer is the solution - analyzing existing SQL workloads to provide instant insights into your workloads and turns that into an intelligent optimization strategy so you can unlock peak performance and efficiency with Hadoop. As the newest addition to Cloudera’s enterprise Hadoop platform, and now available in limited beta, Navigator Optimizer has helped customers profile over 1.5 million queries and ultimately save millions by optimizing for Hadoop.
Data Engineering: Elastic, Low-Cost Data Processing in the CloudCloudera, Inc.
3 Things to Learn About:
*On-premises versus the cloud: What’s the same and what’s different?
*Benefits of data processing in the cloud
*Best practices and architectural considerations
Hive, Impala, and Spark, Oh My: SQL-on-Hadoop in Cloudera 5.5Cloudera, Inc.
Inefficient data workloads are all too common across enterprises - causing costly delays, breakages, hard-to-maintain complexity, and ultimately lost productivity. For a typical enterprise with multiple data warehouses, thousands of reports, and hundreds of thousands of ETL jobs being executed every day, this loss of productivity is a real problem. Add to all of this the complex handwritten SQL queries, and there can be nearly a million queries executed every month that desperately need to be optimized, especially to take advantage of the benefits of Apache Hadoop. How can enterprises dig through their workloads and inefficiencies to easily see which are the best fit for Hadoop and what’s the fastest path to get there?
Cloudera Navigator Optimizer is the solution - analyzing existing SQL workloads to provide instant insights into your workloads and turns that into an intelligent optimization strategy so you can unlock peak performance and efficiency with Hadoop. As the newest addition to Cloudera’s enterprise Hadoop platform, and now available in limited beta, Navigator Optimizer has helped customers profile over 1.5 million queries and ultimately save millions by optimizing for Hadoop.
Impala 2.0 - The Best Analytic Database for HadoopCloudera, Inc.
A look at why SQL access in Hadoop is critical and the benefits of a native Hadoop analytic database, what’s new with Impala 2.0 and some of the recent performance benchmarks, some common Impala use cases and production customer stories, and insight into what’s next for Impala.
An Introduction to Cloudera Impala, shows how Impala works, and the internal processing of query of Impala, including architecture, frontend, query compilation, backend, code generation, HDFS-related stuff and performance comparison.
Impala Architecture Presentation at Toronto Hadoop User Group, in January 2014 by Mark Grover.
Event details:
http://www.meetup.com/TorontoHUG/events/150328602/
The document outlines topics covered in "The Impala Cookbook" published by Cloudera. It discusses physical and schema design best practices for Impala, including recommendations for data types, partition design, file formats, and block size. It also covers estimating and managing Impala's memory usage, and how to identify the cause when queries exceed memory limits.
Scala, Spring-Boot, JPA를 활용한 웹 애플리케이션 개발 과정에 대해 다룬다. Spring-Boot와 JPA 조합만으로도 생산성 있는 웹 애플리케이션 개발이 가능하다. 이 조합만으로도 충분히 의미가 있지만 여기에 Scala라는 약간은 불편한 듯 보이는 언어를 도입함으로써 얻을 수 있는 즐거움을 공유한다. Spring-Boot + JPA 조합에 Scala를 적용하면서의 좌충우돌 경험담을 전한다.
AWS EMR을 사용하면서 비용을 최적화하기 위해 필요한 다양한 관점의 방안을 검토하여 정리한 자료.
비용 최적화 대상은 zeppelin/jupyter notebook과 apache spark를 활용하는 서비스를 대상으로 하였으며, 해당 작업이 aws emr에서 어떻게 동작하는지 내부 구조을 파악하여 확인함.
- AWS EMR이란?
- AWS EMR의 과금 방식은?
- 어떻게 비용을 최적화 할 것인가?
- 최적의 EMR 클러스터 구성 방안
- 가성비 높은 Instance 선정 방안
- Apache Spark 성능 개선 방안
가장 중요한 것은 실행할 job의 자원사용량/성능을 모니터링하고, 이에 맞게 자원을 최적화하는 것이 필요함.
[Games on AWS 2019] AWS 사용자를 위한 만랩 달성 트랙 | Aurora로 게임 데이터베이스 레벨 업! - 김병수 AWS ...Amazon Web Services Korea
Amazon Aurora Database는 오픈소스의 개방성과 상용 데이터베이스의 성능과 안정성을 모두 제공하는 관리형 데이터베이스 서비스입니다. Amazon Aurora Database는 처음 소개된 이후로 계속 기능을 추가하며 진화해 왔습니다. Amazon Aurora의 성능과 새롭게 업데이트된 기능들을 게임사에 적용할 수 있는 사용 사례와 함께 소개합니다.
15. Log Message Interpretation
Unknown disk id. This will negatively affect performance.
Tracking block locality가 활성화 되지 않은 상태이다.
Check your hdfs settings to enable block location metadata
Unable to load native-hadoop library for your platform...
Native check-summing이 활성화 되
using builtin-java classes where applicable
쿼리를 실행했을 때 앞에서 설명한 설정이 되어있지 않을 경우 나타
나는 로그 메세지