In this presentation, you will get a look under the covers of Amazon Redshift, a fast, fully-managed, petabyte-scale data warehouse service for less than $1,000 per TB per year. Learn how Amazon Redshift uses columnar technology, optimized hardware, and massively parallel processing to deliver fast query performance on data sets ranging in size from hundreds of gigabytes to a petabyte or more. We'll also walk through techniques for optimizing performance and, you’ll hear from a specific customer and their use case to take advantage of fast performance on enormous datasets leveraging economies of scale on the AWS platform.
Speakers:
Ian Meyers, AWS Solutions Architect
Toby Moore, Chief Technology Officer, Space Ape
In this presentation, you will get a look under the covers of Amazon Redshift, a fast, fully-managed, petabyte-scale data warehouse service for less than $1,000 per TB per year. Learn how Amazon Redshift uses columnar technology, optimized hardware, and massively parallel processing to deliver fast query performance on data sets ranging in size from hundreds of gigabytes to a petabyte or more. We'll also walk through techniques for optimizing performance and, you’ll hear from a specific customer and their use case to take advantage of fast performance on enormous datasets leveraging economies of scale on the AWS platform.
Speakers:
Ian Meyers, AWS Solutions Architect
Toby Moore, Chief Technology Officer, Space Ape
hbaseconasia2019 HBCK2: Concepts, trends, and recipes for fixing issues in HB...Michael Stack
Wellington Chevreuil of Cloudera
Track 1: Internals
https://open.mi.com/conference/hbasecon-asia-2019
THE COMMUNITY EVENT FOR APACHE HBASE™
July 20th, 2019 - Sheraton Hotel, Beijing, China
https://hbase.apache.org/hbaseconasia-2019/
Nutanix Fundamentals The Enterprise Cloud CompanyNEXTtour
The problem is that traditional infrastructure was not designed for cloud environments. The enterprise datacenter needs to be re-platformed to meet the needs of the enterprise cloud.
Troubleshooting Tips and Tricks for Database 19c - EMEA Tour Oct 2019Sandesh Rao
This session will focus on 19 troubleshooting tips and tricks for DBA's covering tools from the Oracle Autonomous Health Framework (AHF) like Trace file Analyzer (TFA) to collect , organize and analyze log data , Exachk and orachk to perform mass best practices analysis and automation , Cluster Health Advisor to debug node evictions and calibrate the framework , OSWatcher and its analysis engine , oratop for pinpointing performance issues and many others to make one feel like a rockstar DBA
This presentation talks about the different ways of getting SQL Monitoring reports, reading them correctly, common issues with SQL Monitoring reports - and plenty of Oracle 12c-specific improvements!
Hive on spark is blazing fast or is it finalHortonworks
This presentation was given at the Strata + Hadoop World, 2015 in San Jose.
Apache Hive is the most popular and most widely used SQL solution for Hadoop. To keep pace with Hadoop’s increasingly vital role in the Enterprise, Hive has transformed from a batch-only, high-latency system into a modern SQL engine capable of both batch and interactive queries over large datasets. Hive’s momentum is accelerating: With Spark integration and a shift to in-memory processing on the horizon, Hive continues to expand the boundaries of Big Data.
In this talk the speakers examined Hive performance, past, present and future. In particular they looked at Hive’s origins as a petabyte scale SQL engine.
Through some numbers and graphs, they showed how Hive became 100x faster by moving beyond MapReduce, by vectorizing execution and by introducing a cost-based optimizer.
They detailed and discussed the challenges of scalable SQL on Hadoop.
The looked into Hive’s sub-second future, powered by LLAP and Hive on Spark.
And showed just how fast Hive on Spark really is.
hbaseconasia2019 HBCK2: Concepts, trends, and recipes for fixing issues in HB...Michael Stack
Wellington Chevreuil of Cloudera
Track 1: Internals
https://open.mi.com/conference/hbasecon-asia-2019
THE COMMUNITY EVENT FOR APACHE HBASE™
July 20th, 2019 - Sheraton Hotel, Beijing, China
https://hbase.apache.org/hbaseconasia-2019/
Nutanix Fundamentals The Enterprise Cloud CompanyNEXTtour
The problem is that traditional infrastructure was not designed for cloud environments. The enterprise datacenter needs to be re-platformed to meet the needs of the enterprise cloud.
Troubleshooting Tips and Tricks for Database 19c - EMEA Tour Oct 2019Sandesh Rao
This session will focus on 19 troubleshooting tips and tricks for DBA's covering tools from the Oracle Autonomous Health Framework (AHF) like Trace file Analyzer (TFA) to collect , organize and analyze log data , Exachk and orachk to perform mass best practices analysis and automation , Cluster Health Advisor to debug node evictions and calibrate the framework , OSWatcher and its analysis engine , oratop for pinpointing performance issues and many others to make one feel like a rockstar DBA
This presentation talks about the different ways of getting SQL Monitoring reports, reading them correctly, common issues with SQL Monitoring reports - and plenty of Oracle 12c-specific improvements!
Hive on spark is blazing fast or is it finalHortonworks
This presentation was given at the Strata + Hadoop World, 2015 in San Jose.
Apache Hive is the most popular and most widely used SQL solution for Hadoop. To keep pace with Hadoop’s increasingly vital role in the Enterprise, Hive has transformed from a batch-only, high-latency system into a modern SQL engine capable of both batch and interactive queries over large datasets. Hive’s momentum is accelerating: With Spark integration and a shift to in-memory processing on the horizon, Hive continues to expand the boundaries of Big Data.
In this talk the speakers examined Hive performance, past, present and future. In particular they looked at Hive’s origins as a petabyte scale SQL engine.
Through some numbers and graphs, they showed how Hive became 100x faster by moving beyond MapReduce, by vectorizing execution and by introducing a cost-based optimizer.
They detailed and discussed the challenges of scalable SQL on Hadoop.
The looked into Hive’s sub-second future, powered by LLAP and Hive on Spark.
And showed just how fast Hive on Spark really is.
29. NoSQL timeline
29
1998 2009 Current
No SQL No Only SQL No,SQL
RDB
RDBMS
NoSQL API
non-
Relational
DBMS
NoSQL API
RDB
RDBMS
SQL
non-
Relational
DBMS
NoSQL API
RDB
RDBMS
SQL
SQL
67. 高可用性(High Availability) - 同步複製
• Redundancy
• Active – Passive
• Active – Active
Web
Server
DB
DB
Read Write
Replication(Sync)
Primary
Standby
A1
A1
write
read
68. 高可用性(High Availability) - Failover
• Redundancy
• Active – Passive
• Active – Active
Web
Server
DB
DB
Read Write
Primary
Primary
Read Write
A1
A1
write
read
69. 高可用性(High Availability) - 讀寫分離
• Redundancy
• Active – Passive
• Active – Active
Web
Server
DB
DB
Read Write
Read Only
Replication(Async)
A1
A1
A1
A2
write
read
70. 高可用性(High Availability) - 非同步複製
• Redundancy
• Active – Passive
• Active – Active
Web
Server
DB
DB
Read Write
Read Only
Replication(Async)
A2
A1 A2
A2
A1
write
read
71. 高可用性(High Availability) - 非同步複製
• Redundancy
• Active – Passive
• Active – Active
Web
Server
DB
DB
Read Write
Read Only
Replication(Async)
A2
A2
A2
A2
write
read
72. 高可用性(High Availability)
• Redundancy
• Active – Passive
• Active – Active
Web
Server
DB
DB
Read Write
Replication(Async)
DB
Replication(Sync)
Primary
Standby
write
read
74. AWS Cloud
Availability Zone 1 Availability Zone 3
Availability Zone 2
Region
EBS
Amazon EBS
snapshot
Amazon S3
bucket
DB on instance
M
log
75. AWS Cloud
Availability Zone 1 Availability Zone 3
Availability Zone 2
Region
EBS
Amazon EBS
snapshot
Amazon S3
bucket
DB on instance DB on instance
M S
EBS
synchronous
log
76. AWS Cloud
Availability Zone 1 Availability Zone 3
Availability Zone 2
Region
EBS
Amazon EBS
snapshot
Amazon S3
bucket
DB on instance DB on instance
M M
EBS
77. AWS Cloud
Availability Zone 1 Availability Zone 3
Availability Zone 2
Region
Amazon EBS
snapshot
Amazon S3
bucket
M
EBS EBS
S
DB on instance DB on instance
log
78. AWS Cloud
Availability Zone 1 Availability Zone 3
Availability Zone 2
Region
EBS
Amazon EBS
snapshot
Amazon S3
bucket
EBS
asynchronous
log
DB on instance DB on instance
M R
79. AWS Cloud
Availability Zone 1 Availability Zone 3
Availability Zone 2
Region
EBS
Amazon EBS
snapshot
Amazon S3
bucket
DB on instance DB on instance
M S
EBS
synchronous
log
EBS
asynchronous
DB on instance
R
80. AWS Cloud
Availability Zone 1 Availability Zone 3
Availability Zone 2
Region
EBS
Amazon EBS
snapshot
Amazon S3
bucket
DB on instance DB on instance
M S
EBS
synchronous
log
EBS
asynchronous
DB on instance
R
Availability Zone 1
Region
EBS
DB on instance
R
83. AWS Cloud
Availability Zone 1 Availability Zone 3
Availability Zone 2
Region
Aurora Instance Aurora Instance
M R
Node1 Node2 Node3 Node4 Node5 Node6
Distributed Storage Volumes
86. AWS Cloud
Availability Zone 1 Availability Zone 3
Availability Zone 2
Region
Aurora Instance Aurora Instance
M R
Node1 Node2 Node3 Node4 Node5 Node6
Distributed Storage Volumes
EBS
M R
EBS
Asynchronous Cache
Replication
Asynchronous Replication
MySQL Instance MySQL Instance
87. Fault Tolerant, High Availability and
Disaster Recovery
http://www.pbenson.net/2014/02/the-difference-between-fault-tolerance-high-availability-disaster-recovery/
88. 7種上雲的策略- 7 Rs
• Retain
• 保持原狀,不移植到雲端環境
• Retire
• 淘汰現有系統,不移植到雲端環境
• Rehost - Lift and Shift
• 工作負載平移,直接將應用程式平移到雲端環境
• Replatform
• 變更部分應用程式架構,用以因應基礎架構雲端化的變動
• Relocate
• VMware Cloud on AWS
• Refactor(Restructure)
• 重新架構應用程式以利用雲端環境的優勢
• Repurchase(Replace)
• 淘汰現有應用程式,改為使用雲服務產品
資料來源:Get started on your migration business case - 2019 AWS re:Invent
91. 不同策略的使用比例
• Rehost/Replatform - 70%
• Refactor - 10%
• Retire - 5%
• All other - 15%
資料來源:Get started on your migration business case - 2019 AWS re:Invent
102. AWS SCT評估報告
(From MS SQL Server to MySQL)
https://docs.aws.amazon.com/zh_tw/SchemaConversionTool/latest/userguide/
CHAP_UserInterface.html#CHAP_UserInterface.Overview.ProjectWindow
Simple:1小時內可以完成
Medium:1~4小時內可以完成
Signification:需要4小時以上時間