Long Chen
Track 3: Applications
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/
hbaseconasia2019 Recent work on HBase at PinterestMichael Stack
Lianghong Xu
Track 3: Applications
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/
hbaseconasia2019 Phoenix Practice in China Life Insurance Co., LtdMichael Stack
Yechao Chen
Track 3: Applications
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/
TianHang Tang
Track 3: Applications
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/
hbaseconasia2019 The Practice in trillion-level Video Storage and billion-lev...Michael Stack
Xu Ming
Track 3: Applications
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/
Andrew Cheng
Track 3: Applications
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/
hbaseconasia2019 Spatio temporal Data Management based on Ali-HBase Ganos and...Michael Stack
Fei Xiao of Alibaba
Track 2: Ecology and Solutions
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/
hbaseconasia2019 Bridging the Gap between Big Data System Software Stack and ...Michael Stack
Huan-Ping Su (蘇桓平), Yi-Sheng Lien (連奕盛) National Cheng Kung University
Track 2: Ecology and Solutions
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/
hbaseconasia2019 Pharos as a Pluggable Secondary Index ComponentMichael Stack
Lei Wang China Everbright Bank
Track 2: Ecology and Solutions
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/
hbaseconasia2019 Recent work on HBase at PinterestMichael Stack
Lianghong Xu
Track 3: Applications
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/
hbaseconasia2019 Phoenix Practice in China Life Insurance Co., LtdMichael Stack
Yechao Chen
Track 3: Applications
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/
TianHang Tang
Track 3: Applications
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/
hbaseconasia2019 The Practice in trillion-level Video Storage and billion-lev...Michael Stack
Xu Ming
Track 3: Applications
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/
Andrew Cheng
Track 3: Applications
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/
hbaseconasia2019 Spatio temporal Data Management based on Ali-HBase Ganos and...Michael Stack
Fei Xiao of Alibaba
Track 2: Ecology and Solutions
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/
hbaseconasia2019 Bridging the Gap between Big Data System Software Stack and ...Michael Stack
Huan-Ping Su (蘇桓平), Yi-Sheng Lien (連奕盛) National Cheng Kung University
Track 2: Ecology and Solutions
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/
hbaseconasia2019 Pharos as a Pluggable Secondary Index ComponentMichael Stack
Lei Wang China Everbright Bank
Track 2: Ecology and Solutions
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/
hbaseconasia2019 Phoenix Improvements and Practices on Cloud HBase at AlibabaMichael Stack
Yun Zhang
Track 2: Ecology and Solutions
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/
Junhong Xu of Xiaomi
Track 2: Ecology and Solutions
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/
hbaseconasia2019 BigData NoSQL System: ApsaraDB, HBase and SparkMichael Stack
Wei Li of Alibaba
Track 2: Ecology and Solutions
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/
hbaseconasia2019 Test-suite for Automating Data-consistency checks on HBaseMichael Stack
Pradeep S, Mallikarjun V of Flipkart
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/
hbaseconasia2019 Distributed Bitmap Index SolutionMichael Stack
Xingjun Hao of Huawei
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/
hbaseconasia2019 HBase Bucket Cache on Persistent MemoryMichael Stack
Anoop Sam John, Ramkrishna S Vasudevan, and Xu Kai of Intel
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/
hbaseconasia2019 The Procedure v2 Implementation of WAL Splitting and ACLMichael Stack
Mei Yi of Xiaomi
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/
hbaseconasia2019 BDS: A data synchronization platform for HBaseMichael Stack
熊嘉男
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/
hbaseconasia2019 Further GC optimization for HBase 2.x: Reading HFileBlock in...Michael Stack
Anoop Sam John of Intel and Zheng Hu of Alibaba
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/
hbaseconasia2019 HBCK2: Concepts, trends, and recipes for fixing issues in HB...Michael Stack
The document discusses HBCK2, a tool for fixing issues in HBase 2. Some key points:
1. HBCK2 is simpler than HBCK1, with fewer fix commands and no diagnosis commands. It requires a deeper understanding of HBase internals.
2. HBCK2 commands are master-oriented and fix issues one at a time. Common issues include regions not online, stuck procedures, and tables in the wrong state.
3. Recipes are provided to fix specific issues like missing meta regions or regions in transition using HBCK2 commands like assigns and bypass.
4. HBCK2 is still a work in progress but contributions are welcome
Keynote given by Duo Zhang of Xiaomi and Chunhui Shen of Alibab
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/
HBaseConAsia2018 Track3-1: Serving billions of queries in millisecond latenciesMichael Stack
This document discusses how Bloomberg uses HBase to serve billions of queries with millisecond latency. It covers HBase principles like being an ordered key-value store and providing ACID transactions. It also discusses modeling data for HBase, including dealing with data and query skew. Implementation details covered include caching, block size tuning, column families, and compaction. The overall goal is to optimize HBase for Bloomberg's low-latency data storage and retrieval needs.
HBaseConAsia2018 Track1-3: HBase at XiaomiMichael Stack
This document summarizes Xiaomi's implementation and use of HBase for data storage. It discusses Xiaomi's HBase clusters across multiple public cloud providers and data centers. It also describes Xiaomi's approaches to multi-tenancy, quota and throttling, synchronous replication between clusters, and high availability in the case of node or cluster failures. Synchronous replication provides stronger consistency guarantees but with some performance overhead compared to asynchronous replication.
HBaseConAsia2018 Track1-1: Use CCSMap to improve HBase YGC timeMichael Stack
CCSMap is a new data structure introduced by Alibaba to improve the performance of HBase. It aims to reduce the overhead of the default Java ConcurrentSkipListMap (CSLM) data structure and improve young garbage collection times. CCSMap chunks data into fixed size blocks for better memory management and uses direct pointers between nodes for faster access. It also provides various configuration options. Alibaba has achieved significant performance gains using CCSMap in HBase, including reduced young GC times, and it continues working to integrate CCSMap further and add new features.
HBaseConAsia2018 Track2-6: Scaling 30TB's of data lake with Apache HBase and ...Michael Stack
This document summarizes a presentation on scaling a 30 TB data lake using Apache HBase and Scala. It introduces Apache HBase and Spark as technologies for building fast data platforms. It then describes a case study where they were used to architect a retail analytics platform capable of processing 4.6 billion events weekly from 30 TB of historical data. Key aspects included using HBase for data deduplication and as a master data management system, and connecting Spark to HBase using a Scala DSL for efficient querying and updates at scale. Performance was improved 5x by reengineering the data pipeline to be highly concurrent and asynchronous.
HBaseConAsia2018 Track3-3: HBase at China Life InsuranceMichael Stack
This document summarizes an HBase practice presentation at China Life Insurance Co., Ltd. It discusses scenarios for HBase integration, processing, querying, and exporting data. It also covers optimizations to the HBase cluster configuration and for writing and reading. Problems addressed include table copy failures and compactions that never end. Future work may involve using Phoenix for real-time querying and integrating real-time data sources like Kafka.
HBaseConAsia2018 Track3-2: HBase at China TelecomMichael Stack
HBase is used at China Telecom for various applications including persistence for streaming jobs, online reading and writing, and as a data store for their core system. They operate several HBase clusters storing over 500 TB of data ingesting 1 TB per day. They monitor HBase using Ganglia for basic metrics and Zabbix for critical alerts. When issues arise, such as a system hang, they investigate debug cases and perform optimizations like changing the garbage collector from CMS to G1 and implementing read/write splitting.
HBaseConAsia2018: Track2-5: JanusGraph-Distributed graph database with HBaseMichael Stack
This document provides an introduction to JanusGraph, an open source distributed graph database that can be used with Apache HBase for storage. It begins with background on graph databases and their structures, such as vertices, edges, properties, and different storage models. It then discusses JanusGraph's architecture, support for the TinkerPop graph computing framework, and schema and data modeling capabilities. Details are given on partitioning graphs across servers and using different indexing approaches. The document concludes by explaining why HBase is a good storage backend for JanusGraph and providing examples of how the data model would be structured within HBase.
HBaseConAsia2018 Keynote 2: Recent Development of HBase in Alibaba and CloudMichael Stack
New Journey of HBase in Alibaba and Cloud discusses Alibaba's use of HBase over 8 years and improvements made. Key points discussed include:
- Alibaba began using HBase in 2010 and has since contributed to the open source community while developing internal improvements.
- Challenges addressed include JVM garbage collection pauses, separating computing and storage, and adding cold/hot data tiering. A diagnostic system was also created.
- Alibaba uses HBase across many core scenarios and has integrated it with other databases in a multi-model approach to support different workloads.
- Benefits of running HBase on cloud include flexibility, cost savings, and making it
- Apache HBase 2.0.0 is a major new release that was over 4 years in development and focused on compatibility, scale, and performance improvements.
- Key changes include a new master region assignment system, off-heap read/write paths, and in-memory compaction.
- The goals were to support larger clusters with better resource utilization while fixing issues with the previous master region assignment system.
hbaseconasia2019 Phoenix Improvements and Practices on Cloud HBase at AlibabaMichael Stack
Yun Zhang
Track 2: Ecology and Solutions
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/
Junhong Xu of Xiaomi
Track 2: Ecology and Solutions
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/
hbaseconasia2019 BigData NoSQL System: ApsaraDB, HBase and SparkMichael Stack
Wei Li of Alibaba
Track 2: Ecology and Solutions
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/
hbaseconasia2019 Test-suite for Automating Data-consistency checks on HBaseMichael Stack
Pradeep S, Mallikarjun V of Flipkart
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/
hbaseconasia2019 Distributed Bitmap Index SolutionMichael Stack
Xingjun Hao of Huawei
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/
hbaseconasia2019 HBase Bucket Cache on Persistent MemoryMichael Stack
Anoop Sam John, Ramkrishna S Vasudevan, and Xu Kai of Intel
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/
hbaseconasia2019 The Procedure v2 Implementation of WAL Splitting and ACLMichael Stack
Mei Yi of Xiaomi
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/
hbaseconasia2019 BDS: A data synchronization platform for HBaseMichael Stack
熊嘉男
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/
hbaseconasia2019 Further GC optimization for HBase 2.x: Reading HFileBlock in...Michael Stack
Anoop Sam John of Intel and Zheng Hu of Alibaba
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/
hbaseconasia2019 HBCK2: Concepts, trends, and recipes for fixing issues in HB...Michael Stack
The document discusses HBCK2, a tool for fixing issues in HBase 2. Some key points:
1. HBCK2 is simpler than HBCK1, with fewer fix commands and no diagnosis commands. It requires a deeper understanding of HBase internals.
2. HBCK2 commands are master-oriented and fix issues one at a time. Common issues include regions not online, stuck procedures, and tables in the wrong state.
3. Recipes are provided to fix specific issues like missing meta regions or regions in transition using HBCK2 commands like assigns and bypass.
4. HBCK2 is still a work in progress but contributions are welcome
Keynote given by Duo Zhang of Xiaomi and Chunhui Shen of Alibab
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/
HBaseConAsia2018 Track3-1: Serving billions of queries in millisecond latenciesMichael Stack
This document discusses how Bloomberg uses HBase to serve billions of queries with millisecond latency. It covers HBase principles like being an ordered key-value store and providing ACID transactions. It also discusses modeling data for HBase, including dealing with data and query skew. Implementation details covered include caching, block size tuning, column families, and compaction. The overall goal is to optimize HBase for Bloomberg's low-latency data storage and retrieval needs.
HBaseConAsia2018 Track1-3: HBase at XiaomiMichael Stack
This document summarizes Xiaomi's implementation and use of HBase for data storage. It discusses Xiaomi's HBase clusters across multiple public cloud providers and data centers. It also describes Xiaomi's approaches to multi-tenancy, quota and throttling, synchronous replication between clusters, and high availability in the case of node or cluster failures. Synchronous replication provides stronger consistency guarantees but with some performance overhead compared to asynchronous replication.
HBaseConAsia2018 Track1-1: Use CCSMap to improve HBase YGC timeMichael Stack
CCSMap is a new data structure introduced by Alibaba to improve the performance of HBase. It aims to reduce the overhead of the default Java ConcurrentSkipListMap (CSLM) data structure and improve young garbage collection times. CCSMap chunks data into fixed size blocks for better memory management and uses direct pointers between nodes for faster access. It also provides various configuration options. Alibaba has achieved significant performance gains using CCSMap in HBase, including reduced young GC times, and it continues working to integrate CCSMap further and add new features.
HBaseConAsia2018 Track2-6: Scaling 30TB's of data lake with Apache HBase and ...Michael Stack
This document summarizes a presentation on scaling a 30 TB data lake using Apache HBase and Scala. It introduces Apache HBase and Spark as technologies for building fast data platforms. It then describes a case study where they were used to architect a retail analytics platform capable of processing 4.6 billion events weekly from 30 TB of historical data. Key aspects included using HBase for data deduplication and as a master data management system, and connecting Spark to HBase using a Scala DSL for efficient querying and updates at scale. Performance was improved 5x by reengineering the data pipeline to be highly concurrent and asynchronous.
HBaseConAsia2018 Track3-3: HBase at China Life InsuranceMichael Stack
This document summarizes an HBase practice presentation at China Life Insurance Co., Ltd. It discusses scenarios for HBase integration, processing, querying, and exporting data. It also covers optimizations to the HBase cluster configuration and for writing and reading. Problems addressed include table copy failures and compactions that never end. Future work may involve using Phoenix for real-time querying and integrating real-time data sources like Kafka.
HBaseConAsia2018 Track3-2: HBase at China TelecomMichael Stack
HBase is used at China Telecom for various applications including persistence for streaming jobs, online reading and writing, and as a data store for their core system. They operate several HBase clusters storing over 500 TB of data ingesting 1 TB per day. They monitor HBase using Ganglia for basic metrics and Zabbix for critical alerts. When issues arise, such as a system hang, they investigate debug cases and perform optimizations like changing the garbage collector from CMS to G1 and implementing read/write splitting.
HBaseConAsia2018: Track2-5: JanusGraph-Distributed graph database with HBaseMichael Stack
This document provides an introduction to JanusGraph, an open source distributed graph database that can be used with Apache HBase for storage. It begins with background on graph databases and their structures, such as vertices, edges, properties, and different storage models. It then discusses JanusGraph's architecture, support for the TinkerPop graph computing framework, and schema and data modeling capabilities. Details are given on partitioning graphs across servers and using different indexing approaches. The document concludes by explaining why HBase is a good storage backend for JanusGraph and providing examples of how the data model would be structured within HBase.
HBaseConAsia2018 Keynote 2: Recent Development of HBase in Alibaba and CloudMichael Stack
New Journey of HBase in Alibaba and Cloud discusses Alibaba's use of HBase over 8 years and improvements made. Key points discussed include:
- Alibaba began using HBase in 2010 and has since contributed to the open source community while developing internal improvements.
- Challenges addressed include JVM garbage collection pauses, separating computing and storage, and adding cold/hot data tiering. A diagnostic system was also created.
- Alibaba uses HBase across many core scenarios and has integrated it with other databases in a multi-model approach to support different workloads.
- Benefits of running HBase on cloud include flexibility, cost savings, and making it
- Apache HBase 2.0.0 is a major new release that was over 4 years in development and focused on compatibility, scale, and performance improvements.
- Key changes include a new master region assignment system, off-heap read/write paths, and in-memory compaction.
- The goals were to support larger clusters with better resource utilization while fixing issues with the previous master region assignment system.
Meet up Milano 14 _ Axpo Italia_ Migration from Mule3 (On-prem) to.pdfFlorence Consulting
Quattordicesimo Meetup di Milano, tenutosi a Milano il 23 Maggio 2024 dalle ore 17:00 alle ore 18:30 in presenza e da remoto.
Abbiamo parlato di come Axpo Italia S.p.A. ha ridotto il technical debt migrando le proprie APIs da Mule 3.9 a Mule 4.4 passando anche da on-premises a CloudHub 1.0.
Instagram has become one of the most popular social media platforms, allowing people to share photos, videos, and stories with their followers. Sometimes, though, you might want to view someone's story without them knowing.
Understanding User Behavior with Google Analytics.pdfSEO Article Boost
Unlocking the full potential of Google Analytics is crucial for understanding and optimizing your website’s performance. This guide dives deep into the essential aspects of Google Analytics, from analyzing traffic sources to understanding user demographics and tracking user engagement.
Traffic Sources Analysis:
Discover where your website traffic originates. By examining the Acquisition section, you can identify whether visitors come from organic search, paid campaigns, direct visits, social media, or referral links. This knowledge helps in refining marketing strategies and optimizing resource allocation.
User Demographics Insights:
Gain a comprehensive view of your audience by exploring demographic data in the Audience section. Understand age, gender, and interests to tailor your marketing strategies effectively. Leverage this information to create personalized content and improve user engagement and conversion rates.
Tracking User Engagement:
Learn how to measure user interaction with your site through key metrics like bounce rate, average session duration, and pages per session. Enhance user experience by analyzing engagement metrics and implementing strategies to keep visitors engaged.
Conversion Rate Optimization:
Understand the importance of conversion rates and how to track them using Google Analytics. Set up Goals, analyze conversion funnels, segment your audience, and employ A/B testing to optimize your website for higher conversions. Utilize ecommerce tracking and multi-channel funnels for a detailed view of your sales performance and marketing channel contributions.
Custom Reports and Dashboards:
Create custom reports and dashboards to visualize and interpret data relevant to your business goals. Use advanced filters, segments, and visualization options to gain deeper insights. Incorporate custom dimensions and metrics for tailored data analysis. Integrate external data sources to enrich your analytics and make well-informed decisions.
This guide is designed to help you harness the power of Google Analytics for making data-driven decisions that enhance website performance and achieve your digital marketing objectives. Whether you are looking to improve SEO, refine your social media strategy, or boost conversion rates, understanding and utilizing Google Analytics is essential for your success.
hbaseconasia2019 HBase Table Monitoring and Troubleshooting System on Cloud
1.
2.
3.
4.
5. 2
1 2 2
2
Cloud Management Service
1. Manage all clusters
2. Provide technical services
User Space
1. Isolation between each user cluster
2. Separate host
3. Separate network
4. Focus on slef business